Search Results (5,797)

A three-dimensional discrete element method (DEM) framework was developed and applied to investigate the time-domain seismic response of a soil–pier system embedded in stratified dry sand. The numerical model was validated against analytical solutions to determine the ultimate vertical load capacity and internal forces when subjected to a lateral load at the pier head. Simulations were conducted to explore the influence of different excitation frequencies and amplitudes on soil–foundation interaction. Dynamic p–y curves were extracted at multiple elevations along the shaft to examine variations in lateral stiffness with depth. The results show that seismic loading significantly increases lateral displacement, and the residual response is strongly governed by the input motion amplitude. Peak lateral deformation and internal forces were observed when the excitation frequency coincided with the pier’s natural frequency. Both cyclic shear strain and ground settlement reached their maximum near the natural frequency of the soil deposit, and increased substantially with shaking amplitude. Full article

We present a fully controlled thermodynamic study of the two-dimensional dipolar Q-state clock model on small square lattices with free boundaries, combining exhaustive state enumeration with noise-free evaluation of canonical observables. We resolve the complete energy spectra and degeneracies $\{E_n,c_n\}$ for the Ising case ($Q=2$) on lattices of size $L=3,4,5$, and for clock symmetries $Q=4,6,8$ on a $3\times 3$ lattice, tracking how the competition between exchange and long-range dipolar interactions reorganizes the low-energy manifold as the ratio $\alpha =D/J$ is varied. Beyond a finite-size characterization, we identify several qualitatively new thermodynamic signatures induced solely by dipolar anisotropy. First, we demonstrate that ground-state level crossings generated by long-range interactions appear as exact zeros of the specific heat in the limit $C(T\to 0,\alpha )$, establishing an unambiguous correspondence between microscopic spectral rearrangements and macroscopic caloric response. Second, we show that the shape of the associated Schottky-like anomalies encodes detailed information about the degeneracy structure of the competing low-energy states: odd lattices ($L=3,5$) display strongly asymmetric peaks due to unbalanced multiplicities, whereas the even lattice ($L=4$) exhibits three critical values of $\alpha$ accompanied by nearly symmetric anomalies, reflecting paired degeneracies and revealing lattice parity as a key organizing principle. Third, we uncover a symmetry-driven crossover with increasing Q: while the $Q=2$ and $Q=4$ models retain sharp dipolar-induced critical points and pronounced low-temperature structure, for $Q\ge 6$, the energy landscape becomes sufficiently smooth to suppress ground-state crossings altogether, yielding purely thermal specific-heat maxima. Altogether, our results provide a unified, size- and symmetry-resolved picture of how long-range anisotropy, lattice parity, and discrete rotational symmetry shape the thermodynamics of mesoscopic magnetic systems. We show that dipolar interactions alone are sufficient to generate nontrivial critical-like caloric behavior in clusters as small as $3\times 3$, establishing exact finite-size benchmarks directly relevant for van der Waals nanomagnets, artificial spin-ice arrays, and dipolar-coupled nanomagnetic structures. Full article

The Collatz map is investigated from a nonlinear-dynamics perspective with emphasis on the structure of its iterative orbits. By embedding integers within Sharkovsky’s ordering, odd initial values are shown to be sufficient for a complete characterization of dynamics. A “direction-phase” decomposition is introduced to separate iterative orbits into upward and downward phases, yielding a family of recursive functions parameterized by the number of upward phases. This formulation reveals a logarithmic scaling relation between the total iteration count and the initial value, confirming finite-time convergence to the period-three orbit. The Collatz dynamics is further shown to be dynamically equivalent to a binary shift map, whose ergodicity implies inevitable evolution toward attractors, thereby reinforcing convergence. Numerical analysis indicates that attraction basins follow a power-law distribution and display pronounced self-similarity. Moreover, odd integers grouped by upward-phase counts are found to follow Gamma statistics. Beyond its research implications, the framework provides a concise pedagogical case study illustrating how nonlinear dynamics, symbolic dynamics, and statistical characterization can be integrated to analyze a classical discrete problem. Full article

Maritime transport in semi-enclosed seas is increasingly exposed to short-term weather variability, a challenge expected to intensify under climate change and to affect the economic sustainability of shipping operations. This study proposes an integrated probabilistic framework to assess the impact of weather-induced uncertainty on operational costs, using a ferry service in the Baltic Sea as a case study. The approach combines a semi-Markov process, representing transitions between discrete weather hazard states derived from ERA5 reanalysis data (2010–2025), with a state-dependent cost model of key technical subsystems across the vessel’s operational cycle. The results show a strongly disproportionate cost structure, with most expenditures concentrated in open-sea navigation states. Although severe weather conditions occur infrequently, they generate a nonlinear amplification of operational costs, significantly reducing cost predictability and system resilience. The findings indicate that enhancing sustainability in maritime transport requires targeted, state-specific adaptation measures, such as weather-aware routing and condition-based maintenance. The proposed framework supports climate-adaptive decision-making and contributes to sustainability-oriented planning in maritime transport through improved operational robustness and cost resilience under weather uncertainty. Full article

