Search Results (376)

Land-use and land-cover change plays a critical role in shaping urban expansion patterns and modifying near-surface soil conditions, hydrological behaviour, and geomorphological stability in rapidly developing regions. This study presents a comparative modelling framework to analyze long-term land-use change and its implications for regional-scale geotechnical risk screening by integrating historical land-use classification, Markov transition analysis, and machine learning–based spatial simulation. Landsat imagery from 1985 and 2024 was classified using a Support Vector Machine approach, and future land-use projections for 2063 were generated using both the TerrSet Land Change Modeler (LCM) and the GeoFLUS model under identical transition demands. Spatial driving variables included topographic, hydrological, and accessibility-related factors that influence soil behaviour and urban suitability. The results reveal sustained urban expansion primarily driven by the systematic conversion of agricultural land into built-up surfaces, while forested areas and water bodies exhibit high class persistence, as indicated by dominant diagonal values in the Markov transition matrix. Although both models reproduce consistent directional trends, they generate distinct spatial allocation patterns, with LCM producing compact and centralized growth and GeoFLUS generating more spatially dispersed expansion. These differences lead to contrasting implications for potential settlement, flooding, and slope instability zones. By treating future land-use maps as alternative geotechnical screening scenarios rather than fixed predictions, this study demonstrates how model uncertainty can be incorporated into hazard-sensitive planning. The proposed framework supports preliminary geotechnical zoning and infrastructure planning by identifying robust development corridors and spatial uncertainty zones where detailed site investigations may be prioritized. The methodology is transferable to other rapidly urbanizing regions facing complex soil and geomorphological constraints. Full article

We propose a novel theoretical framework for describing photon–electron interactions and electron collision processes in a unified manner within quantum electrodynamics. Specifically, we develop a method to construct the Dirac operator in curved spacetime using only matrix representations rooted in the basis structure of four-dimensional gamma matrix algebra, without introducing vierbeins (tetrads) or independent spin connections. We realize 16 gamma matrices with two indices as $256\times 256$ matrices and embed the spacetime metric directly into the matrix elements. This reduces geometric operations such as covariantization, connection-like operations, and basis transformations to matrix products and trace calculations, yielding a unified and transparent computational scheme. The spacetime dimension remains as four, and the number “16” represents the number of basis elements of four-dimensional gamma matrix algebra ($2^4=16$). Based on the extended QED Lagrangian, vertex rules, propagators, spin sums, and traces can be handled uniformly, making it suitable for automation. As validation of this method, we analyzed four fundamental scattering processes in atomic and particle physics: (i) Compton scattering (photon–electron scattering), (ii) muon pair production ($e^{+}{e}^{-}\to {\mu }^{+}{\mu }^{-}$), (iii) Møller scattering (electron–electron collision), and (iv) Bhabha scattering (electron–positron collision). In the flat spacetime limit, we confirmed the exact reproduction of standard quantum electrodynamics (QED) results including the Klein–Nishina formula. Furthermore, trial calculations using a metric with off-diagonal components show systematic deviations from flat results near scattering angle $\theta \approx {90}^{\circ }$, suggesting that metric-induced angular dependence could in principle serve as an observable signature. The matrix representation developed in this work enables unified pipeline execution of theoretical calculations for photon interactions and charged particle collision processes, with expected applications to precision calculations in atomic and particle physics. Full article

The position operator $\widehat{r}$ appears as $i{\partial }_p$ in wave mechanics, while its matrix form (e.g., under a Bloch basis) is well known diverging in diagonals, causing difficulties in basis transformation, observable yielding, etc. We aim to find a convergent r-matrix (CRM) to improve the existing divergent r-matrix (DRM), and investigate its influence at both the conceptual and the application levels. A key modification is increasing the familiar substitution of $\widehat{r}$ by $i{\partial }_p$ to $i{\sum }_j{\partial }_{k_j}$, namely the N-th Weyl algebra. Resolving the divergence makes r-matrix rigorously defined, and we are able to show r-matrix is distinct from a spin matrix in terms of its defining principles, transformation behavior, and the observable it yields. Conceptually, the CRM fills the logical gap between the r-matrix and the Berry connection (this unremarked vagueness has caused the diagonal divergence). In application, we focus on transport, and discover that the Hermitian matrix is not identical with the associative Hermitian operator, i.e., $r_{m,n}=r_{n,m}^{*}\nLeftrightarrow \widehat{r}={\widehat{r}}^{†}$, which subtly affects the celebrated Berry curvature formula for adiabatic current. We also discuss how such a non-representation CRM can contribute to building a unified transport theory. Full article

