Search Results (9,907)

Pearlitic wheel steel subjected to thermal–mechanical fatigue (TMF) during braking can undergo catastrophic fracture. This study clarifies the microstructural evolution governing the macroscopic cyclic hardening/softening behavior of pearlitic wheel steel under thermal–mechanical fatigue (TMF) with a constant mechanical strain range of −0.4% to +0.2%. At lower temperature amplitudes (200–500 °C), the geometrically necessary dislocation (GND) density reaches 20.4 × 10¹⁴/m² during initial cycles, corresponding to cyclic hardening due to dislocation pile-ups at cementite lamellae interfaces. With increasing cycles, the GND density decreases to 12.3 × 10¹⁴/m², concurrent with softening arising from lamellar bending/fracture, partial spheroidization, and dynamic recrystallization of ferrite. At higher temperature amplitudes (200–730 °C), the GND density decreases from 8.8 × 10¹⁴/m² to 3.5 × 10¹⁴/m², reflecting sustained cyclic softening dominated by thermally activated mechanisms, including cementite spheroidization and dislocation annihilation. The resulting softened microstructure consists of ferrite grains, intragranular dispersed cementite, and chain-like coarse cementite at boundaries. Unlike previous studies that focused on single loading conditions (e.g., thermal fatigue, rolling contact fatigue, or wear), the present work addresses the more complex TMF scenario and quantitatively elucidates the interplay between mechanical response and microstructural evolution in pearlitic steel. This work provides theoretical guidance for the development of a fatigue life prediction model for pearlitic wheels under braking. Full article

Background: price dynamics in financial markets exhibit scale-invariant volatility, quantized liquidity and collective behaviour that resist single-paradigm models; Multiscale Relativistic Quantum Finance (MRQF) reconciles these facets on an energy–entropy $(E,S)$ plane, but its translation into a deployable decision system has remained open. Methods: we propose MRQF-MAS, a cooperative multi-agent system (MAS) in which institutional, commercial and retail operators become first-class agents, each decomposed into signal, energy, entropy, risk and execution sub-agents that share beliefs through a horizontal cooperation layer and a shared knowledge base (SKB) of $(E,S)$ trajectories. The framework is benchmarked as a high-volatility regime classifier on 6978 daily EUR/USD reference rates published by the European Central Bank (ECB) over 1999–2026 against four baselines including Generalized Autoregressive Conditional Heteroscedasticity (GARCH)(1,1). Results: on the full official ECB EUR/USD series, MRQF-MAS attains 83.0% accuracy, precision 0.552 and Matthews correlation coefficient (MCC) 0.479 with 95% bootstrap CI [0.46, 0.51] and a one-day median detection latency, improving slightly on a rolling-volatility baseline while remaining below a GARCH(1,1) reference. Conclusions: MRQF-MAS delivers a structurally interpretable, agent-traceable regime decomposition complementary to scalar volatility estimators. Full article

We re-conceptualize corporate debt default risk (EDF) as an emergent state variable of a coupled financial system and ask how capital-market opening reshapes its equilibrium. Extending the structural credit-risk framework with three interacting subsystem channels—external financing, investment efficiency, and information disclosure—we derive a closed-form result showing that an exogenous increase in liberalization strictly reduces the system-level corporate debt default probability through three complementary channels. We then exploit the staggered roll-out of China’s Shanghai–Hong Kong and Shenzhen–Hong Kong Stock Connect (HSGT) programs as a quasi-natural experiment on a panel of 21,351 firm-year observations over 2011–2023. A difference-in-differences (DID) estimator confirms a significant stabilizing effect on the firm’s market-implied default probability that is robust to an extensive battery of identification and specification checks; mechanism regressions confirm all three model-implied channels. The stabilizing effect is further amplified in firms facing greater environmental uncertainty and greater customer concentration—precisely the regimes in which our model predicts the underlying subsystem coupling to be most fragile. Our findings recast capital-market opening as a system-level intervention that simultaneously re-balances financing, investment, and information subsystems of the financial system, with implications for financial-stability policy in emerging economies. Full article