Eye movement classification, particularly the identification of fixations and saccades, plays a vital role in advancing our understanding of neurological functions and cognitive processing. Conventional modalities of data, such as RGB webcams, often face limitations such as motion blur, latency and susceptibility to noise. Neuromorphic Vision Sensors, also known as event cameras (ECs), capture pixel-level changes asynchronously and at a high temporal resolution, making them well suited for detecting the swift transitions inherent to eye movements. However, the resulting data are sparse, which makes them less well suited for use with conventional algorithms. Spiking Neural Networks (SNNs) are gaining attention due to their discrete spatio-temporal spike mechanism ideally suited for sparse data. These networks offer a biologically inspired computational paradigm capable of modeling the temporal dynamics captured by event cameras. This study validates the use of Spiking Neural Networks (SNNs) with event cameras for efficient eye movement classification. We manually annotated the EV-Eye dataset, the largest publicly available event-based eye-tracking benchmark, into sequences of saccades and fixations, and we propose a convolutional SNN architecture operating directly on spike streams. Our model achieves an accuracy of 94% and a precision of 0.92 across annotated data from 10 users. As the first work to apply SNNs to eye movement classification using event data, we benchmark our approach against spiking baselines such as SpikingVGG and SpikingDenseNet, and additionally provide a detailed computational complexity comparison between SNN and ANN counterparts. Our results highlight the efficiency and robustness of SNNs for event-based vision tasks, with over one order of magnitude improvement in computational efficiency, with implications for fast and low-power neurocognitive diagnostic systems. Full article

This study explores how teachers’ work sustainability is shaped through everyday governance practices within private higher education institutions in China. Using a constructivist grounded theory approach, the analysis draws on long-term fieldwork and in-depth interviews with teachers, administrators, leaders, and students from two private colleges. The findings suggest that teachers’ difficulties do not stem from isolated adverse incidents, but rather from an ongoing organizational process embedded in routine management practices. Evaluation-centered promotion systems, relationship-based governance, and data-driven oversight interact to restructure how teaching work is organized, recognized, and assessed. Professional contributions are frequently treated as negotiable outcomes subject to managerial discretion, while informal alignment practices and selective monitoring gradually narrow teachers’ space for professional judgment and initiative. Despite accumulating dissatisfaction, most teachers remain in their positions. Occupational identity, social expectations, and constrained labor mobility limit realistic exit options, transforming short-term accommodation into prolonged endurance. In this context, teacher retention reflects not organizational stability, but the persistence of governance conditions that challenge the long-term sustainability of teachers’ work. By examining how routine management practices gradually reshape teachers’ work, this study highlights an overlooked dimension of sustainability in higher education: the long-term viability of teachers’ professional lives within existing governance arrangements. Unlike studies that conceptualize teachers’ difficulties through the lens of workplace bullying or interpersonal conflict, this study focuses on how ordinary governance practices shape long-term work sustainability without overt confrontation. Full article

Airport digital transformation is commonly approached through technological integration and data-driven optimization, yet such perspectives provide limited insight into system-level reasoning and governance. This paper introduces the cognitive airport paradigm (CAP) as a mathematically grounded framework that models the airport as a domain-specific cognitive digital twin within a complex aviation ecosystem. Methodologically, the study follows a conceptual–analytical and design-science research approach, combining system analysis, conceptual modeling, ontology engineering, and formal mathematical representation of cognitive transitions and governance constraints. CAP represents airport cognition as an explicit state space characterized by cognitive maturity, governance integrity, and semantic stability. Analytical reasoning, adaptive learning, and orchestration mechanisms are formalized through instrument dominance profiles and cognitive performance functionals, enabling analytical comparison of airport configurations and identification of cognitive regimes. The results include (i) a formalization of airports as cognitive digital twins with measurable cognitive and governance properties; (ii) quantitative indices such as the cognitive readiness index, governance integrity index, and ethical alignment coefficient supporting structured evaluation of airport cognitive maturity; and (iii) illustrative expert-based parameterizations and a geometric interpretation in a cognitive simplex demonstrating that governance-oriented orchestration stabilizes airport cognition under increasing system complexity. Airport development is interpreted as continuous cognitive evolution rather than discrete stages of digitalization. The paper further proposes a cognitive roadmap for guiding airport evolution through structured cognitive rebalancing. The framework contributes to the theoretical foundations of cognitive digital twins and is transferable to other safety-critical and institutionally governed socio-technical systems. Full article

