Search Results (1,666)

International tourism plays an important role in the global service economy, contributing to trade, employment, and regional development. For this reason, identifying the factors that influence tourist flows is an important issue for tourism policy, market strategy, and infrastructure planning. A large body of research has applied gravity models to analyze tourism flows between countries. While this approach provides a clear economic interpretation, it is usually based on linear specifications and may therefore capture only part of the relationships present in tourism data. This study examines the economic and geographic determinants of international tourism flows to Mongolia using a framework that combines a traditional gravity model with machine learning techniques. Mongolia serves as an instructive empirical setting, a landlocked, geographically peripheral destination whose inbound demand determinants have received limited systematic empirical attention. The analysis uses panel data for 27 origin countries covering the period from 2000 to 2024. In the first stage, a gravity model is estimated to assess how tourism flows relate to economic size and geographic distance. The results show that tourism flows tend to increase with the economic size of origin and destination countries, while greater geographical distance is associated with lower tourism flows. The estimated distance elasticity ranges from approximately −1.85 to −2.10 across model specifications, which is larger in absolute terms than the values typically reported in cross-country studies. This result is consistent with the relatively high travel cost barriers associated with Mongolia’s geographic location. These findings are consistent with the distance decay relationship commonly reported in the tourism literature. In the second stage, machine learning algorithms, including Random Forest, LightGBM, and XGBoost, are used as complementary interpretive instruments rather than forecasting tools to explore possible nonlinear relationships among the explanatory variables. To make the results more interpretable, the contribution of individual variables is examined using SHAP (Shapley Additive Explanations). The machine learning results indicate that some relationships in tourism demand may be nonlinear and not fully captured by the linear gravity specification. Specifically, distance sensitivity is approximately 6.5 times greater in nearby markets than in long-haul markets, with a structural inflexion at around 5700 km. Further analysis suggests that the influence of geographical distance is not uniform across all markets. In particular, tourism flows originating from middle-income countries appear to be more sensitive to increases in travel distance than those from higher-income countries. Overall, the findings indicate that economic size and geographical distance remain key determinants of international tourism flows to Mongolia. At the same time, the use of machine learning methods provides additional insight into potential nonlinear patterns in tourism demand. By combining econometric modelling with explainable machine learning techniques, the study offers an integrated analytical perspective for examining international tourism flows at geographically peripheral destinations where standard gravity assumptions may be insufficient. Full article

Land subsidence in the Yellow River Floodplain, approaching 60 mm/year, is severely exacerbated by annual groundwater oscillations of 3 to 8 m. Conventional hydro-mechanical models, which primarily rely on effective stress principles, often struggle to fully capture the moisture-induced structural degradation of calcareous cemented soils under such hydraulic disturbances. To address this theoretical gap, we conducted a multifactor orthogonal triaxial experiment to quantitatively decouple the macroscopic factors governing the hydro-mechanical degradation. The results reveal that moisture content acts as the absolute dominant driver, accounting for 81.65% of the variance in macroscopic shear strength variance and completely overwhelming the mechanical advantages provided by initial compaction. A generalized dual-path water-sensitive damage model was explicitly derived, mathematically uncovering a fundamental asynchronous degradation mechanism. Cohesion exhibits an inward-concave, brittle fracture trajectory, which is macroscopically inferred to be associated with the water-induced softening of calcareous bonds (phase-transition parameter 0.81, maximum allocation 75.1%). Conversely, the internal friction angle demonstrates an outward-convex, hysteretic decline (parameter 1.59), maintaining structural interlocking until severe water-film lubrication occurs. By decoupling highly state-dependent initial strength parameters from invariant degradation operators, the modified Mohr–Coulomb model achieved exceptional forward blind-prediction accuracy. Validations across distinct initial skeletal structures constrained relative prediction errors strictly between −19.3% and +13.7% without any subjective parameter recalibration. The quantified extreme vulnerability theoretically proves that minor water infiltration can instantly eradicate over 75% of cohesive strength, necessitating a paradigm shift from shallow mechanical compaction to stringent waterproofing in regional engineering practices. Full article

The implementation of stringent regulatory policies for foodborne pathogens necessitates ultra-sensitive analytical methods. Digital detection, characterized by absolute quantification and tolerance to complex matrices, serves as a robust approach for food safety monitoring. This review summarizes recent advances in digital detection for foodborne pathogens, including nucleic acid amplification-based platforms such as droplet digital PCR and digital isothermal amplification, as well as emerging preamplification-free approaches based on enzyme-mediated signal conversion, functional nanomaterials, and microfluidic devices. We also profile the applications of digital detection technologies for achieving highly specific and accurate detection of foodborne pathogens and discuss their capabilities in viable bacteria quantification, antimicrobial resistance analysis, and multiplex detection. We finally discuss emerging trends, including partition-free digital detection and artificial intelligence-assisted analysis. These advances are expected to promote the development of intelligent and data-driven food safety surveillance strategies. Full article

