Search Results (263)

Titanium dioxide (TiO₂) is a versatile material that exhibits a high refractive index, strong light-scattering capability, effective UV-absorption, wide band gap semiconductor behavior (3.0–3.2 eV), and excellent chemical stability. Owing to this unique combination of properties, TiO₂ is widely used in applications such as cosmetic and healthcare products, architectural and automotive coatings, and photocatalytic degradation of environmental pollutants. In photocatalytic applications, the crystal structure, phase composition and electronic properties of TiO₂ play a critical role in determining its performance. In the present study, TiO₂ nanotubes were synthesized by anodization of Ti° coatings deposited via a semi-industrial arc-PVD process. A post-anodization heat treatment was carried out at 430 °C for 1 h to promote the formation of the anatase phase within the TiO₂ nanotube structures. The structural characterization of the synthesized film was performed using X-ray diffraction (XRD) and Rietveld refinement. This methodology enabled the identification of the formed oxide phases, structure, and crystalline, confirming the formation of mixed oxides in the coating. To address the difficulty of refinement of these crystalline phases, the Le Bail method was applied. This refinement strategy allowed the identification of the crystalline phases that are present in the Ti_xO_y coating, including a hexagonal structure characteristic of α-Ti (space group P63/mmc, No. 194), the tetragonal anatase TiO₂ (space group I41/amd, No. 141) phase, and the trigonal Ti₂O₃ phase (space group R-3/c No. 167). Key crystallographic parameters such as lattice constants, bond lengths and angles, crystallite sizes, unit cell distortion and electron density were systematically evaluated for each phase. In addition, the Wyckoff positions and interatomic distances of the constitutive atoms were calculated, providing a comprehensive description of the TiO₂+Ti₂O₃/Ti° crystallographic system. The topographic and surface oxidation states were recorded by using profilometry and X-ray photoelectron spectroscopy, respectively. Full article

Traditional image feature matching methods often fail to achieve satisfactory performance on multimodal remote sensing images (MRSIs), mainly due to significant nonlinear radiometric distortion (NRD) and complex geometric deformation caused by different imaging mechanisms. The key to successful MRSI matching lies in preserving high-frequency edge structures that are robust to geometric deformation, while overcoming nonlinear intensity mappings induced by NRD. To address these challenges, this paper proposes a novel high-precision matching framework, termed structure-aware feature descriptor with multi-scale side window filtering (SA-SWF). The proposed framework consists of three stages: (1) an anisotropic morphological scale space is constructed based on multi-scale side window filtering to strictly preserve geometric edges, and feature points are extracted using a multi-scale adaptive structure tensor with sub-pixel refinement to ensure high localization precision; (2) a structure-aware feature descriptor is constructed by integrating gradient reversal invariance and entropy-weighted attention mechanisms, rendering the multi-modal description highly robust against contrast inversion and noise; and (3) a coarse-to-fine robust matching strategy is established to progressively refine correspondences from descriptor-space matching to strict sub-pixel geometric verification, thereby minimizing alignment errors. Experiments on 60 multimodal image pairs from six categories, including infrared-infrared, optical–optical, infrared–optical, depth–optical, map–optical, and SAR–optical datasets, demonstrate that SA-SWF consistently outperforms seven state-of-the-art competitors. Across all six dataset categories, SA-SWF achieves a 100% success rate, the highest average number of correct matches (356.8), and the lowest average root mean square error (1.57 pixels). These results confirm the superior robustness, stability, and geometric accuracy of SA-SWF under severe radiometric and geometric distortions. Full article

