Search Results (315)

The rapid expansion of road construction has significantly contributed to economic development and regional connectivity. However, linear infrastructure such as roads, railways, and utility corridors has also introduced considerable ecological disruptions. Although increasing global attention is being paid to mitigating these effects, most existing research primarily focuses on quantifying and monitoring external environmental changes (e.g., landscape structure or vegetation coverage) while often neglecting internal ecological dynamics such as ecosystem resilience. Previous road-ecology studies have extensively examined road-buffer effects, land-use/land-cover changes, vegetation-index dynamics, and landscape fragmentation. Therefore, the contribution of this study does not lie in proposing an entirely new class of indicators. Rather, it lies in applying a combined external–internal assessment framework to a recently constructed expressway corridor by jointly examining annual land-cover transitions and leaf area index (LAI)-derived temporal variability/resilience indicators across multiple distance buffers and spatial resolutions. This design allows us to compare whether structural land-cover changes and vegetation time-series responses show similar distance–decay patterns around the expressway corridor. The results show that: (1) Land-cover transformation was mainly concentrated within the first 500–1000 m from the expressway, especially for impervious surface expansion and vegetation loss. Multi-indicator distance-gradient analysis showed that land-cover change intensity and LAI-derived variability indicators gradually approached the distal reference condition at approximately 2000 m, which was therefore used as an empirical corridor-analysis boundary rather than a definitive ecological impact threshold. (2) Within the 2000 m buffer zone, forest area increased from 21.866 km² in 2001 to 45.370 km² in 2023, while impervious surface area increased from 3.016 km² to 6.869 km². During the construction and early operation period from 2018 to 2023, impervious surface area increased from 6.268 km² to 6.869 km², indicating localized artificial surface expansion along the expressway corridor. (3) During 2018–2023, the 30 m LAI product showed a 22.3% increase in coefficient of variation (CV), indicating enhanced relative LAI variability. In contrast, temporal autocorrelation (TAC) did not show the consistent increase expected under classical critical slowing down theory, suggesting that TAC-based evidence for resilience decline was weak or inconclusive during this short period. The observed TAC/CV changes were interpreted as critical slowing down (CSD)-related vegetation variability signals, rather than as a distinct or definitive critical slowing down signature. (4) The multi-resolution comparison showed weak pixel-level correspondence between the 30 m and 250 m LAI products, indicating clear scale dependence rather than robust multi-scale consistency. The 250 m data were useful for characterizing long-term regional background trends, whereas the 30 m data were more suitable for detecting localized corridor-scale vegetation variability. Thus, the multi-resolution analysis should be regarded as a scale-sensitivity assessment rather than as direct cross-scale validation. Full article

The natural disasters caused by climate change are increasingly threatening the livelihood sustainability of rural households. How to enhance the adaptability of farmers has become a major issue that urgently needs to be addressed in rural development. An increasing amount of research suggests that the digital transformation in rural areas may provide solutions to this problem. This study selected large sample data from the China Family Panel Studies (CFPS) from 2014 to 2020 to empirically test the role of digital economy in mitigating the adverse impact of natural disasters on livelihood resilience of rural households in China. The empirical results indicate that although natural disasters have a significant negative impact on the livelihood resilience of rural households, the embedding of digital technology can alleviate this negative impact to some extent by expanding non-agricultural employment opportunities for rural households, enhancing households’ access to credit, and improving agricultural production strategies. During this process, the buffering capacity, self-organization ability, and learning ability of rural families have been significantly improved. Moreover, the mitigating effect of the digital economy exhibits heterogeneity. It is more pronounced in the central and western regions than in the eastern regions, and also more evident among high-income families relative to low-income families. The key to improving the benefits of digital technology may lie in tailored policy interventions and digital skills training for farmers. Finally, this study provides empirical evidence from rural China on the role of the digital economy in strengthening farmers’ adaptive capacity against disaster risks. While these findings are context-specific, they may still provide valuable insights for other agriculture-dependent developing countries facing persistent climate risks. Full article