Identifying phase transitions in complex many-body systems traditionally necessitates the definition of specific order parameters, a task often requiring prior knowledge of the statistical model and the symmetry-breaking mechanism. In this work, we propose a framework for detecting phase transitions directly from raw (experimental) data without requiring knowledge of the underlying model Hamiltonian, parameters, or pre-defined labels. Inspired by generative modeling in machine learning, our method utilizes autoregressive networks to estimate the normalized probability distribution of the system from raw configuration data. We then quantify the intrinsic sensitivity of this learned distribution to control parameters (such as temperature) to construct a robust indicator of phase transitions. This indicator is based on the expectation of the change in absolute logarithmic probability, derived entirely from the raw data. Our approach is purely data-driven: it takes raw data across varying control parameters as input and outputs the most likely estimate of the phase transition point. To validate our approach, we conduct extensive numerical experiments on the 2D Ising model on both triangular and square lattices, and on the Sherrington–Kirkpatrick (SK) model utilizing raw data generated via Markov Chain Monte Carlo and Tensor Network methods. The results demonstrate that our generative approach accurately identifies phase transitions using only raw data. Our framework provides a general tool for exploring critical phenomena in model systems, with the potential to be extended to realistic experimental data where theoretical descriptions remain incomplete. Full article

(1) Background: Military personnel are required to perform high-intensity actions and tactical tasks under external load, which increases system weight and alters movement mechanics. Understanding how these loaded conditions influence neuromuscular performance is essential for informing physical preparation and readiness monitoring. This study quantified the effects of tactical equipment on countermovement jump (CMJ) and countermovement rebound jump (CMRJ) force–time characteristics in active military personnel and evaluated the within-session reliability of these metrics under loaded and unloaded conditions; (2) Methods: Eighteen male soldiers performed CMJ and CMRJ assessments on dual force plates (1000 Hz) under unloaded and loaded conditions (standardized tactical equipment: 10.6 ± 1.18 kg). Force–time variables were categorized as strategy (phase durations, countermovement depth), driver (mean braking and propulsive force), and outcome (jump height, jump momentum, and modified reactive strength index; mRSI) metrics; (3) Results: CMJ outcome and driver metrics demonstrated good to excellent reliability under load (ICC ≥ 0.87; CV ≤ 8.4%), whereas CMRJ outcome variables showed reduced reliability and greater variability. Loaded conditions reduced jump height and mRSI in both CMJ and CMRJ (p < 0.05), while jump momentum and absolute mean force production increased, whereas force production relative to body mass decreased. During the CMJ (slow-SSC), participants exhibited longer braking and propulsive phase durations, indicating a temporal change in movement strategy under load, whereas CMRJ (fast-SSC) force–time characteristics showed increased contact time and reduced rebound metrics; (4) Conclusions: Overall, fast stretch–shortening cycle tasks appear more sensitive to loading conditions, whereas the CMJ provides a more robust and reliable assessment for monitoring neuromuscular performance in military personnel, particularly when considering both absolute and relative force responses. Full article

This study aims to evaluate the controlling factors of tight sandstone reservoir sensitivity in the third member of the Yanchang Formation, Xunyi–Yijun area, southern Ordos Basin. Based on core samples from 12 wells, we established a partial least squares regression (PLS) model through thin section observation, SEM, XRD, high-pressure mercury injection, and sensitivity flow experiments, to quantitatively analyze the relationship between reservoir sensitivity and its controlling factors. The results show that the study area reservoirs are dominated by feldspathic sandstone with moderate compaction, characterized by low porosity (4.4–17.8%, avg. 10.93%), low permeability (0.104–2.33 mD, avg. 0.82 mD), and heterogeneous distribution of clay minerals (mainly chlorite, illite, kaolinite, and illite/smectite mixed layer). The reservoirs generally show weak to moderately weak sensitivity. The PLS model reveals that reservoir sensitivity is controlled by the coupled effects of multiple factors, with no single absolute dominant factor for any sensitivity type. Porosity is the most influential variable for overall reservoir sensitivity, followed by feldspar, illite, and illite/smectite mixed layer, and porosity exerts the strongest control on most sensitivity types via VIP score analysis. This study provides a theoretical basis for reservoir damage prevention in the study area and a technical reference for quantitative sensitivity evaluation of similar tight sandstone reservoirs. Full article

