Search Results (226)

The rapid expansion of ski tourism in Northeast China has triggered extensive land use and land cover change (LULCC), yet the micro-scale spatial mechanisms linking historical land conversion to the accessibility of tourist services remain largely unquantified. This study addresses this gap by integrating annual 30 m CLCD land cover data with GIS network analysis of Points of Interest (POIs) around 30 major ski resorts (2018–2023). Specifically, it makes a novel distinction between the accessibility outcomes of construction-oriented and agriculture-oriented land transitions. Results indicate that while forest-to-construction conversion significantly predicts reduced travel distances to services (e.g., hotels: r = −0.532, p < 0.01), a distinct and previously unreported agri-tourism synergy emerges: forest-to-cropland conversion is positively associated with higher per capita tourist spending (r = 0.366, p < 0.05). This finding challenges the conventional zero-sum view of land use competition and suggests that cultivated landscapes can function as complementary tourism assets. These empirical patterns provide an evidence-based framework for integrated land-transport planning in emerging winter sports destinations. Full article

Revealing the spatial differentiation patterns of cultivated land multifunctionality contributes to the improvement of cultivated land protection policies. This study investigated the spatiotemporal differentiation characteristics and functional zoning of cultivated land multifunctionality in Alpine Valley Area from a topographic gradient perspective. An evaluation index system for cultivated land multifunctionality in Alpine Valley Area was constructed across four dimensions: production (PF), social (SF), ecological (EF), and landscape (LF) functions. Using Yulong County, Yunnan Province, as a case study, methods including kernel density analysis, standard deviation ellipse theory, topographic gradient analysis, and hierarchical clustering were employed to quantify the horizontal and topographic gradient characteristics of the multifunctionality of cultivated land from 2010 to 2020, thereby delineating functional zones. Results indicated: (1) Cultivated land multifunctionality shows clear topographically-dependent spatial differentiation: PF concentrates in central basins and northwest specialty agricultural zones, SF overlaps with production but with more dispersed high/low values, EF follows a “high in the center, low on the lateral areas” pattern, and LF remains relatively stable; (2) Significant hierarchical differences in cultivated land functions were observed along the elevation, slope, and terrain niche index (TNI) gradients. PF, EF, and LF generally decreased with increasing elevation, slope, and TNI, whereas the dominance of SF exhibited an inverted-V-shaped distribution along the gradient. (3) The study area was divided into five zones: Flat-Basin Agritourism Zone (FAZ), River-Valley Eco-Agriculture Zone (REZ), Sub-Alpine Specialty Agricultural Production Zone (SSAPZ), Sub-Alpine Steep Slope Integrated Management Zone (SSIMZ), and Mid-Mountain Steep Slope Ecological Conservation Zone (MSECZ), with differentiated strategies proposed for each. This study innovatively integrates a topographic gradient perspective, TNI, and hierarchical clustering to systematically evaluate the cultivated land multifunctionality in Alpine Valley Area, providing a new methodological framework for similar mountainous regions. Full article

The agricultural sector is increasingly confronted with numerous challenges, including declining prices for agricultural products, escalating production costs, intensified globalization, rapid industrialization, urban expansion and growing competition in global markets. To promote rural development and improve farmers’ livelihoods through diversified sources of income, agritourism has been identified as a viable alternative strategy. This study aims to determine the factors influencing farmers’ willingness to participate in agritourism in Mpumalanga Province, South Africa. Primary data were collected from November 2022 to June 2023 using a structured questionnaire and a simple random sampling technique to select 100 farmers. A logistics regression model was used to analyse data. The findings revealed that profitability, non-farm employment, the number of labourers, and access to information positively influence WTP. Age also positively influenced WTP, while marital status showed a negative but significant effect. The findings imply that farmers with stronger financial capacity, labour availability and access to information are more likely to consider agritourism as a diversification strategy. The study suggests strengthening extension services, improving farm profitability and enhancing access to information to increase readiness to engage in agritourism. Full article