Located in northeastern Anatolia, Çıldır and Ardahan serve as a gateway to the Caucasus for political entities such as the state and mobile groups such as the tribe. Due to this geopolitical characteristic, the region has fallen under the dominion of numerous states and civilizations throughout history. With its fertile highlands, Lake Çıldır, and natural water resources like the Kura River, the area constitutes an attractive living space for hem settled agriculturalists and nomadic tribe groups subsisting on animal husbandry. These features have profoundly influenced the ethnic, demographic, socio-economic, and cultural fabric of the region. Following the establishment of Ottoman sovereignty in the 16th century, Çıldır and Ardahan assumed a vital role in the state’s Caucasian and Eastern policies. This research addresses the Turkmen tribe and other ethnic communities residing around the eyalet of Çıldır and the sanjak of Ardahan. It further examines the banditry activities carried out by these groups, the attitudes of central and local administrators toward such activities, migration and settlement patterns, and the economic and political pressures exerted by the Russian State upon these tribes. The political and economic pressures exerted by the Russian State on these tribes reflect a broader imperial strategy of frontier making, as discussed by Khodarkovsky in the context of Russia’s expansion into its southern borderlands. By positioning the region as a negotiated frontier, this study moves beyond a descriptive narrative to analyze how tribal mobility and settlement functioned as tools of sovereignty and resistance within the broader context of Ottoman state formation and trans-imperial rivalry. The methodology employed in this study is the Qualitative Research Method; accordingly, documents from the Presidential Ottoman Archives (BOA) were transcribed, and the relevant sections were interpreted and incorporated into the study. The archival findings are contextualized within recent historiographical debates concerning the shifting definition of the state versus nomadic agency during the transition from the 18th to the 19th century. While existing literature contains academic studies aiming to elucidate the archaeological, geographical, economic, and administrative structures of Çıldır and Ardahan, it has been determined that no academic research has been conducted to analyze the ethno-socio-demographic structure of the region specifically focusing on the 18th and 19th centuries in a historical sense. With this focus on the interplay between imperial frontiers and tribal identity, this study provides a critical analysis of how local dynamics shaped the grand strategies of the Ottoman and Russian Empires. Full article

Rapid, standardised discrimination of soil taxonomic units remains challenging when relying solely on conventional field descriptions and laboratory analyses, particularly at high sampling densities. This study evaluated whether proximal spectroscopy and hyperspectral imaging can support the classification of Brazilian Soil Classification System (SiBCS) suborders and pedogenetic horizons when surface and subsurface spectra are treated separately. Six intact soil monoliths (0.12 × 1.60 m) were collected in Paraná State, southern Brazil, representing one Organossolo (Ooy), three Latossolos (LVd, LVd1, and LVd2) and two Argissolos (PVAd and PVd). For each monolith, 800 spectra were acquired per sensor with a non-imaging VIS–NIR–SWIR spectroradiometer (350–2500 nm), and 800 spectra per sensor per monolith were extracted from the SWIR hyperspectral images (1200–2450 nm). Principal component analysis (PCA) was used to summarise spectral variability, and supervised classification was performed via k-nearest neighbours, random forest, decision tree and gradient boosting for suborders (10-fold cross-validation), and a neural network was used for within-profile horizon classification. PCA indicated that most of the spectral variance was captured by a dominant axis, with clearer separation among suborders in the SWIR space than in the full VIS–NIR–SWIR range. With respect to suborder classification, subsurface spectra outperformed surface spectra, and SWIR outperformed VIS–NIR–SWIR: the best accuracies were 0.96 for subsurface SWIR (gradient boosting; AUC = 0.99; MCC = 0.95) and 0.89 for surface SWIR (k-nearest neighbours; AUC = 0.98; MCC = 0.87). Within-profile horizon classification via VIS–NIR–SWIR achieved accuracies of 0.84–0.97 with the Neural Network, with most misclassifications occurring between adjacent horizons. Overall, subsurface SWIR information provided the most reliable basis for taxonomic discrimination, whereas horizon classification was feasible but reflected gradual spectral transitions along the profile. Full article