This study examines the operational resilience of Limassol Port, Cyprus’s primary maritime hub, amid disruptions caused mainly by the COVID-19 pandemic. Utilizing high-resolution port-level data from 2018 to 2025, we evaluate performance across five key performance indicators: port calls, anchorage utilization, berth utilization, waiting times, and arrival punctuality. The analysis adopts a longitudinal approach, spanning pre-pandemic, peak-pandemic, post-pandemic, and recent phases, while differentiating impacts across vessel categories. Unlike many regional ports, Limassol’s cruise sector exhibited unique counter-cyclical growth, with calls doubling during the pandemic as the port transitioned into a strategic safe haven and repositioning base. This surge normalized over the 2024–2025 period as temporary operational disruptions resolved. Conversely, container and Ro-Ro (roll-on/roll-off) segments demonstrated robust stability, achieving rapid post-pandemic normalization, while bulk and tanker operations exhibited higher volatility linked to shifting commodity demands. These findings, validated through one-way analysis of variance (ANOVA) and Cohen’s d effect sizes, underscore the adaptive capacity of mid-sized Mediterranean hubs. The study concludes that operational flexibility, coupled with enhanced digital coordination and strategic capacity planning, is essential for maintaining the resilience of short sea shipping networks during global crises. Full article

Wildfires in plateau and mountainous regions are increasingly destructive, often disrupting ground transportation networks and severely constraining emergency logistics, while unmanned aerial vehicles (UAVs) remain limited by payload capacity. To address this challenge, this study proposes an air–ground collaborative emergency material dispatch method for dynamic, time-varying wildfire environments. A multi-layer spatiotemporal network model is developed by incorporating key uncertainties, including fire spread and meteorological fluctuations, into dynamic parameters, and a multi-objective mixed-integer programming framework is established to jointly optimize emergency response time, total dispatch cost, and rescue fairness. To solve the resulting high-dimensional dynamic rescheduling problem, a Fast Ant Colony Optimization-Genetic Algorithm (FACO-GA) integrated with a rolling horizon mechanism is designed. Simulation results under Level 1–10 dynamic perturbations show that, compared with conventional standalone algorithms (GA and ACO), the proposed method demonstrates markedly better robustness and computational efficiency, reducing the extreme average rescheduling response time to 6.80 s, while maintaining a Hypervolume (Hv) retention rate of 96.30% and limiting the Spacing (Sp) change rate to 4.15%. These findings indicate that the proposed approach can effectively overcome computational bottlenecks and provide an adaptive decision-support framework for emergency logistics dispatch in complex wildfire scenarios. Furthermore, comprehensive ablation studies and sensitivity analyses validate the structural necessity of the rolling horizon and ACO modules, ensuring the algorithm’s parameter robustness under extreme stochastic perturbations. Full article

The performance of haul trucks in open-pit mining is strongly affected by haul-road geometry, surface condition, rolling resistance, and operational traffic regimes. However, existing studies often consider road-surface mapping, vehicle dynamic response, and onboard telemetry as separate information streams, which limits the reproducible assessment of how road-related factors are associated with VIMS-derived fuel-use proxy burden, mechanical dynamic response, and transport-cycle instability. This study proposes a field-based, segment-level multisensor framework that integrates unmanned aerial vehicle/light detection and ranging (UAV/LiDAR) road-surface reconstruction, global positioning system/inertial measurement unit (GPS/IMU) trajectory and vibration data, and Caterpillar Vial Information Management System (VIMS) telemetry into a unified spatiotemporal analytical dataset. The methodological contribution consists in the synchronization of heterogeneous data sources at the road-segment level, the calculation of interpretable road-condition and vehicle-response indicators, and the statistical assessment of road-related effects while explicitly accounting for confounding factors such as longitudinal grade, payload state, speed regime, truck class, and operational variability. Unlike studies that use LiDAR mapping, vibration monitoring, or onboard telemetry as separate diagnostic channels, the proposed approach introduces a segment-level analytical framework in which road morphology, truck response, and operational penalties are aligned within the same spatial unit, interpreted under confounder-aware conditions, and verified through repeat-pass reproducibility and robustness checks. The framework was tested on haul roads around the Ekibastuz open-pit coal mine. The field analysis identifies road segments where degraded surface morphology, increased waviness, unfavorable longitudinal profile, and higher rolling resistance coincide with increased mechanical dynamic response, VIMS-derived fuel-use proxy burden, braking instability, and travel-time variability. The results are interpreted as controlled field-supported associations rather than as isolated causal effects. The proposed maintenance ranking should therefore be regarded as a decision-support output, while the operational effectiveness of specific repair interventions requires future before–after validation. Full article