Swarm Unmanned Surface Vehicles (SUSV) must sustain real-time coordination as fleet scale and formation change. We propose hierarchical and modular architecture that decouples mission-planning algorithms from interface evolution, composed of an Interface Adapter System (IAS), a Mission Execution System (MES), and an Interoperation Integration Verification System (IIVS). The IAS standardizes and integrates data from diverse sensors and subsystems through modular adapters, facilitating flexible subsystem integration. The MES employs a discrete-event system specification (DEVS)-based modeling approach, providing independent and efficient mission execution capability without necessitating interface modifications. The IIVS utilizes LabVIEW-based analytical methods to verify and validate subsystem interoperability continuously, enabling rapid and reliable scaling. The architecture was implemented in an operational SUSV program and evaluated through simulation experiments and sea trials. During scale-up from 10 to 20 USVs, mission-cycle deadlines (≤250 ms) were met in 99% of cases, while integration lead time for scale-up decreased by about 80%. Message-level tests confirmed robust interoperation under increased load, and algorithm-level tests showed stable plan re-computation under dynamic tasking. These results indicate improved interoperability, scalability, and reliability, offering a practical blueprint for mission planning in maritime swarms. Full article

This study proposes an efficient numerical scheme for simulating heat transfer governed by the diffusion equation with moving singular sources. The work addresses two-dimensional problems with line sources and three-dimensional problems with plane sources, which are prevalent in irreversible thermodynamic processes. Developed within a finite difference framework, the method employs a partitioned discretization strategy to accurately resolve the solution singularity near the heat source—a region critical for precise local entropy production analysis. In the immediate vicinity of the source, we analytically derive and incorporate the solution’s “jump” conditions to construct specialized finite difference approximations. Away from the source, standard second-order-accurate schemes are applied. This hybrid approach yields a globally second-order convergent spatial discretization. The resulting sparse system is efficient for large-scale simulation of dissipative systems. The accuracy and efficacy of the proposed method are demonstrated through numerical examples, providing a reliable tool for the detailed study of energy distribution in non-equilibrium thermal processes. Full article

A multiscale approach to calculating the effective elastic properties of a composite material with a fibrous filler and a polymer matrix is presented. Modeling at various scales is performed using a unified algorithmic scheme, solving Cauchy problems for systems of ordinary differential equations. At the atomic level, molecular dynamics modeling is used to calculate the elastic constant tensor of the polymer material. At the mesoscale, the author’s discrete element method is applied to calculate the effective elastic properties of the composite material. The method was tested on problems with regular fiber placement with symmetry, which have an analytical solution. The capabilities of the proposed approach are demonstrated using defect composite homogenization problems, where symmetry is broken. Fiber cracking and matrix–fiber debonding are considered. Full article

In this scientific article, a quantitative assessment is carried out of the influence of the ERTMS modernisation factor on the practical efficiency of operation and resilience of the Belgian railway lines 50A/51A with the application of methodological triangulation in the MATLAB R2025a Update 1 (25.1.0.2973910) software environment (discrete-event modelling, Petri nets, Markov reliability modelling, and correlation analysis). The modelling reveals that the scenario with an expanded level of automation increases the capacity from 18.3 to 26.0 trains over 2 h (+42.1%) and reduces the average waiting time from 1.53 min (baseline level) to 0.21 min—virtually the theoretical lower bound of zero under favourable conditions. The results of the block-occupancy analysis by means of Petri nets show that a more dynamic distribution of blocks provides higher capacity, and Markov chains reflect the reduction of the impact of control centre unavailability in developing communications and virtualisations. Spearman correlation analysis additionally shows coordinated improvement of the metrics of safety, digital protection, resilience, and performance. Relying on the modelling results, a phased roadmap is proposed, combining technical improvements (development of communication systems, readiness for automation, comparable management of rolling stock movement) with compliance with regulatory requirements and the goals of sustainable development, related to SDGs 9, 11, and 13. Full article