We introduce a minimal relational network model in which superconducting-like behavior emerges as a collective phase of constrained connectivity and phase coherence, without assuming microscopic electrons, phonons, or material-specific interactions. The model is formulated as a concrete instantiation of a previously introduced axiomatic relational–informational framework for emergent geometry and effective spacetime, in which geometry and effective forces arise from constrained information flow rather than from a background manifold. Mathematically, this construction is realized on a finite weighted graph with binary edge-activation variables and compact vertex phase variables, sampled through a Gibbs ensemble generated by an additive informational action. The system is represented as a finite weighted graph with weighted edges encoding transport or informational costs, augmented by dynamically activated low-cost channels and compact phase degrees of freedom defined at vertices. The effective edge costs induce a weighted shortest-path metric, providing an operational notion of emergent relational geometry. Using Monte Carlo simulations on two-dimensional periodic lattices, we show that the same informational action supports three distinct emergent regimes: a normal resistive phase, a fragile low-temperature-like superconducting phase characterized by noise-sensitive coherence, and a noise-robust high-temperature-like superconducting phase in which global phase coherence persists under substantial fluctuations. These regimes are identified using purely relational observables with direct graph-theoretic and statistical-mechanical interpretation, including percolation of low-cost channels, phase correlation functions, an operational phase stiffness (helicity modulus), and a geometric diagnostic based on relational ball growth. In particular, we extract an effective geometric dimension from the scaling of low-cost accessibility balls, using a ball-growth relation of the form $⟨B\left(r\right)⟩ ~ r^{d_{eff}}$, revealing a clear monotonic hierarchy between normal, fragile superconducting, and noise-robust superconducting—like regimes. This demonstrates that superconducting-like behaviour in the present framework corresponds not only to percolation and phase alignment, but also to a qualitative reorganization of relational geometry. Robustness is tested via finite-size comparison between 8 × 8, 12 × 12 and 16 × 16 lattice realizations. Within this framework, normal and superconducting-like behavior arise from the same underlying relational mechanism and differ only in the structural stability of connectivity, coherence, and geometric accessibility under fluctuations. The aim of this work is structural rather than material-specific: we do not reproduce detailed experimental phase diagrams or microscopic pairing mechanisms, but identify minimal relational conditions under which low-dissipation, phase-coherent transport can emerge as a generic organizational regime of constrained relational systems. Full article

The present study proposes a fractal-inspired multiscale framework to interpret the structural, thermal, mechanical and dielectric properties of polylactic acid (PLA). Experimental investigations were performed using tensile testing, TG-DTA thermal analysis, X-ray diffraction (XRD) and dielectric spectroscopy. The structural organization was analyzed using XRD data, where a scaling tendency compatible with power-law behavior was identified over a limited $\mathrm{q}$-range. The thermal degradation exhibited a sharp transition, while the mechanical and dielectric responses reflected the heterogenous behavior typical of semicrystalline polymers. Rather than claiming a fully validated fractal model, the present work introduces a conceptual multiscale interpretation, supported by experimental observations, and proposes a fractal integrity index (FII) as an exploratory descriptor integrating structural, thermal and mechanical information. The results suggest that fractal-based descriptors may provide a useful complementary framework for interpreting complex polymer behavior, although further validation across multiple materials and experimental conditions is required. Full article

Baroclinic instability is a fundamental mechanism of midlatitude atmospheric variability, and the Eady model remains one of its most useful idealized representations. In this work, we revisit the Eady configuration from the viewpoint of solution-space geometry rather than the classical normal-mode/growth-rate analysis. Starting from the reduced Eady vertical-structure equation, we show that the ratio of two independent solutions satisfies a Schwarzian-type relation that is invariant under homographic transformations, which naturally leads to an SL(2R) projective symmetry of the solution family. On this basis, we introduce a complex amplitude representation and reformulate coherence in terms of phase–amplitude synchronization constrained by projective invariants. Using Riccati-type constructions along geodesic parametrizations, the reduced dynamics are connected to a Stoler-type transform. Numerical exploration of the reduced model shows a systematic dependence on the control parameter $\omega$: small $\omega$ is associated with simple oscillatory or burst-like behavior, intermediate $\omega$ with period-doubling-like behavior, and large $\omega$ with strongly modulated dynamics and more intricate reconstructed attractors. These results should be interpreted as properties of the reduced symmetry-based model, and they suggest that projective invariants may provide a useful framework for classifying organization regimes in Eady-type disturbances, complementary to classical growth-rate analyses. Full article