We introduce the local kernel-based calculus, a unifying framework for local differential and integral operators based on an arbitrary positive continuous kernel function. This framework encompasses conformable, non-conformable, and our newly introduced local Euler-kernel derivatives as special cases. The parameter of the kernel is unrestricted and may take negative values, reflecting its role as a genuine parameter rather than an order of fractional differentiation. Within this general setting, we rigorously prove a complete set of foundational theorems: linearity, the product rule, continuity, Rolle’s theorem, the mean value theorem, and the fundamental theorem of calculus via the associated integral operator. We also derive a new formulation of the chain rule that expresses the chain rule entirely in terms of the kernel-based derivatives. While algebraically equivalent to the classical form, this representation preserves the intuitive structure of the chain rule without reference to the classical derivative. We further establish the Fundamental Theorem of Local Euler Calculus and its generalization, the Fundamental Theorem of Local Kernel-Based Calculus, confirming that the derivative and integral operators are genuine inverses, with the classical fundamental theorem recovered as special cases when the kernel reduces to unity. As an important illustration, we develop the local Euler calculus with the exponential kernel in full detail, providing explicit derivative and integral formulas for elementary functions. This special case demonstrates the simplicity and power of the functional approach. Overall, the local kernel-based calculus provides a solid, self-contained foundation that unifies a wide class of local operators and extends far beyond the traditional setting. Full article

Ultrathin titanium alloy foils are attractive for engineering components requiring flexural compliance and mechanical support, yet their recoverable deformation behavior at the foil scale remains insufficiently characterized. This study evaluates 30 μm Ti–24Nb–4Zr–8Sn (wt.%, Ti2448) foils in the as-rolled and solution-treated states and compares their tensile loading–unloading response with same-thickness CP Ti and Ti–6Al–4V reference foils. The Ti2448 foils exhibit a larger recoverable-deformation window and a lower apparent loading modulus than the reference foils under the same testing protocol. The highest recoverable strain is obtained in the solution-treated longitudinal condition, indicating that the recoverable deformation is sensitive to both processing state and loading direction. These results suggest Ti2448 foils as potential candidates for flexure-related applications requiring large recoverable deformation. Full article

This study aims to investigate the effects of heterogeneous microstructure and strain rate on the microstructural evolution and mechanical properties of ultrafine-grained (UFG) austenitic stainless steel during elevated-temperature tension. In this research, 17Cr-10Ni austenitic stainless steel was rolled to a 60% reduction in thickness at room temperature and 200 °C, followed by annealing at 1000 °C and 500 °C, respectively. The microstructural evolution of the annealed samples and high-temperature tensile specimens was characterized using optical microscopy, transmission electron microscopy, scanning electron microscopy equipped with electron backscatter diffraction, and X-ray diffraction. Results show that at room temperature, the heterogeneous twinned UFG (TW-UFG) sample, influenced by hetero-deformation-induced stress strengthening, maintains good ductility while exhibiting higher strength than the uniform UFG sample. During tensile deformation at 600 °C, grain refinement still contributes to strengthening, and the dominant deformation mechanism in the uniform UFG sample is dislocation dynamic recovery, whereas in the TW-UFG sample is detwinning combined with dynamic dislocation recovery. At low strain rates (10⁻⁴ s⁻¹), sufficient dynamic recovery and detwinning in the TW-UFG sample delay plastic instability and improve elongation. Full article