This research introduces a novel approach for seismic fragility assessment by employing a long short-term memory (LSTM) neural network to identify the most effective scalar and vector intensity measures (IMs). This approach enables the rapid and accurate plotting of vector fragility surfaces for shield tunnels embedded in layered soils and subjected to seismic actions. First, an extensive suite of two-dimensional, fully nonlinear soil–structure interaction analyses was executed to generate ground–motion–structure response pairs. These records were subsequently leveraged to train the LSTM network, which received free-field acceleration time histories and directly output critical engineering demand parameters along the tunnel lining. The developed framework significantly mitigates computational expenses while maintaining an acceptable level of fidelity relative to the reference finite element results. Consequently, it serves as an alternative to traditional time history evaluation techniques. Second, we conducted an IM screening process using the results of the LSTM predictions. On the basis of criteria such as relevance, efficiency, practicality, and professionalism, we benchmarked 17 scalar IM and 3 vector IM candidate schemes. The findings indicate that the peak ground velocity (PGV) serves as the most effective scalar IM, whereas the combination of peak ground acceleration (PGA) and PGV forms the optimal vector IM. Finally, probabilistic demand and capacity models are integrated within a fully analytical fragility formulation to derive both scalar and vector fragility estimates. Comparative evaluation reveals that vector IM based fragility surfaces markedly reduce epistemic uncertainty and furnish refined probabilistic descriptions of damage states (DSs) across the seismic demand space. Full article

Many complex systems, particularly glasses and disordered materials, exhibit energy landscapes with exponentially many metastable states. Such landscape structure strongly influences equilibrium behavior but is not explicitly represented in standard thermodynamic state spaces. We develop a constrained equilibrium framework in which configurational complexity, defined as the logarithmic density of metastable basins, is treated as an additional macroscopic coordinate. Starting from maximum entropy with simultaneous constraints on energy and complexity, we obtain a generalized Gibbs ensemble characterized by a conjugate bias parameter. Standard thermodynamic structure remains intact, with extended relations arising as constrained equilibrium identities. A mean-field glassy example with explicit complexity function demonstrates how complexity bias shifts the saddle-point structure of the partition function and modifies equilibrium response functions. The geometric formulation further provides a diagnostic of landscape reorganization within an enlarged state space. This framework offers a systematic equilibrium description of how energy-landscape structure influences thermodynamic behavior in systems with rugged configuration spaces. Full article

Integral quantization is a powerful framework for mapping classical phase-space functions—defined on a symplectic manifold—onto quantum operators in a Hilbert space. It encompasses several quantization methods, such as coherent-state quantization, and inherently incorporates operator symmetrization. The formalism relies on a choice of weight function, whose flexibility allows for a family of possible quantizations. In this work, we address the inverse problem: given a quantum operator, how can one determine a classical phase-space function whose integral quantization reproduces exactly that operator? We propose a systematic method, within the integral quantization framework, to construct such a classical function, which depends on the chosen weight. We demonstrate that quantizing the resulting function recovers the original operator, thereby establishing a consistent two-way mapping between classical and quantum descriptions. The method is applied to several physically relevant operators: the projector, a mixed-state density operator, the annihilation operator, and an entangled state. We also analyze how quantum entanglement manifests in the structure of the corresponding classical phase-space function. Full article

This paper presents a detailed investigation of the deterministic and stochastic dynamics of a noise-driven forced nonlinear oscillator in a periodically driven framework. An overlap-mapping approach is used to compare multiple traveling-wave solutions and verify the structural consistency among distinct solution families. The qualitative behavior of the system is further characterized through geometric and stability-based analysis, supported by two- and three-dimensional phase portraits, time-series responses, and reconstructed three-dimensional attractors to examine periodic and chaotic regimes under varying parameters and initial conditions. The sensitivity to parameter perturbations is quantified and the distribution of final states is analyzed to identify chaotic regions in the phase space. The high-dimensional chaotic nature of the dynamics is rigorously confirmed through Lyapunov exponent estimation, Poincaré sections, and return-map analysis, collectively demonstrating strong sensitivity to initial conditions and systematic transitions induced by parameter variations. These results provide a comprehensive dynamical description of the nonlinear oscillator and contribute to a deeper understanding of noise-influenced nonlinear driven systems. Full article