The Common Agricultural Policy (CAP), as one of the most prominent European Union policies, has increased concerns about the environmental sustainability of farms, particularly since its major reform in 1992. The changes implemented since this reform have intended to promote more integrated rural development, with deeper interrelationships between the agricultural sector and other rural activities, including agritourism, from the perspective of diversification of the activities that can be developed on farms and in rural areas. The idea of this strategy is to bring more income for farmers by changing the policy measures and enhancing a more sustainable agricultural and rural development. Nonetheless, the interrelationships between the diversification of activities in the agricultural sector and the characteristics of the farms have not yet been fully explored. In this context, this research aims to bring more insight into how agritourism revenues can be predicted by the farm characteristics in the European Union (UE) agricultural regions, considering data from FADN (Farm Accountancy Data Network), for 2023, using machine learning algorithms (following IBM SPSS Modeler Version 18.4 procedures). The results obtained show that agritourism output is higher in EU countries with larger farms (Slovakia and Czechia) and that are more economically dynamic (Netherlands and Denmark). Slovenia, Austria, Italy, and Finland are countries in which farms have a higher part of agritourism revenues in the total output. There is space to better explore agritourism potentialities and to improve the availability of data. When the total crop output increases by 1%, agritourism revenue grows by 0.719%. Full article

Agritourism is an expanding form of experience-based rural tourism, yet limited empirical research explains how experiential marketing shapes perceived value and satisfaction in authentic farming contexts. Drawing on Schmitt’s Strategic Experiential Modules and the Memorable Tourism Experience (MTE) framework, this study develops and tests a structural model linking agritourism experience, perceived value, and satisfaction. Survey data from 398 visitors across twelve certified agritourist communities in Taiwan were analyzed using CFA and SEM. Results show that agritourism experiences significantly enhance perceived value and directly increase satisfaction, with perceived value exerting a strong mediating effect. From a sustainability perspective, the findings underscore the distinctiveness of agritourism, where authenticity, natural variability, and human–land interactions generate experiential outcomes not replicable in constructed tourism spaces. The study advances experiential marketing theory and offers practical guidance for rural tourism development, thereby supporting sustainable rural development by fostering long-term tourist engagement and local economic vitality. Full article

Background: Gamma Knife radiosurgery (GKS) delivers highly conformal intracranial irradiation, yet clinical decision-making still relies predominantly on physical dose metrics that do not account for fractionation, dose rate, treatment time, or DNA repair. Classical radiobiological models—including the linear–quadratic (LQ) formula and the Jones–Hopewell single-session repair model—do not extend naturally to 3- and 5-fraction GKS. Meanwhile, growing evidence suggests that biologically effective dose (BED) may better capture radiosurgical response in selected pathologies. A unified, biologically grounded, multi-fraction GKS framework has been lacking. Methods: We developed AI-BED-Fx, the first multi-fraction extension of the Jones–Hopewell radiobiological model capable of computing fraction-resolved BED for 1-, 3-, and 5-fraction GKS. The framework incorporates α/β ratio, dual-component repair kinetics, isocentre geometry, beam-on–time structure, and lesion-specific biological parameters. Four synthetic pathology-specific cohorts—arteriovenous malformation (AVM), meningioma (MEN), vestibular schwannoma (VS), and brain metastasis (BM)—were generated using distinct radiobiological signatures. Machine-learning models were trained to quantify the predictive value of physical dose versus BED for local control or obliteration. Additional experiments included Bayesian estimation of α/β and a neural-network surrogate for fast BED prediction. An exploratory comparison with a 60-lesion clinical brain–metastasis dataset was performed to assess whether key trends observed in the synthetic BM cohort were consistent with real radiosurgical outcomes. Results: AI-BED-Fx produced realistic pathology-specific BED distributions (AVM 60–210 Gy_2.47; MEN 41–85 Gy_3.5; VS 46–68 Gy₃; BM 37–75 Gy₁₀) and biologically coherent dose–response relationships. Predictive modeling demonstrated strong pathology dependence. In AVM, the three models achieved AUCs of 0.921 (Model A), 0.922 (Model B), and 0.924 (Model C), with corresponding Brier scores of 0.054, 0.051, and 0.051, with BED-based models performing best. In meningioma, BED was the dominant predictor, with AUCs of 0.642 (Model A), 0.660 (Model B), and 0.661 (Model C) and Brier scores of 0.181, 0.177, and 0.179, respectively. In vestibular schwannoma, the narrow BED range resulted in minimal BED contribution, with AUCs of 0.812, 0.827, and 0.830 and Brier scores of 0.165, 0.160, and 0.162, with physical dose and tumor volume determining performance. In brain metastases, outcomes were driven primarily by volume and physical dose, with AUCs of 0.614, 0.630, and 0.629 and Brier scores of 0.254, 0.250, and 0.253, showing negligible improvement from BED. AI-BED-Fx also accurately recovered the true α/β from synthetic outcomes (posterior mean 2.54 vs. true 2.47), and a neural-network surrogate reproduced full radiobiological BED calculations with near-perfect fidelity (R² = 0.9991). Conclusions: AI-BED-Fx provides the first unified, biologically explicit framework for modeling single- and multi-fraction Gamma Knife radiosurgery. The findings show that the predictive usefulness of BED is pathology-specific rather than universal, and that radiobiological dose provides additional predictive value only when repair kinetics and dose–response biology support it. By integrating mechanistic radiobiology with machine learning, AI-BED-Fx establishes the conceptual and computational foundations for biologically adaptive, AI-guided radiosurgery, and cross-pathology comparison of treatment response. This work uses large radiobiologically grounded synthetic cohorts for methodological validation; limited real-patient data are included only for exploratory consistency checks, and full clinical validation is planned. Full article