Background/Objectives: Topical imiquimod is an established non-invasive treatment for superficial basal cell carcinoma (sBCC). However, data on treatment-associated changes in tumor microvascularization remain limited. This study investigated vascular changes before and after imiquimod therapy using multimodal non-invasive imaging. Methods: In this single-center, prospective observational study, 31 basal cell carcinomas in 20 patients were examined before and 12–16 weeks after topical imiquimod therapy (5%, five times weekly for six weeks) using dermoscopy, dynamic optical coherence tomography (D-OCT), and line-field confocal optical coherence tomography (LC-OCT). Analyses were performed as paired before-and-after comparisons. While approved for sBCC, a small number of thin nodular and infiltrative BCCs were included exploratorily; subgroup analyses were not powered. Results: Dermoscopy showed a nominally significant shift toward smaller vessel diameter categories after therapy (ATS = 8.183, df = 1, p = 0.004). D-OCT-derived parameters (vessel density, vessel diameter, and depth of the vascular plexus) did not show nominally significant changes. LC-OCT showed nominally lower apparent intratumoral flow scores (ATS = 13.285, df = 1, p < 0.001), reduced occurrence of vessel-wall-associated intraluminal structures showing a rolling-like motion pattern (86.7% before treatment versus 33.3% after treatment; ATS = 13.357; df = 1, p < 0.001), and a reduction in maximum vessel diameter (ATS = 6.110, df = 1, p = 0.013). The primary LC-OCT inferential analyses were performed at the lesion level without adjustment for within-patient clustering and should therefore be interpreted as exploratory. An additional patient-cluster-adjusted paired change-score sensitivity analysis for LC-OCT maximum vessel diameter yielded a directionally consistent estimate (−17.81 µm; 95% CI: −34.40 to −1.23; p = 0.037). The primary exploratory endpoints were LC-OCT–based apparent intratumoral flow and maximum vessel diameter; secondary endpoints included dermoscopic and D-OCT–based vascular parameters. In the exploratory response-stratified analysis, the change in LC-OCT-based maximum vessel diameter did not differ significantly among the assigned response groups (Kruskal–Wallis H = 3.870, df = 2, raw p = 0.144; BH-adjusted p = 0.753). Conclusions: LC-OCT detected several exploratory vascular changes between the pre-treatment examination and follow-up and may provide complementary information for the non-invasive assessment of BCC after imiquimod therapy. Given the exploratory design, limited sample size, and lack of systematic histological confirmation, these findings are hypothesis-generating and require validation in larger prospective studies. Full article

Sodium (Na) metal anodes suffer from poor processability, severe volume fluctuation, unstable interfacial chemistry, and uncontrolled dendrite growth during cycling, which significantly hinder their practical application. Herein, a Na/Na₂In composite foil is fabricated through an in situ spontaneous alloying reaction enabled by a simple rolling–folding process using Na and indium (In) foils as precursors. Structural characterizations confirm the complete conversion of metallic In into the Na₂In alloy phase, forming a continuous architecture with uniformly distributed Na₂In networks embedded within the Na matrix. Owing to the sodiophilic and mechanically robust Na₂In framework, the Na/Na₂In composite anode effectively regulates Na plating/stripping behavior and suppresses dendritic growth, thereby maintaining a dense and stable electrode morphology during repeated charge/discharge processes. As a result, the Na/Na₂In symmetric cell exhibits stable cycling for over 900 h at 0.5 mA cm⁻² and 1 mAh cm⁻² with low polarization hysteresis, whereas the pure Na counterpart fails after only 143 h. Moreover, full cells paired with NaFe_1/3Ni_1/3Mn_1/3O₂ cathodes deliver enhanced cycling stability, retaining 87% of the initial capacity after 100 cycles at 0.5 C, together with improved rate capability. This work demonstrates a scalable mechanical fabrication strategy for high-stability Na metal composite anodes and provides new insights into the practical development of high-energy-density Na metal batteries. Full article

Effective identification of corporate default risk is crucial for maintaining financial stability and safeguarding investors’ interests. Existing models remain limited in addressing class imbalance and the dynamic evolution of default-related features over time. To overcome these challenges, we propose an adaptive spherical neighborhood resampling and class-specific reliability evidential reasoning model (ASNR-crER). By combining feature-weighted minority sample reconstruction with reliability-guided recursive evidence fusion, the proposed model aims to improve the prediction accuracy of both default and non-default firms under class imbalance. This study uses Chinese listed small enterprises from 2000 to 2023 as the research sample, comprising 10,449 firm-year observations from 2182 firms. By matching default status in year t with firm indicators from t-0 to t-5, six rolling prediction windows are constructed. The empirical results show that: (1) Compared with mainstream benchmark methods, ASNR-crER achieves the best overall performance in terms of accuracy, AUC, and F1 across all prediction windows, indicating that it can more reliably identify high-risk default firms while maintaining strong recognition of non-default firms. (2) SHAP analysis indicates that financial, non-financial, and macroeconomic indicators exert time-varying effects on corporate default risk. Financial indicators, including “Retained earnings/total assets”, “Other receivables/current assets”, and “Annualized return on assets”, reflect internal capital accumulation and profitability, serving as key predictors of default risk. Non-financial indicators, such as “Top 10 Tradable Shares H-index” and “Top 10 shareholders H-index”, can provide supplementary signals for medium-term risk identification. Macroeconomic indicators, including “M2 YoY growth rate”, “Urban HH per capita income”, and “Benchmark short-term loan rate”, show stronger explanatory power in longer prediction windows. Therefore, this study provides an effective early-warning tool for financial institutions and relevant stakeholders to identify high-risk firms, and enriches empirical evidence on the time-varying drivers of corporate default risk. Full article