The aviation industry aims to reduce environmental impact by adopting alternative propulsion systems, including hydrogen-based, hybrid-electric, and all-electric architectures, requiring a new Thermal Management System (TMS). In addition, new design methods are needed for the TMS, at the system and component levels, to handle various fluids and varying fluid properties. Within the TMS, heat exchangers are critical components that may require significant space and must be considered early in the design process. This paper presents a parametric sizing methodology for heat exchangers suitable for early design phases within a Multidisciplinary Design Analysis and Optimization (MDAO) framework, specifically for cryogenic heat transfer. The method combines physical equations with validated empirical relationships, using iterative solver algorithms for sizing. To address multi-variable design challenges, the methodology integrates discretization schemes for fluid properties, temperature, and energy calculations, and constraint-based optimization with a weighted-sum approach for solution selection. The methodology is validated with a commercial heat exchanger, and cross-validated with a cryogenic Heat Exchanger (HX). A case study for an all-electric hydrogen fuel cell aircraft architecture with a 7.6 MW propulsion system is presented to demonstrate the effectiveness of the methodology. The presented heat exchanger performance can be predicted across multiple conditions quickly enough to enable large design space exploration. Overall, the presented model is a crucial element for the design of a TMS for future aircraft with hydrogen-based propulsion systems. Full article

Underground coal gasification (UCG) is an emerging energy technology that involves the in situ conversion of coal into syngas through controlled combustion within a subsurface excavation. The geomechanical processes associated with UCG can lead to significant overburden caving and surface subsidence, posing risks to surface infrastructure and groundwater systems. To accurately predict the size of overburden caving zones and associated surface subsidence, a prediction model was developed based on simulation results using discrete element method (DEM) numerical models. The main purpose of developing such a model is to establish a systematic and computationally efficient method for the rapid prediction of the height of overburden caving and its associated surface subsidence induced by underground excavation. The model is broadly applicable to different types of underground excavations, and UCG is used in this study as a representative application scenario to demonstrate the relevance and performance of the model. Sensitivity analysis indicates that excavation span, tensile strength, and burial depth are the primary controls on the height of the caving zone within the ranges of parameters investigated. Rock density is retained as a secondary background parameter to represent gravitational loading and its contribution to the in situ stress level. The derived model was validated using published numerical, experimental, and field measurement data, showing good agreement within practical ranges. To further demonstrate the application of the model developed, the predicted caving geometries were incorporated into finite element method (FEM) models to simulate surface subsidence under different geological conditions. The results highlight that the arch structure formed by overburden caving can help redistribute stresses and thereby reduce surface deformation. The proposed model provides a practical, parameter-driven tool to assist in underground excavation design, environmental risk evaluation, and ground stability management. Full article

This investigation provides a comprehensive analytical framework for the topological morphology and global convergence dynamics governing a specific family of sixth-order iterative schemes designed for nonlinear equations with multiple roots. By invoking a Möbius conjugacy transformation upon the specialized polynomial class $f\left(z\right)={\left((z-p)(z-q)\right)}^m$, we project the iterative sequence onto the Riemann sphere $\widehat{\mathbb{C}}$, effectively recasting the algorithm as a discrete complex dynamic system. The core of this study lies in the bifurcation analysis of the associated parameter space. We meticulously chart the stability manifolds, tracing the evolution of critical orbits to distinguish between regions of predictable convergence and those characterized by chaotic instability. By examining the iterative methods generated by these rational endomorphisms, the research unveils the intricate fractal boundaries that delineate the basin of attraction, offering a profound insight into the structural robustness of higher-order methods. In the dynamical plane, the geometry of the basins of attraction is scrutinized to evaluate the robustness of the numerical flow and its sensitivity to the configuration of weight functions. By analyzing the fractal complexity of the boundaries within these basins, we provide a detailed characterization of the iterative morphology and its global reliability. The analytical findings are supported by high-resolution graphical representations and comparative numerical data, illustrating the superior performance and structural integrity of the proposed methods in solving nonlinear problems. Full article

This study introduces a mathematical framework for analyzing unsteady gas filtration in porous media with pressure-dependent porosity variations. The physical process is formulated as a nonlinear parabolic boundary value problem that captures the coupled interaction between pressure evolution and porosity changes during gas production. To solve the equation, a numerical strategy is developed by integrating the Alternating Direction Implicit (ADI) scheme with quasi-linearization iterations, employing finite difference discretization on a two-dimensional spatial grid. Extensive computational experiments are performed to investigate the influence of key reservoir parameters—including porosity coefficient, permeability, gas viscosity, and well production rate—on the spatiotemporal behavior of pressure and porosity during long-term extraction. The results indicate significant porosity variations near the wellbore driven by local pressure depletion, reflecting strong sensitivity of the system to formation properties. The validated numerical model provides valuable quantitative insights for optimizing reservoir management and improving production forecasting in gas field development. Overall, the proposed methodology serves as a practical tool for oil and gas engineers to assess long-term reservoir performance under diverse operational conditions and to design efficient extraction strategies that incorporate pressure-dependent formation property changes. Full article