Comprehensively integrating eco-farms and rural tourism represents a crucial pathway for advancing rural revitalization and sustainable development; however, existing research has pre-dominantly focused on the net effects of individual factors, failing to reveal the underlying complexity of multiple co-occurring factors and their interactive logics. With the aim of addressing this theoretical gap, we employ a configurational approach that integrates Necessity Condition Analysis (NCA) with fuzzy set qualitative comparative analysis (fsQCA), and data was collected from 1041 Chinese ecological farms (ecological farm operators) using a structured questionnaire, to systematically explore the integrated complex configurational driving logic. Our findings reveal that no single necessary condition independently causes high-level integration. The fsQCA results further reveal that high-level integration is attainable via two distinct, yet equivalent pathways. First, the “Endogenous–Technological–Economic Synergistic Drive Model” emphasizes the intrinsic development needs of business entities, requiring extensive synergy with external technological empowerment and the regional economic environment; second, the “re-source–market–integration linkage-driven” pathway leverages unique resource endowments and achieves value transformation through efficient resource integration capabilities, guided by clear market demand. Both pathways exhibit functional substitutability among their conditions, demonstrating strategic systemic flexibility. Additionally, in the analysis of non-high-integration configurations, we draw upon structural hole theory to categorize systemic failures caused by missing key connections or factor misalignment. Full article

This paper empirically investigates the influence of specific direct sales approaches in attracting visitors to an agritourism operation and its profitability using survey data from the U.S. This study further examines the mediating role of the number of visits to a farm in the relationships between specific direct sales approaches and profitability. Agritourism operations enhance economic viability and sustain the business by opening farms to visitors for education, recreation, entertainment, and direct sales of farm products and services. The goal is to invite visitors to a farm and enhance income. Previous studies in the U.S. show that on-farm direct sales, in general, show a positive association, whereas off-farm direct sales show a negative association with the profitability of agritourism operations, along with many other factors. Farmers consider U-pick, sales through a farm stand/store, and subscription farming or community-supported agriculture (CSA) (on-farm pick-up) as on-farm, and CSA (off-farm delivery) and selling at a farmers’ market as off-farm direct sales approaches. However, which specific approach attracts visitors to a farm and generates profitability is not known. Multivariate analysis using the recently collected data from a U.S. national survey of operators reveals that on-farm direct sales such as a U-pick and a farm stand/store attracted significantly more visits to an agritourism operation, which ultimately yielded higher profitability. In contrast, the selling of produce at farmers’ markets attracted significantly fewer visits to the farm and reportedly reduced profitability. These results are adjusted for other factors including various agritourism experiences offered to the visitors. Moreover, as theoretically expected, the number of visits mediated the effects of specific direct sales (particularly a U-pick and farm stand sales) on profitability. This evidence has implications for agritourism operators, policymakers, and extension educators engaged in starting, expanding, and promoting direct sales via agritourism operations for their economic viability and sustainability. Full article