Eliminating poor scaling of variables of minimized functions is a pressing issue in solving high-dimensional minimization problems where it is impossible to use methods that change the metric of the space with full-scale metric matrices. In this paper, we propose an iterative method for scaling variables using a diagonal metric matrix and apply it to the gradient minimization method and the conjugate gradient method. In conjugate gradient methods, for quadratic functions, the descent directions are orthogonal to the previous gradient differences. In the proposed method, the transformation of diagonal metric matrices is based on the noted property. For the gradient method with a diagonal metric matrix, an estimate for the convergence rate on strongly convex functions with a Lipschitz gradient was obtained. A computational experiment was conducted, and the presented methods were compared with the Hestenes–Stiefel conjugate gradient method. On the given set of test functions, the gradient method with scaling is comparable in convergence rate to the Hestenes–Stiefel conjugate gradient method, while the conjugate gradient method with scaling matrices significantly outperforms the Hestenes–Stiefel conjugate gradient method. The obtained results confirm the acceleration properties of scaling methods in the case of poor scaling of the variables of the function being minimized. This allows us to conclude that the studied methods can be used alongside conjugate gradient methods to solve smooth, high-dimensional optimization problems with a high degree of conditionality. Full article

The electronic band structure of two-dimensional lithium is calculated using the Dirac equation. Lithium is modeled as a two-dimensional square lattice in which the two strongly bound inner electrons and the fixed nucleus are treated as a positively charged ion (+e), while the outer electron is assumed to be uniformly distributed within the cell. The electronic potential is obtained by considering Coulomb-type interactions between the charges inside the unit cell and those in the surrounding cells. A numerical method that divides the unit cell into small pieces is employed to calculate the potential and then the Fourier coefficients are obtained. The Bloch method is used to determine the energy bands, leading to an eigenvalue matrix equation (in momentum space) of infinite dimension, which is truncated and solved using standard matrix diagonalization techniques. Convergence is analyzed with respect to the key parameters influencing the calculation: the lattice period, the dimension of the eigenvalue matrix, the unit-cell partition used to compute the potential’s Fourier coefficients, and the number of neighboring cells that contribute to the electronic interaction. Full article

Glass fiber-reinforced polymer (GFRP) composites are widely used in structural applications due to their high specific strength and durability; however, their mechanical performance strongly depends on fiber architecture and environmental exposure. This study evaluates the mechanical behavior and moisture-induced degradation of GFRP laminates through tensile tests, impact tests, dynamic mechanical analysis (DMA), and thermomechanical analysis (TMA) performed on a bi-directional glass–epoxy GFRP laminate ([0°/90°]). Tensile tests revealed a maximum longitudinal strength of 369 MPa in dry specimens, while water immersion for up to 21 days led to a significant reduction in tensile strength, from 207 MPa to 63 MPa, in diagonally cut specimens. Impact tests conducted at 12 J showed larger displacements in specimens cut along directions not aligned with the fibers, indicating matrix-dominated behavior. Dynamic mechanical analysis demonstrated strong dependence of stiffness on fiber orientation, with storage modulus values decreasing by approximately 45% in 45° specimens compared with the principal directions, while the glass transition temperature remained within 59–62 °C. Thermomechanical analysis confirmed an increase in the coefficient of thermal expansion after aging, from 205.6 to 291.65 µm/(m·°C) below Tg. These results provide insights into the structure–property–environment relationships governing the durability of GFRP composites and support the optimization of their design for long-term polymer-based applications. Full article

Let R be an axis-aligned rectangle. We define a floorplan as a partition of R into rectangular regions (rooms) such that each vertex is shared by at most three rooms. Following the approach of Nakano et al.,we also assume the presence of a set of points that impose constraints on the walls passing through them, allowing only horizontal or vertical segments. These constraints can be encoded by a permutation matrix whose entries are labeled H and V, which we refer to as a pattern matrix. In this work, we characterize the well-known classes of guillotine, diagonal, and diagonal–guillotine floorplans in terms of the presence of specific families of pattern matrices. In this way, we translate a purely geometric characterization into a combinatorial one. Full article