A symmetric redundant-actuated 4-PSS compliant parallel micro-motion mechanism is proposed to meet the high requirements for stiffness and motion precision in micro-nano manipulation. First, the screw theory is employed to confirm that the mechanism possesses spatial three translational (3T) degrees of freedom along the X, Y and Z axes. On this basis, the global compliance model of the mechanism is constructed by combining the compliance matrix method with coordinate transformation technology, and the kinetostatic model reflecting the mapping relationship between input force/displacement and output displacement is further derived. The finite element analysis (FEA) is used to verify the kinetostatic model, and the results show that under the predefined spiral trajectory, the maximum absolute error between the theoretical calculation and the simulation result is less than 6 × 10⁻⁷ m, which proves the high accuracy of the established model. Moreover, a comprehensive performance analysis of the 4-PSS mechanism is carried out from the perspectives of output stiffness and parasitic motion, with the traditional 3-PSS compliant parallel mechanism as the reference. The comparative results indicate that within the specified 50 μm cubic workspace, the 4-PSS mechanism achieves a 33.3% improvement in output stiffness and a 28.15% reduction in the maximum parasitic displacement compared with the 3-PSS mechanism, while maintaining excellent global stiffness isotropy (GSI). Sensitivity analysis confirms the robustness of these advantages against manufacturing variations, and the workspace-to-footprint ratio remains unchanged. This research verifies that the introduction of symmetric redundant actuation branch chains can effectively enhance the comprehensive performance of compliant parallel micro-motion mechanisms and provide engineering references for the redundant design and performance optimization of high-precision compliant parallel mechanisms in the field of micro-nano manipulation. Full article

Background: Age-related macular degeneration (AMD) is the primary cause of severe visual acuity loss in individuals over 60 with increasing prevalence. Currently, no effective treatments exist for geographic atrophy and macular scarring, highlighting the need for visual rehabilitation in these patients. Microperimetry offers functional assessment at any AMD stage and employs fixation training to help patients utilize the most effective retinal areas for vision. Methods: A prospective study involving 25 patients (50 eyes) aged 67 to 90. The MAIA II microperimeter assessed scotoma size and location, retinal sensitivity, macular integrity, fixation parameters (P1, P2, 63%BCEA, 95%BCEA), fixation stability, and preferred retinal locus. Quality of life was evaluated using the National Eye Institute Visual Function Questionnaire (NEI-VFQ-25). A subgroup with inactive AMD-related macular changes, either bilateral geographic atrophy (13 patients, 26 eyes) or bilateral scarring (12 patients, 24 eyes), was identified, all exhibiting bilateral absolute central scotomas of at least 2 degrees. Each patient completed 10 fixation training sessions with a microperimeter, training the eye with better acuity weekly. One-week post-training, a functional assessment was performed on both trained and untrained eyes. Results: Fixation training significantly improved best corrected visual acuity (BCVA) in trained eyes (mean change −0.14 logMAR, p < 0.001, large effect size) and also in fellow untrained eyes (−0.16 logMAR, p < 0.001). BNVA improved from 2.25 to 1.86 in trained eyes (p < 0.001) and from 2.96 to 2.76 in untrained eyes (p = 0.004). Fixation stability parameters improved significantly, including increases in P1 and P2 and reductions in Bivariate Contour Ellipse Area (BCEA). Quality of life measured using the NEI-VFQ-25 questionnaire improved significantly in 9 of 11 domains. Conclusions: Microperimetry may be a valuable tool for assessing visual function in AMD patients. Fixation training with the MAIA II microperimeter is both safe and effective for vision rehabilitation in those with geographic atrophy and macular scarring. Full article

Cold-atom absolute gravimeters are widely used for measuring the acceleration of gravity, yet their sensitivity is often limited by ground vibrations. Existing vibration compensation algorithms struggle to strike a balance between search accuracy and computational efficiency and are prone to local optima. Here, we propose an improved whale optimization algorithm (IWOA) to address these issues. By combining Logistic-LHS (Latin hypercube sampling) chaotic initialization, adaptive adjustment, and a Gaussian mutation operator to prevent premature convergence, IWOA achieves higher search efficiency and superior sensitivity than traditional algorithms. The method is validated through multiple simulation studies and further assessed experimentally on the NIM-AGRb-1 cold-atom gravimeter system. The results show that IWOA reduces the uncertainty of the fitted phase parameter by 66%. The Pearson correlation between atomic transition probability and the calculated phase increases to a maximum of 0.98, and the gravity sensitivity improves to 47 $\mathrm{\mu }\mathrm{Gal}/\sqrt{\mathrm{Hz}}$ when the evolution time T is 80 ms. Full article