Living systems exhibit anticipation, adaptability, and resilience that cannot be fully explained by stimulus–response models, static homeostasis, or convergence-based optimization. This work addresses this gap by proposing a theoretical framework in which a central aspect of biological function is understood through the geometry and stability of distributions over unrealized but accessible future trajectories. We formalize these distributions as a counterfactual manifold, defined as a probabilistically supported subset of path space induced by a system’s effective internal dynamics. Using tools from information geometry and dynamical systems theory, we analyze adaptive systems that modify the laws governing their own future trajectories and construct explicit dual-channel adversarial dynamics that couple processes expanding future possibilities with antagonistic processes enforcing feasibility constraints. We show that adaptive systems of this kind are generically unstable, tending toward either collapse of accessible futures or unbounded sensitivity to perturbation. Constructive adversarial dynamics are sufficient to stabilize counterfactual geometry without requiring convergence to a fixed point. A minimal adversarial model reveals three generic regimes: collapse, runaway sensitivity, and bounded non-convergent regulation. The framework yields operational, falsifiable predictions through measurable proxies based on response diversity, perturbation sensitivity, recovery geometry, and boundary residence, allowing these regimes to be discriminated using finite observations without reconstructing underlying state-space dynamics. Interpreting disease as instability of counterfactual geometry provides a unifying language for understanding rigidity, volatility, and context dependence across biological domains. Rather than replacing mechanistic models, the proposed framework offers a higher-level geometric and dynamical perspective in which such models can be embedded and compared, shifting attention from component-level dysfunction to the stability of biological futures and establishing a principled foundation for analyzing disease, intervention, and adaptability across scales. Full article

Generative Adversarial Networks (GANs) perform well on natural images but often fail in domains governed by strict geometric or symbolic constraints. This work focuses on convolutional GANs and studies how their inductive biases interact with two contrasting types of synthetic image data: fractal patterns, characterized by self-similarity and scale-invariant local structure, and Euclidean shapes, defined by simple geometric primitives and rigid global constraints. Using multiple convolutional GAN architectures (DCGAN, WGAN-GP, and SNGAN), two resolutions (64 × 64 and 128 × 128), and a suite of evaluation metrics, we compare adversarial training behavior on these datasets under tightly controlled conditions. Fractal datasets yield stable training dynamics and perceptually plausible generations, whereas Euclidean shape datasets consistently exhibit structural failure modes that persist under higher resolution, smoother shape representations, and architectural stabilization. Geometry-aware metrics reveal severe violations of global shape consistency in Euclidean outputs that are not reliably captured by standard perceptual or distributional measures such as FID, SSIM, or LPIPS. We argue that these findings reflect a fundamental inductive bias of convolutional generative models toward a locally rich, scale-repeating structure rather than globally constrained geometry. Rather than indicating that fractals are intrinsically easier to model, our results show that Euclidean geometry exposes limitations of adversarial generative learning that remain hidden under conventional evaluation. From this perspective, fractal datasets serve as informative diagnostic benchmarks for probing how adversarially trained convolutional generators handle scale-invariant structure versus globally constrained geometry, and our results highlight the need for domain-aware metrics and alternative architectural biases when applying generative models to structured or symbolic data. Full article