This paper proposes a dual path mixed coupling wireless power transfer (DPMPT) coupler as a four-port structure for near-field wireless power transfer in drone and unmanned aerial vehicles. The DPMPT coupler integrates orthogonal double-D coils and 8-plates to realize mixed inductive–capacitive coupling at 6.78 MHz without additional lumped matching networks. A four-port equivalent model is developed by classifying the mutual networks into three coupling types and representing them with a transmission-matrix formulation fitted to three-dimensional full-wave simulations. The model is used to identify the main coupling paths and to evaluate the effect of rotation and lateral/diagonal misalignment on power-transfer characteristics. Simulation results at a transfer distance of 70 mm show a maximum transmission coefficient of about 0.82 at 6.78 MHz and high robustness against rotation. When switch-based port selection is applied on the transmit side, blind spots associated with pose variations that cause an abrupt drop in transmission characteristics are significantly reduced, demonstrating that the DPMPT coupler with switch control provides an effective structural basis for enhancing alignment tolerance in mixed coupling wireless power transfer systems. Full article

Deep neural networks are vulnerable and susceptible to adversarial examples, which can induce erroneous predictions by injecting imperceptible perturbations. Transferability is a crucial property of adversarial examples, enabling effective attacks under black-box settings. Adversarial examples at flat maxima-those around which the loss peaks and grows slowly-have been demonstrated to exhibit higher transferability. Existing methods to achieve flat maxima rely on the gradient of the worst-case loss within the small neighborhood around the adversarial point. However, the neighborhood structure is typically defined as a Euclidean space, which neglects the input space’s information geometry, leading to suboptimal results. In this work, we build upon the idea of flat maxima but extend the neighborhood structure from Euclidean space to the manifold measured by the Fisher metric, which takes into account the information geometry of the data space. In the non-Euclidean case, we search for the worst-case point in the direction of the natural gradient with respect to adversarial examples. The natural gradient adjusts the original gradient using the Fisher information matrix, giving the steepest direction in the manifold. Furthermore, to reduce the computational cost of calculating the Fisher information matrix, we introduce a diagonal approximation of the matrix and propose an empirical Fisher method under the model ensemble setting. Experimental results demonstrate that our proposed manifold extensions significantly enhance attack success rates against both normally and adversarially trained models. In particular, compared to methods relying on the Euclidean metric, our approach demonstrates more efficient performance. Full article

It is well known that the position of the Jacobian matrix spectrum in the left-half complex plane provides the local asymptotic stability of a nonlinear dynamical system, but it is also well known that for large matrices, computing its eigenvalues just to see their position is computationally prohibitive. Instead, it is recommended to check if a given matrix belongs to the H-matrix class and has negative diagonal entries. Since confirming the H-matrix property is computationally costly, the preference is to work with its subclasses, which are defined by simpler conditions. In this paper, we develop and investigate a new subclass of H-matrices via the Frobenius matrix norm, which generalizes the recently introduced classes. We support its significance with real-life examples and clarify its relationship to some well-known block H-matrices based on the Euclidean matrix norm. The main novelty in this paper is that when a fast and inexpensive answer about the stability of a dynamical system is required, and the system matrix has a natural block structure, we develop a simple tool to check whether this structure, along with the additional condition of negative diagonal elements, ensures stability. This is especially important when the matrix does not belong to any previously known H-matrix subclasses. Full article

This paper proposes a compressive-sensing (CS) acquisition algorithm for low-power, high-dynamic GNSS receivers based on low-dimensional time-domain measurements, a non-iterative compressive-domain direct-projection peak-search pipeline, and a coherence-optimized sensing-matrix design. Unlike most existing GNSS-CS acquisition approaches that rely on explicit sparse-recovery formulations (e.g., OMP/BP/LS-type iterative reconstruction) to identify the delay–Doppler support—often incurring substantial computational burden and acquisition latency—the proposed method performs peak detection directly in the compressive measurement domain and is supported by unified Gram-matrix optimization and perturbation/detection analyses. Specifically, the measurement Gram matrix is optimized on the symmetric positive-definite (SPD) manifold to obtain a diagonally dominant and well-conditioned structure with reduced inter-column correlation, thereby bounding reconstruction-induced perturbations and preserving the main correlation peak. Simulation results show that the proposed scheme retains the low online complexity characteristic of direct-projection baselines while achieving a 2–3 dB acquisition sensitivity gain, and it requires substantially fewer operations than iterative OMP-based CS acquisition schemes whose cost scales approximately linearly with the sparsity level K. The proposed framework enables robust, low-latency acquisition suitable for resource-constrained GNSS receivers in high-dynamic environments. Full article