Micro-expression recognition (MER) has a lot of applications in lie detection, education, healthcare, etc., as involuntary micro-expressions (MEs) may provide subtle facial cues associated with affective responses. With the development of deep learning, many studies have recently employed Vision Transformers (ViTs) to investigate MER, since ViTs show promising performance in various visual domains due to their excellent local–global modeling ability. However, such methods confront two fundamental challenges: First, fine-grained visual features are needed to capture the subtle facial movements of MEs, which ViTs relatively fall short on due to coarse patch resolution constrained by their quadratic complexity. Second, the data-intensive nature of ViTs impedes effective learning given the limited scale of ME data. To overcome the aforementioned limitations of using ViTs for MER, we propose the Prior-aware Sparse Transformer (PAST), a novel Transformer-based architecture integrating spatial and semantic prior knowledge synergistically into a sparse attention mechanism, enabling linear-complexity processing of large amounts of fine-grained features. Specifically, we first designed an extraction algorithm to generate a representative set of motion-intensive Principal Anchors, which are used to guide the model’s focus on biologically critical regions during sampling. Second, we introduced the Semantic Dictionary, which was trained with a carefully designed self-contrastive loss to embed task-invariant discriminative semantics of the anchors. Such global semantics further modulate patch sampling and attention weighting in the sparse attention procedure, achieving better training performance with limited ME data. Extensive evaluations on MEGC and CD6ME protocols demonstrate state-of-the-art performance, validating PAST’s efficacy for MER. Full article

Non-invasive electroencephalography (EEG) enables practical brain-state monitoring for applications such as emotion recognition and event-related potential (ERP)-based deception detection. However, robust EEG classification remains challenging because of noise, non-stationarity, limited labeled data, and substantial inter-subject variability. In this work, we present a sensor-density-aware framework that applies different deep architectures to low- and high-channel EEG acquisition settings and augments the training data using a physiologically constrained signal-level procedure. For the 5-channel LieWaves dataset, the CNN–Transformer achieved $97.14\pm 1.36\%$ subject-dependent accuracy with augmentation, compared with $92.91\pm 4.34\%$ without augmentation. For the 62-channel SEED dataset, the Inception CNN achieved $98.52\pm 0.79\%$ with augmentation and $98.44\pm 0.83\%$ without augmentation. The improvement on LieWaves was statistically significant, whereas the small improvement on SEED was not statistically significant. Under subject-independent evaluation, performance was $57.83\pm 8.96\%$ on LieWaves with augmentation and $57.95\pm 8.38\%$ on SEED. These results demonstrate strong subject-dependent performance while confirming that cross-subject generalization remains challenging. Overall, the proposed framework combines sensor-density-aware architecture selection with signal-level augmentation and provides a systematic comparison of subject-dependent and subject-independent EEG classification. Full article

Regression methods are widely used to investigate macroeconomic relationships; however, they are generally estimated without first examining whether the underlying variables exhibit fractal structures, persistence, and chaotic dynamics. Although nonlinear regression models relax the assumption of linearity, they rarely account for the complex geometric, long-memory, and dynamical properties that characterize macroeconomic time series. Motivated by this limitation, this study proposes a fractal-oriented Lie regression framework that integrates fractional persistence and Lie algebra to model nonlinear macroeconomic interactions within a unified analytical structure. For Türkiye, the empirical analysis employs monthly data on inflation, interest rates, exchange rates and oil prices covering the period 2000M1–2026M1, encompassing major economic crises and structural breaks. Prior to model estimation, the dynamical characteristics of the variables are examined using entropy measures, long-range dependency analysis, Lyapunov exponents and attractors. The results reveal persistent fractal structures, significant fractional dependence and chaotic behavior, indicating that macroeconomic variables evolve within a complex nonlinear dynamical system rather than around a conventional equilibrium. Based on these results, the variables are represented within a Lie algebra framework in which nonlinear transformation matrices preserve the underlying geometric structure while simultaneously capturing both self-dynamics and cross-variable interactions. The proposed Lie regression model demonstrates substantial improvements over standard regression methods in both model adequacy and forecasting performance. Oil prices emerge as the dominant transmitter of shocks by generating pronounced asymmetric effects on inflation, exchange rates and overall macroeconomic stability. The model achieves remarkable forecasting accuracy by reducing RMSE, MAE, and MAPE from 18.58, 13.61, and 69.92 under a standard regression model to 0.27, 0.22 and 16.4, respectively. Finally, the estimated Lie transformation matrix is employed as a policy-simulation mechanism to evaluate the transmission of alternative oil-price shocks. Scenarios based on 5%, 10%, and 20% increases in oil prices quantify the resulting adjustments in inflation, interest rates, and exchange rates by providing forward-looking assessments of macroeconomic vulnerability. The proposed framework extends standard regression analysis by explicitly incorporating fractional persistence and chaotic dynamics into a Lie algebra representation, thereby offering a more accurate and theoretically consistent approach for modeling complex macroeconomic systems. Full article