The core challenge in video-text retrieval lies in measuring the cross-modal gap between visual and linguistic representations. While mainstream methods leverage deep learning to project video and text features into a shared space for similarity matching, they often rely on superficial statistical correlations. This limits their ability to model underlying causal relationships or capture high-level semantics, resulting in poor interpretability. To address this, we analyze the biases existing in video-text retrieval tasks and introduce a Causal Visual–Semantic Enhancement (CVSE) method that integrates causal inference with deep learning. Our approach applies causal intervention to mitigate bias from contextual confounders and uses textual descriptions as a condition to guide frame aggregation, emphasizing semantically relevant frames while suppressing redundant ones. Experiments on MSR-VTT, MSVD, and LSMDC demonstrate that the proposed method outperforms state-of-the-art retrieval models, validating its superior performance. Full article

This Editorial introduces the Symmetry Special Issue “Theory and Applications of Special Functions II” and summarizes the nine contributions collected therein. The papers span the analytic continuation of multivariate hypergeometric functions; stability theory for differential equations via integral transforms; numerical schemes for multi-space fractional partial differential equations based on nonstandard finite differences and orthogonal polynomials; applications of the Lambert W function to viscoelastic creep modeling; algebraic constructions of new Hermite-type polynomial families via the monomiality principle; higher-level generalizations of poly-Cauchy numbers; Bell-polynomial expansions for Laplace transforms of higher-order nested functions; and two complementary studies on the physical implementation and algebraic description of Gaussian quantum states. Beyond the contributions of the Special Issue, we highlight methodological connections—continued fractions and complex analysis, transform techniques, special polynomials, and combinatorial sequences—and emphasize the unifying role of symmetry across mathematical structures and applications. Full article

This paper addresses a central conceptual challenge in Quantum-like Cognition and Decision-Making (QCDM) and the broader research program of Quantum-like Modeling (QLM): the interpretation of phases in quantum-like state superpositions. In QLM, system states are represented by normalized vectors in a complex Hilbert space, $|\psi ⟩={\sum }_k{X}_k|k⟩$, where the squared amplitudes $P_k={\left|X_k\right|}^2$ are outcome probabilities. However, the meaning of the phase factors $e^{i{\varphi }_k}$ in the coefficients $X_k=P_k{e}^{i{\varphi }_k}$ has remained elusive, often treating them as purely phenomenological parameters. This practice, while successful in describing cognitive interference effects (the “interference of the mind”), has drawn criticism for expanding the model’s parameter space without a clear physical or cognitive underpinning. Building on a recent framework that connects QCDM to neuronal network activity, we propose a concrete interpretation. We argue that the phases in quantum-like superpositions correspond directly to the phases of random oscillations generated by neuronal circuits in the brain. This interpretation not only provides a natural, non-phenomenological basis for phase parameters within QCDM but also helps to bridge the gap between quantum-like models and classical neurocognitive frameworks, offering a consistent physical analogy for the descriptive power of QLM. Full article

Kaprekar’s routine, i.e., sorting the digits of an integer in ascending and descending order and subtracting the two, defines a finite deterministic map on the state space of fixed-length digit strings. While its attractors (such as 495 for $D=3$ and 6174 for $D=4$) are classical, the global information-theoretic structure of the induced dynamics and its dependence on the digit length D have received little attention. Here an exhaustive analysis is carried out for $D\in \{3,4,5,6\}$. For each D, all states are enumerated and the transition structure is computed numerically; attractors and convergence distances are obtained, and the induced distribution over attractors across iterations is used to construct “entropy funnels”. Despite the combinatorial growth of the state space, average distances remain small and entropy decays rapidly before entering a slow tail. Permutation symmetry is then exploited by grouping states into digit multisets and, in a further reduction, into low-dimensional digit-gap features. On this gap space, a first-order Markov approximation is empirically estimated by counting one-step transitions induced by the exhaustively enumerated deterministic map. From the resulting empirical transition matrix, drift fields and the stationary distribution are computed numerically. These quantities serve as descriptive summaries of the projected dynamics and are not derived in closed form. Full article