Background: Early detection of dental caries is essential for effective oral health management. Current diagnostic workflows rely heavily on radiographic imaging, which involves infrastructure requirements, workflow coordination, and resource considerations that may limit frequent use in high-throughput or resource-constrained settings. These contextual factors motivate exploration of adjunct screening concepts that could support front-end triage decisions within existing care pathways. This study evaluates, in simulation, whether modeled tooth-percussion response signals contain sufficient discriminative information to justify further translational and managerial investigation. Implementation costs, workflow optimization, and economic outcomes are not evaluated directly; rather, the objective is to assess whether the technical preconditions for a potentially scalable screening concept are satisfied under controlled in silico conditions. Methods: An in silico model of tooth percussion was developed in which enamel, dentin, and pulp/root structures were represented as a simplified layered mechanical system. Impulse responses generated from simulated tapping were used to compute the modeled surface-vibration response (enamel-layer displacement), which served as a proxy for a measurable percussion-related signal (e.g., contact vibration), rather than a recorded acoustic waveform. Carious conditions were simulated through depth-dependent reductions in stiffness and effective mass and increases in damping to represent enamel and dentin demineralization. A synthetic dataset of labeled simulated signals was generated under varying structural parameters and measurement-noise assumptions. Machine-learning models using Mel-frequency cepstral coefficient (MFCC) features were trained to classify healthy teeth, enamel caries, and dentin caries at a screening (triage) level. Results: Under baseline simulation conditions, the classifier achieved an overall accuracy of 0.97 with balanced macro-averaged F1-score (0.97). Misclassifications occurred primarily between healthy and enamel-caries categories, whereas dentin-caries cases were most consistently identified. When measurement noise and structural variability were increased, performance declined gradually, reaching approximately 0.90 accuracy under the most challenging simulated scenario. These results indicate that discriminative information is present within the modeled signals at a screening (triage) level, meaning that higher-risk categories can be distinguished probabilistically rather than with definitive diagnostic certainty. Sensitivity and specificity trade-offs were not optimized in this study, as the objective was to assess separability rather than to define clinical decision thresholds. Conclusions: Within the constraints of the in silico model, simulated tooth-percussion response signals demonstrated discriminative patterns between healthy, enamel caries, and dentin caries categories at a screening (triage) level. These findings establish technical plausibility under controlled simulation conditions and support further investigation of percussion-based screening as a potential adjunct to clinical assessment. From a healthcare management perspective, the present results address a prerequisite question—whether such signals contain sufficient information to justify translational research, rather than demonstrating workflow optimization, cost reduction, or system-level impact. Clinical validation, threshold optimization, and implementation studies are required before managerial or operational benefits can be evaluated. Full article

This work is axiomatic and structural in nature and is not intended as a phenomenological physical theory, but as a framework clarifying minimal informational primitives from which geometric and dynamical descriptions may emerge. We present a background-independent framework in which physical geometry, interaction-like forces, and spacetime arise as effective descriptions of constrained relational information rather than as fundamental entities. The only primitive structure is a network of degrees of freedom linked by admissible informational relations, each subject to quantifiable constraints on accessibility or flow. The motivation is to identify whether a single minimal relational primitive can account jointly for the emergence of geometry, forces, and spacetime, without presupposing a manifold, fields, or fundamental interactions. The framework is formalized using weighted relational graphs in which constraint weights encode limitations on information flow between degrees of freedom. Effective geometry is defined operationally through minimal constraint cost along relational paths, yielding an emergent metric without assuming spatial embedding. Relational evolution is modeled via a minimal configuration-space dynamics defined by local rewrite moves, and a statistical description is introduced through an informational action that governs coarse-grained response rather than serving as a fundamental dynamical law. Curvature-like observables are defined using transport-based comparisons of local accessibility structure. Within this setting, metric structure emerges from constrained relational accessibility, while curvature-like behavior arises from heterogeneity in constraint structure. Effective forces appear as entropic or informational action gradients with respect to coarse-grained control parameters that modulate relational constraints, and are interpreted as emergent responses rather than primitive interactions. A finite worked example explicitly demonstrates the emergence of nontrivial distance, curvature proxies, and an effective force via geodesic switching under constraint variation, without assuming fundamental spacetime, fields, or particles. The results support an interpretation in which geometry, forces, and spacetime are representational features of constrained information flow rather than fundamental elements of physical law. The framework clarifies conceptual distinctions and points of compatibility with existing approaches to emergent spacetime, and it outlines qualitative expectations for regimes in which smooth geometric descriptions are expected to break down. The work delineates the scope and limits of geometric description without proposing a complete phenomenological theory. Full article