This paper presents a theoretical algebraic block transformation based on bi-periodic Pell matrices and elliptic-curve point-valued blocks. The construction uses block-dependent scalar key matrices $K_i={\tilde{P}}_{m+i-1}(a,b)\in G{L}_2\left({\mathbb{F}}_q\right)$ to transform plaintext blocks whose entries lie in a selected elliptic-curve subgroup $G=⟨G_0⟩\subseteq E\left({\mathbb{F}}_p\right)$ of prime order $q$. The encryption operation is defined by the elliptic-curve scalar matrix action $M_i=B_i\star K_i,$ where the entries of $K_i$ act as scalar coefficients modulo $q$, and point additions are performed in $G$. The main purpose of the construction is to formalize the interaction between recursive Pell-type matrix sequences over ${\mathbb{F}}_q$ and elliptic-curve point-valued block transformations. The algebraic correctness follows from the invertibility of $K_i$ over ${\mathbb{F}}_q$ and the compatibility of the $\star$-action with the matrix multiplication. The deterministic version is not claimed to provide semantic security or IND-CPA security without additional randomization, nonce-dependent masking, and formal security proofs. Full article

Vehicular platoons under realistic scenarios are prone to actuator saturation, model uncertainties, and external disturbances, which degrade transient tracking and spacing stability. Conventional prescribed-performance control (PPC) strictly requires initial errors to lie within a predefined envelope, while finite/fixed-time schemes cannot directly assign the settling-time bound. To resolve these limitations, this paper proposes a practical predefined-time sliding-mode adaptive platoon control strategy under input saturation constraints. Specifically, a smooth hyperbolic-tangent approximation combined with a mean-value-theorem-based gain formulation is utilized to handle saturation nonlinearity and simplify stability analysis. A novel initial-error transformation is developed to eliminate the stringent envelope constraint on the original initial tracking error. Furthermore, a predefined-time sliding variable and an adaptive compensation mechanism are synthesized to guarantee that tracking errors converge into a bounded neighborhood of the origin within a user-specified time. Numerical simulations and comparisons with predefined-time sliding-mode and PID controllers demonstrate that the proposed strategy eliminates initial error restrictions and suppresses chattering. Compared to the alternative schemes, the proposed method restricts the maximum tracking error within 0.05 m—representing reductions of approximately 77% and 91%, respectively—and shortens the settling time to within 2 s. These results validate its effectiveness for robust cooperative platoon control. Full article

Symmetry operations are usually studied within the frameworks of group theory, geometry, and operator algebra. In the present work, a Ramsey-theoretic approach to symmetry is developed. Symmetry operations are treated as operators serving as vertices of complete bi-colored graphs, called symmetry graphs (SGs). Two symmetry operators are connected by a maroon edge when they commute and by a teal edge when they do not commute. Thus, the commutation structure of a symmetry group is transformed into a combinatorial object suitable for Ramsey-theoretic analysis. The introduced coloring is generally non-transitive, leading naturally to nontrivial complete bi-colored graphs constrained simultaneously by group-theoretical and combinatorial principles. It is shown that every symmetry graph containing six vertices necessarily contains either a monochromatic commuting triangle or a monochromatic non-commuting triangle as a direct consequence of the classical Ramsey theorem $R\left(\mathrm{3,3}\right)=6$. The framework is illustrated for the symmetry groups of the equilateral triangle, regular tetrahedron, crystallographic point groups, infinite Cairo pentagonal tilings, and the triangular Ising ferromagnet. Higher-order structures, including teal quadrangles, second-order graph symmetries, infinite monochromatic cliques, and Lie-algebraic constraints arising from the Jacobi identity, are discussed. The proposed framework establishes a new connection between symmetry theory, Ramsey theory, graph theory, crystallography, and operator algebra. Full article