As oil and gas development increasingly targets low and ultra-low permeability reservoirs, conventional recovery techniques often prove insufficient for mobilizing residual oil. Surfactant flooding, a key chemical enhanced oil recovery (EOR) technology, thus requires careful system optimization and mechanistic investigation. This study focuses on low-permeability reservoirs in the Changqing Oilfield, evaluating three surfactant systems—YHS-Z1 (a 7:3 mass ratio blend of hydroxypropyl sulfobetaine and cocamide), YHS-Z2 (a polyether carboxylate, a nonionic-anionic composite) and a middle-phase microemulsion system (Heavy alkylbenzene sulfonate and hydroxysulfobetaine were combined with a mass ratio of 7:3)—through a series of experiments including interfacial tension measurement, contact angle analysis, static and dynamic oil displacement tests, as well as emulsion transport/retention index assessments, to comprehensively characterize their oil displacement properties. Based on the experimental data, this study constructed four classical regression models: Ridge Regression, Random Forest (RF), Gradient Boosting Regression (GBR), and Support Vector Regression (SVR), and conducted a comparative analysis of their predictive performance. The results demonstrate that the Random Forest (RF) model achieved the optimal prediction performance, with a Mean Absolute Error (MAE) of 1.8245, a Mean Absolute Percentage Error (MAPE) of 4.78%, and a coefficient of determination (R²) of 0.9428 on the training set. Further analysis using the SHapley Additive exPlanations (SHAP) algorithm revealed that the retention index is the primary global factor (accounting for 49.79% of the variance), while significant intergroup differences exist in the primary factors across different surfactant systems. Concurrently, single-factor and multi-factor sensitivity analyses were conducted to elucidate synergistic effects and threshold behaviors among parameters. The optimal parameter combination, identified via a random search method, achieved a predicted recovery factor of 45.61%, representing a 6.57% improvement over the highest experimental value. This study demonstrates that machine learning methods can effectively identify the dominant factors in oil displacement and enable synergistic parameter optimization, thereby providing a theoretical foundation for the efficient development of surfactant flooding in low-permeability reservoirs. Full article

Accurate absolute pressure measurement is of great importance in industrial control, environmental monitoring, and aerospace. Traditional fiber-optic Fabry–Perot (F-P) pressure sensors usually involve complex microfabrication and high-cost demodulation systems, while conventional diaphragm capsule sensors are limited in sensitivity and resolution. This work presents a low-cost, high-resolution fiber-optic F-P absolute pressure sensor. The sensor uses a vacuum capsule as one reflective surface and a partially reflective fiber collimator as the other, forming a low-finesse F-P interferometer. The cavity length is linearly modulated by the elastic deformation of the capsule under pressure, and high-precision demodulation is realized using frequency modulated continuous wave (FMCW) interferometry instead of conventional spectral methods. Static experiments from 10 to 110 kPa show that the sensor exhibits a high sensitivity of 15,105 nm/kPa and a resolution of 3.3 Pa. Furthermore, the sensor operates normally within the range of −20 °C to 70 °C, exhibiting a pressure–temperature cross-sensitivity of 0.081 kPa/°C and a cavity length drift of 496 nm/h. With the advantages of high performance, simple structure, low cost, and good scalability by selecting different capsules, the proposed sensor has promising potential for practical applications in pressure measurement fields. Full article

Background: The clinical intra-anesthetic changes of perioperative hypersensitivity (POH) are not specific and require a thorough differential diagnosis with other mimicking conditions. Biomarkers such as tryptase and histamine provide supportive evidence for POH. From the suggested cutoffs, a common decision threshold has not been validated for use in daily practice. The aim of this systematic review and meta-analysis is to identify biomarkers investigated for POH and to evaluate their diagnostic performance. Methods: This meta-analysis included original diagnostic accuracy studies comparing patients with clinically suspected POH and controls with no signs of intraoperative hypersensitivity reactions, in whom allergy biomarkers were evaluated, aiming to investigate diagnostic performance of the assays. Data was pooled to evaluate sensitivity and specificity. Results: In seven studies on tryptase and three studies on histamine dosing for the diagnosis of POH/POA, different fixed or dynamic thresholds for positivity were proposed. For tryptase, fixed thresholds had 59.8% sensitivity and 95.2% specificity for an optimal cutoff of 12.68 ng/mL, while dynamic thresholds yielded 77.2% sensitivity and 88.5% specificity. For histamine, fixed cutoffs presented 78% sensitivity and 85% specificity, while dynamic thresholds investigated in a single study yielded 78.2% sensitivity and 91.1% specificity. Estimates for histamine are unreliable due to limited data. Conclusions: From published data, tryptase is clearly the most robust biomarker, dynamic thresholds boost sensitivity without major specificity loss and confirm the added diagnostic value of relative changes over fixed absolute cutoffs. Preliminary results suggest that histamine might have optimal diagnostic performance, but estimates are severely limited by small sample sizes. Full article