This paper introduces an advanced framework for modeling and scheduling construction processes using causal inference techniques, with particular emphasis on capturing complex technological and organizational interdependencies. By integrating causal calculus and counterfactual reasoning, the study demonstrates how construction schedules can be analyzed and optimized not only through temporal relationships but also through explicit cause–effect structures. A matrix-based scheduling methodology is presented, incorporating diagonal and reverse-diagonal time couplings consistent with the Time Coupling Method (TCM). The computational procedure is detailed, including the determination of earliest and latest event times, identification of the critical path, and computation of activity floats. Based on an in-depth examination of technological and organizational constraints, eight theorems are formulated and proven, establishing the fundamental properties of a scheduling approach that embeds causal mechanisms. The findings indicate that the integration of causal inference into construction planning enables more accurate identification of factors influencing project duration, enhances synchronization of dependent activities, and minimizes conflicts and idle times. This causally informed framework strengthens decision-making by allowing practitioners to predict the consequences of modifications in project execution strategies. The developed models constitute a robust foundation for future research on leveraging causal inference algorithms and artificial intelligence to advance construction process management. Full article

We study basis-independent structures in the Type-I seesaw mechanism for light Majorana neutrinos, assuming the canonical scenario with three heavy right-handed (sterile) neutrinos. Let $m_{\nu }$ denote the $3\times 3$ mass matrix of light neutrinos, obtained at tree level from heavy Majorana singlets with a diagonal mass matrix $D_N=\mathrm{diag}(M_1,M_2,M_3)$ and a Dirac matrix $m_D$. We show that all right actions F on the seesaw matrix that leave $m_{\nu }$ unchanged form the group $G=D_N^{1/2}O(3,\mathbb{C})D_N^{-1/2}$. While oscillation data determine the PMNS matrix $U_{\mathrm{PMNS}}$ and the mass-squared splittings, they do not fix the F-class within G. We classify basis-invariant quantities into those that are class-blind (e.g., $det \eta$) and class-sensitive (e.g., $\mathrm{Tr}\eta$, $\mathrm{Tr}{\eta }^2$, an alignment measure, and CP-odd traces relevant to leptogenesis), where $\eta$ denotes the non-unitarity matrix of the light sector. We provide explicit formulas and both high-scale and GeV-scale benchmark examples that illustrate these invariant fingerprints and their scaling with $D_N$. This converts the degeneracy at fixed $m_{\nu }$ into measurable, basis-invariant fingerprints. Full article

As the third generation of neural networks, Spiking Neural Networks (SNNs) simulate the event-driven processing mode of the brain, offering superior energy efficiency and biological interpretability compared to traditional deep learning. Combining the architectural strengths of Transformers with SNNs has recently demonstrated high accuracy and significant potential. SNNs process binary spikes and rich temporal information, resulting in lower computational complexity and making them particularly suitable for neuromorphic datasets. However, neuromorphic data typically involve dynamic edges and high-frequency pixel intensity changes. Capturing this frequency information is challenging for traditional spatial methods but is critical for event-driven vision. To address this, we investigate the integration of the Fast Fourier Transform (FFT) into SNNs and propose the Adaptive Spiking Fourier Neural Operator Transformer (ASFNOformer). This architecture adapts the Adaptive Fourier Neural Operator (AFNO)—originally validated in Artificial Neural Networks (ANNs)—specifically for the spiking domain. Unlike standard AFNOs, our module applies FFT across both spatial (H, W) and temporal (T) dimensions, followed by a Multi-Layer Perceptron structure (MLP) mechanism with a block-diagonal weight matrix. This design effectively captures both spatial features and temporal dynamics inherent in event streams. Furthermore, we incorporate Leaky Integrate-and-Fire (LIF) neurons optimized with Learnable Weight Parameters (LWP-LIF) to enhance temporal feature extraction and adaptivity. Experimental results on standard benchmarks indicate that our method reduces the parameter count by approximately 25%. In terms of recognition accuracy, ASFNOformer is comparable to mainstream models on static datasets and demonstrates superior performance on neuromorphic datasets by efficiently capturing frequency features. Notably, ablation studies confirm the model’s generalizability, and when using QKformer as a baseline, our method achieves state-of-the-art (SOTA) performance on the CIFAR10-DVS dataset. This work advances frequency-domain analysis in SNNs, paving the way for efficient deployment on neuromorphic hardware. Full article

In this paper, we derive a semi-discrete scheme using the central difference method, which perfectly preserves the conservation of mass and energy for the Hirota equation. By applying the Crank–Nicolson method for temporal discretization, we develop the fully discrete scheme that conserves mass and energy. It is shown that the accuracy of the fully discrete scheme is of the second order in space and time. Because the Crank–Nicolson discretization leads to a nonlinear algebraic system, an efficient iterative solver is proposed that linearizes and solves the resulting five-diagonal matrix at each iteration while treating high-order contributions iteratively to reduce computational cost. Numerical experiments are presented to demonstrate the accuracy and verify the conservation properties. Full article