We give a proof of Cartan’s Theorem on totally geodesic submanifolds for real analytic manifolds endowed with a real analytic, torsion-free, affine connection. We apply the theorem to real analytic Hadamard manifolds and, more generally, to real analytic manifolds with a torsion-free, analytic, affine connection, such that at a manifold point $p\in M$, the exponential map is a real analytic diffeomorphism from the tangent space $T_p\left(M\right)$ to M. Examples of manifolds with this property are statistical manifolds with a cubic form divisible by the metric, as was recently proven. We also give examples of totally geodesic submanifolds obtained as fixed points of affine transformations of M and, moreover, as certain submanifolds of connected Lie groups with the 0-connection of Cartan–Schouten. Finally, we also determine all connected complete totally geodesic surfaces of the Riemannian manifold $({\mathcal{P}}_2,g)$ of symmetric positive definite $2\times 2$ real matrices, endowed with the trace metric g. Full article

The Yilgarn Craton hosts some of the world’s largest orogenic gold deposits, yet discovery rates have declined sharply as near-surface resources approach exhaustion. Exploring deeper, covered terrains demands new predictive tools that transcend the limitations of conventional mineral prospectivity mapping (MPM). Here we integrate convolutional neural networks (CNNs) and Vision Transformers to construct a data-driven MPM framework trained on 6028 gold occurrences across 16 map sheets in the Yilgarn Craton. The CNN achieves 79.3% classification accuracy by capturing local structural features; the Vision Transformer attains 74.0% but identifies prospective zones in data-sparse regions that the CNN misses. An empirical test was conducted in the untrained Sandstone Greenstone Belt to verify the model’s generalization ability. The results reveal that most known gold deposits lie within the high metallogenic potential zones defined by the model. Meanwhile, three prospective targets are newly delineated in this area based on model prediction, including northwest-trending ultramafic units, a basalt-sediment transition zone and NW-SE trending amphibolite units along the Edale Shear Zone. These targets are hardly identifiable by conventional exploration techniques and merit further field investigation. These results demonstrate that CNN–Transformer integration provides a robust, complementary framework for orogenic gold exploration in covered terrains. Full article

The tale commonly known as “Hunter Catches Birds” circulates widely across South Asia, the Islamicate world, and insular Southeast Asia. Despite linguistic, religious, and cultural differences, the narrative architecture of the Hunter Catches Birds tale displays remarkable continuities across Buddhist, Persian, Malay, Indonesian, and Javanese traditions. Its persistence across radically different religious and cultural settings raises a broader question of how narrative meaning remains recognizable through continual reinterpretation. In early Malay renderings, particularly within the Hikayat Bayan Budiman tradition, oral materials are reorganized into framed and nested literary structures. These forms enable both textual and visual interplay while supporting ethical instruction alongside aesthetic elaboration. Frequently positioned as an introductory episode in parrot-cycle literature, the story integrates motifs such as collective escape, feigned death, interspecies conflict, and the tension between loyalty and betrayal. These narrative elements remain open to reinterpretation in different moral and cultural settings. Drawing upon Sanskrit, Persian, Uyghur, Malay, Indonesian, and Javanese materials, this study examines how the tale moved across oral, manuscript, and visual traditions. Rather than treating the narrative as a fixed folktale type, the article approaches it as a flexible modular structure whose ethical meanings were continually reshaped across changing religious and social environments. These interactions generate layered systems of meaning in which image and text jointly shape narrative tension, vulnerability, and strategic judgment. In Persian miniature traditions, scenes of entrapment, sacrifice, and escape are organized through sequential composition and spatial tension, allowing conflict, vulnerability, and narrative causality to be experienced visually as well as textually. By tracing these transformations, this study argues that the enduring vitality of the Hunter Catches Birds tradition may lie less in narrative stability than in the sustained reinterpretation of repeated narrative structures across textual and visual cultures. Full article

In the epistemological constellation of contemporary visual education, web comics establish themselves as semiotic devices with a dual pedagogical value. On the one hand, they represent the advanced synthesis of multimodal codes (visual, textual, sound); on the other, they structure participatory learning environments typical of digital culture. This contribution starts from the theoretical premise that digital comic narratives, in their hypertextual and algorithmic essence, constitute true liminal spaces where ontological meanings are negotiated and transformative visual literacy skills are developed. The research, conducted according to the methodological paradigm of design-based research and rooted in the framework of multimodal social semiotics, demonstrates how sequential narrative structure and visual metaphors reduce cognitive load, through scaffolding. The interactive mechanisms typical of the medium (comments, sharing, remixes) promote an aesthetic of participation, transforming students from passive users to producers of knowledge, according to the principles of connected learning. The analysis focuses on: Panda Likes Bevilacqua, Totally Unnecessary Comics by Leone; Rossoni’s Rouge Worms, Lele Corvi’s strips, Natangelo’s cartoons. The limitations of the study lie in the limited sample and its preliminary nature, as it analyses the device itself without evaluating its implications in the classroom or the professional skills (TPACK) necessary for teachers. Large-scale teaching feasibility remains to be investigated in future applied experiments, which involve the direct involvement of classes and teachers. From a pedagogical perspective, web comics are effective teaching tools for students with ASD. Their community-based nature requires a recalibration of traditional pedagogical frameworks towards more ecological approaches. Full article