Limited data exists on the combined nutritional and phytochemical profiles of UK commercially available plant-based foods, limiting comprehensive compositional data available for dietary assessment and food formulation. This study addresses this gap by providing thorough compositional analysis of quinoa (red, black, organic), chia seeds (organic, white), pumpkin seeds (conventional, organic), flaxseeds (brown, golden, organic), and triticale grain (organic, cereal meal, rolled), profiling macronutrients, dietary fiber, amino acids, fatty acids, essential minerals, and bioactive phytochemicals. Pumpkin seeds exhibited the highest protein (29–36%) and fat (42–46%) contents, markedly exceeding quinoa and triticale, highlighting their role as a plant-based protein and energy source. Flaxseeds and chia seeds provided the greatest dietary fiber (15 g/100 g), while mineral analysis identified pumpkin seeds as particularly rich in phosphorus and magnesium, and white chia seeds as a rich source of calcium and iron. Targeted LC-MS/MS and HPLC screening (171 molecules) revealed substantial variation in phytochemical composition among products with red quinoa, golden flaxseed, and white chia seed containing the highest concentrations of quantified phytochemicals (up to 97.2 mg/100 g). These findings provide integrated data on the nutrient and phytochemical composition of selected commercially available products, reinforcing the practical importance of crop diversity for enhancing dietary nutrient and phytochemical diversity and informing future research, food innovation, and dietary assessment initiatives involving plant-based foods. Full article

Reducing emissions is increasingly critical for mitigating the environmental impact of the iron and steel industry. Achieving this transition requires an accurate evaluation of carbon emission intensity for steel production, which relies on an in-depth analysis of carbon metabolism mechanisms across the entire steel production chain. Existing approaches predominantly focus on carbon tracing within material flows, which cannot deeply integrate carbon migration pathways with energy flows and thus fail to reveal the actual sources and transmission mechanisms of carbon emissions. To address this gap, this study develops a carbon metabolism simulation model of the steel manufacturing process that considers the coupling of material production with the energy network. The differentiated carbon metabolism patterns are characterized in terms of carbon fixation, migration, and dissipation to support more accurate carbon emission accounting and enable the formulation of targeted decarbonization strategies. The results show that the coking process fixes 72.51% of its carbon input. The sintering and pelletizing process shows typical carbon dissipation characteristics, with nearly 100% of input carbon discharged. Carbon emissions from steelmaking and the rolling process are mainly induced by indirect energy consumption. The total carbon dissipation of integrated steel production system is 440.62 kg-C/t-CS, with the ironmaking process contributing the largest share of 33.92%. Full article

Stable interactions among electricity, carbon allowance, and fossil fuel markets are essential for sustainable energy transition, because excessive cross-market risk transmission may affect energy affordability, carbon-price credibility, and low-carbon investment signals. This study provides comparative evidence on dynamic connectedness, tail-state shock responses, and return-based complexity in representative Chinese and European benchmark markets. Using daily market data from the Wind database for November 2021–January 2026, the empirical framework combines time-varying parameter vector autoregression (TVP-VAR), quantile vector autoregression and quantile impulse response functions (QVAR/QIRFs), and rolling multifractal detrended fluctuation analysis (MFDFA). The results show that the European benchmark system has a higher absolute connectedness level than the Chinese benchmark system: the full-sample mean total connectedness index (TCI) is 18.75 in Europe and 5.63 in China, while the crisis-period mean TCIs are 25.19 and 12.12, respectively. Post-peak adjustment depends on the reversion metric used: China shows a faster initial half-life decline from the crisis peak, whereas reversion to lower region-specific connectedness thresholds depends on the selected benchmark. Natural-gas-shock QIRFs indicate stronger upper-tail persistence in Europe, whereas China is characterized mainly by short-run directional divergence; supplementary coal-, oil-, and carbon-shock checks show that response patterns are shock-source-dependent. Electricity-return multifractal spectrum width (MFW) does not show stable full-sample explanatory power for TCI, but it provides stage-dependent auxiliary diagnostic information. These findings provide a comparative diagnostic framework for monitoring cross-market systemic risk and supporting sustainability-oriented energy-market governance under low-carbon transition. Full article