This study aims to establish a method-integrative framework for emotion-oriented architectural image generation. The framework combines Stable Diffusion with targeted LoRA (Low-Rank Adaptation), a lightweight and parameter-efficient fine-tuning approach, together with ControlNet-based structural constraints, to examine how controllable design-element manipulations influence emotional responses. The methodology follows a closed-loop “generation–evaluation” workflow, with each LoRA module independently targeting a single design element. Guided by the relaxation–arousal emotional dimension, the framework is evaluated using subjective ratings and electroencephalogram (EEG) measures. Twenty-seven participants viewed six architectural space categories, each comprising four conditions (baseline, color, material, and form modification). EEG α/β power ratio (RAB) served as the primary neurophysiological marker of arousal. Statistical analysis indicated that LoRA-based modifications of design elements produced distinct emotional responses: color and material changes induced lower arousal, whereas changes in form elicited a bidirectional pattern involving relaxation and arousal. The right parietal P4 electrode site showed the most sensitive emotional response to design element changes, with consistent statistical significance. P4 is a human scalp EEG location associated with cortical activity related to visuospatial processing. Descriptive results suggested opposite directional effects with similar intensity trends; however, linear mixed-effects model (LMM) inference did not support significant group-level linear coupling, indicating individual variation. This study demonstrates the feasibility of emotion-guided architectural image generation, showing that controlled manipulation of color, material, and form can elicit measurable emotional responses in human brain activity. The findings provide a methodological basis for future multimodal, adaptive generative systems and offer a quantitative pathway for investigating the relationship between emotional states and architectural design elements. Full article

Long-tailed image classification remains challenging for vision–language models. Head classes dominate training while tail classes are underrepresented and noisy, and short prompts with weak text supervision further amplify head bias. This paper presents TASA, an end-to-end framework that stabilizes textual supervision and enhances cross-modal fusion. A Semantic Distribution Modulation (SDM) module constructs class-specific text prototypes by cosine-weighted fusion of multiple LLM-generated descriptions with a canonical template, providing stable and diverse semantic anchors without training text parameters. Dual-Space Cross-Modal Fusion (DCF) module incorporates selective-scan state–space blocks into both image and text branches, enabling bidirectional conditioning and efficient feature fusion through a lightweight multilayer perceptron. Together with a margin-aware alignment loss, TASA aligns images with class prototypes for classification without requiring paired image–text data or per-class prompt tuning. Experiments on CIFAR-10/100-LT, ImageNet-LT, and Places-LT demonstrate consistent improvements across many-, medium-, and few-shot groups. Ablation studies confirm that DCF yields the largest single-module gain, while SDM and DCF combined provide the most robust and balanced performance. These results highlight the effectiveness of integrating text-driven prototypes with state–space fusion for long-tailed classification. Full article

The article considers the description of a macrosystem in terms that do not depend on the nature of the macrosystem. The results obtained can be used to describe macrosystem models of thermodynamic processes, and to create interdisciplinary models that take into account interactions of various natures. The macrosystem model is based on its representation in the form of a self-similar oriented weighted graph where the equation of state is fulfilled for each node, which connects extensive variables. One of the extensive variables is entropy, the maximum of which corresponds to the state of equilibrium. For processes in which fluxes are linearly dependent on driving forces, Onsager’s relations are shown to be true, which makes it possible to prove that in the space of stationary processes, entropy production in a closed macrosystem is a metric similar to the Mahalanobis metric, which determines the distance between processes. Zero in such a space indicates reversible processes, and thus the production of entropy shows the degree of irreversibility as the distance from a researched process to a reversible one. Full article