Although AI systems are increasingly being used as strategic tools in the agri-food sector, empirical evidence regarding their use and integration into wine marketing by wineries has been limited to date. The reasons for this delay may lie in various factors, both internal and external to the companies. This study aims to help fill this gap by examining some possible causes that could influence the propensity to use this technology and by attempting to analyze them. In-depth semi-structured interviews with marketing managers of 17 selected wineries in Italy and Spain were carried out. Process flows and Social Network Analysis (SNA) were developed to investigate marketing structures, levels of digitalization, and suitability for technological innovation. Findings show that wine marketing processes are data-driven systems integrating strategic and operational dimensions, but their implementation remains partial and fragmented. The observed wineries exhibit a moderate level of digitalization, characterized by the potential availability of data but limited capabilities in data collection and integration. SNA reveals a dense and homogeneous relational network, which could support shared data management systems; however, inter-firm collaboration is largely absent. Overall, the study identifies a latent potential for AI-driven marketing transformation, which is hindered by limited internal capabilities, and cultural resistance. Full article

This paper studies a realization-theoretic problem inside the standard Lorentz-covariant Fourier-dual framework on $L^2\left({\mathbb{R}}^{3,1}\right)$: whether position-space and momentum-space geometric translations can be placed on equal structural footing without leaving the ordinary X- and K-polarized realizations. Working on the common Schwartz core $\mathcal{S}\left({\mathbb{R}}^{3,1}\right)$, we first isolate a Fourier-compatibility obstruction: Fourier transform exchanges geometric translations with character actions, while the Poincaré algebra contains at most one Lorentz-covariant abelian translation ideal. The main result is that, within the resulting Fourier-compatible realization class, the minimal operator-generated Lie algebra is the Lorentz–Heisenberg algebra. We then determine the full center of its universal enveloping algebra, derive the normalized Lorentz-bivector invariants, orbit data, and connected stabilizers in nondegenerate sectors, and show that the orbit variable is a normalized Lorentz bivector rather than a momentum vector. Finally, for fixed spectral elements in the dual translation sectors, we derive the associated scalar, Dirac, and vector equations in position and momentum space and show that, in the regular polarized realizations, the represented Heisenberg sector induces dual local abelian phase groups, compatible covariant derivatives, curvatures, and primary Dirac–Maxwell systems. Full article

The fundamental purpose of education—preparing new generations to be contributing members of society—remains constant, yet achieving this has become increasingly complex amid multifaceted technological, cultural, economic, and social transformations. Educational leaders worldwide continuously seek innovative pedagogical models addressing diverse learner needs and rapid societal changes. However, this article challenges the assumption that educational quality requires constant novelty, arguing that solutions lie in the innovative integration of established pedagogical theories developed over the past 150 years by scholars such as Dewey, Vygotsky, Piaget, Feuerstein, Gardner, Freire, and others. The article’s primary objective is to encourage education leaders and teacher educators to reconceptualize innovation by prioritizing pedagogical integration over continuous adaptation to rapidly expanding domain-specific knowledge and emerging technologies. Accordingly, this article employs a conceptual synthesis of major pedagogical approaches to equip educators with theoretical foundations and practical tools to foster learner independence, critical thinking, and holistic development across cognitive, emotional, and social domains. It will also promote inclusion through a practical framework integrating pedagogical theories, addressing diversity from a dual perspective, recognizing that both teachers and learners bring unique characteristics, strengths, and needs. Moreover, developing independent learners requires empowering teachers to cultivate unique professional methodologies grounded in integrated pedagogical understanding, so that a shift from innovation-centered to integration-centered teacher education may serve as a sustainable path toward educational quality and academic excellence. Full article