To address the challenge of detecting internal defects in medium-thick titanium alloy laser welds, a combined simulation and experimental study on ultrasonic testing was conducted. A finite element model employing a 5 MHz shear wave angle transducer for inspecting titanium alloy welds was established. An ultrasonic testing system was developed, incorporating a DPR300 pulser-receiver (JSR Ultrasonics, Pittsford, NY, USA) and an MSO5204 oscilloscope (RIGOL, Suzhou, China), and was calibrated using standard reference blocks. The inspection results for four prefabricated internal defects at various depths demonstrated that all defects were effectively detected, with the minimum detectable equivalent defect size reaching 1 mm. The measured signal-to-noise ratio (SNR) averaged 17.6 dB, validating the high sensitivity of the proposed system. The mean absolute error for defect localization was 0.438 mm, achieving a positioning accuracy better than 0.5 mm. This study indicates that the pro-posed method enables effective detection and accurate localization of internal defects in titanium alloy laser welds, providing critical technical support for laser welding quality assessment. Full article

Offshore pipeline systems associated with floating platforms operate under complex environmental and operational conditions that significantly influence their structural integrity and inspection requirements. Limited accessibility, harsh marine environments, and time-dependent degradation mechanisms require inspection planning to be supported by structured decision-making frameworks capable of explicitly accounting for both degradation processes and failure consequences. In this study, a Risk-Based Inspection (RBI)-driven integrity assessment is applied to three carbon steel pipeline systems associated with a SPAR offshore platform. The analysis integrates system description, identification of dominant damage mechanisms, and RBI quantification to evaluate probability of failure and consequence-related risk under offshore operating conditions. Internal corrosion is identified as the dominant long-term degradation mechanism for all analyzed pipelines, while external corrosion governs short-term inspection interval definition due to its higher growth rate and sensitivity to insulation characteristics and environmental exposure. Although all pipelines are classified within the same overall qualitative risk category, significant differences in failure probability, risk intensity, and consequence-driven risk behavior are observed, reflecting variations in system configuration, insulation systems, length, and functional role within the offshore production infrastructure. To enable meaningful comparison between pipeline systems of significantly different total lengths, normalized risk indicators per unit length are introduced. These indicators provide additional insight into local risk intensity and spatial risk distribution that are not evident from absolute risk values alone. The results highlight the importance of treating risk as a dynamic quantity rather than a static classification and demonstrate that RBI-based assessment supported by normalized risk metrics can enhance inspection prioritization and maintenance decision-making for SPAR-associated offshore pipeline systems. Full article

Vibration-assisted water flooding (VA-WF) can improve sweep efficiency. However, unclear macro-scale mechanisms limit its wider adoption in heavy oil reservoirs. This study combines previous sandpack experiments with two-dimensional Volume-of-Fluid (VOF) simulations to show how vibrations reshape permeability fields and, in turn, pressure and production behaviour. Heavy oil sandpacks were water-flooded under conditions of no vibration and 2 Hz and 5 Hz axial excitation. Measured injection pressure histories and oil production were used to calibrate a VOF model in which absolute permeability follows a log-normal distribution with directional anisotropy. Only when axial and radial permeabilities were assigned a negative local correlation did the model reproduce key observations: secondary pressure spikes, irregular viscous-fingering morphologies, delayed production drops, and variability in cumulative recovery. Parameter sweeps quantify the sensitivity of VA-WF performance to the variance and correlation of the permeability field, and multiple runs estimate the variability in outcomes introduced by stochastic heterogeneity. This study proposes a transferable workflow—comprising sample testing, parameter inference, and probabilistic simulation—to screen excitation conditions and forecast VA-WF performance prior to field implementation, enabling operators to optimize vibration frequency based on reservoir-specific permeability characteristics and to anticipate production variability under uncertainty. These results highlight the dominant factors affecting swept volume and oil recovery, supporting data-driven decision making in VA-WF projects. Full article