Search Results (8,982)

Objectives: This multicenter cross-sectional study investigated the relationship between age and organ-specific iron burden and characterized multi-organ iron phenotypes across different age groups in adults with non-transfusion-dependent thalassemia (NTDT). Methods: We retrospectively evaluated 96 adults with NTDT enrolled in the multicenter Italian Extension-Myocardial Iron Overload in Thalassemia network. Hepatic, pancreatic, and cardiac iron burden were assessed by quantitative magnetic resonance imaging (MRI). Results: The median age was 41.64 (36.08–53.92) years. Hepatic iron overload (IO) was present in 59.4% of patients and pancreatic IO in 29.2%, whereas no patient exhibited myocardial IO. Age was not associated with liver iron concentration (LIC) or cardiac R2*. A weak positive correlation was observed between age and pancreatic R2* (R = 0.215, p = 0.035), although this association was no longer significant after the exclusion of one extreme value. LIC and pancreatic R2* were not correlated. Across age groups (<40, 40–59, and ≥60 years), the prevalence of pancreatic IO increased (22.9%, 28.6%, and 50.0%, respectively), whereas hepatic IO remained stable. Isolated hepatic IO was the most common phenotype overall, while combined hepatic and pancreatic IO was more frequent among patients aged ≥60 years. Conclusions: In adults with NTDT, age was not associated with greater hepatic or cardiac iron burden, whereas older patients showed a higher prevalence of pancreatic iron accumulation and combined hepatic–pancreatic iron involvement. These findings highlight the organ-specific heterogeneity of iron distribution in NTDT and support the role of multi-organ MRI assessment beyond LIC evaluation, particularly in older patients. Full article

Vertical-axis wind turbines (VAWTs) could be a good alternative to horizontal axis turbines for offshore applications. However, the variability of the aerodynamic forces with the blade azimuthal position subjects the blade to fatigue stresses and generates typical power periodicity. Extreme wind conditions, such as gusts, could aggravate the aerodynamic load peaks, compromising the system durability. We analyze the transient aerodynamic loads generated under gusty winds for a 5 MW offshore troposkein-shaped VAWT. The behavior of the turbine at its equatorial plane is simulated by 2D-CFD assuming gusts characterized by different periods and amplitudes. Since the turbine is stall-regulated, a range of wind velocities going from the maximum efficiency velocity to the nominal power velocity are considered. The instantaneous overloads of power, blade radial force, and total force on the shaft are analyzed. An increase in the peak values up to 119% (power) and 51% (force on the shaft) are predicted relative to the most severe steady condition, depending on gust characteristics and the blade position when the gust crest occurs. These results refer to the equatorial plane, rather than the entire turbine. It is demonstrated how the passive stall load regulation method protects the turbine from excessive overloads. Full article

Lake Nasser represents Egypt’s principal strategic freshwater reservoir; however, its long-term phytoplankton dynamics at the basin scale remain insufficiently characterized. This study presents a detailed spatiotemporal investigation of chlorophyll-a (Chl-a) variability across the reservoir during the 2002–2020 period using exclusively MODIS-Aqua Level-3 satellite observations. As no concurrent in situ measurements were available for validation, the reported values are treated as satellite-derived Chl-a estimates rather than direct observations of phytoplankton biomass. A non-parametric statistical approach, incorporating the Mann–Kendall test, Theil–Sen slope estimator, coefficient of variation (CV), and relative anomaly analysis, was used to characterize pixel-level climatology and temporal trends. The findings demonstrate that the reservoir exhibits pronounced spatial heterogeneity and a well-defined bimodal seasonal pattern associated with the annual Blue Nile flood pulse during August–October and winter convective mixing processes between November and February. Although annual-scale analysis did not indicate a statistically significant monotonic trend, seasonal investigations revealed strongly contrasting temporal responses throughout the year. At the pixel level, significant increases dominated ten of the twelve calendar months, from September through June, with median slopes among significant pixels of +0.36 to +1.47 mg m⁻³ yr⁻¹. July and August reversed this pattern, combining significant declines along the northern and central main channel with the steepest increases in the record at the extreme southern inflow. The directional contrast was unaffected by control of the false discovery rate within each calendar month (q = 0.05), which retained 66% of the significant pixels. Summer conditions were additionally marked by substantial temporal variability (CV > 60%) and pronounced positive anomalies, reaching +274.51% during August. Notably, these anomalies were spatially concentrated within the central and southern sectors of the reservoir, rather than within the conventionally productive northern region. This spatial redistribution, together with the broad enrichment tendency indicated by the pixel-level trends, points to an intensification of the summer maximum within the southern inflow zone and to a possible emergence of localized eutrophication risk, a hypothesis that requires confirmation through concurrent nutrient, transparency, and dissolved-oxygen observations. Overall, the study provides valuable geospatial insights for the environmental management of Lake Nasser, emphasizing the importance of implementing targeted early-warning systems for harmful algal bloom monitoring. The results also demonstrate the critical role of satellite-based Earth observation technologies in supporting adaptive water resource management under evolving climatic and hydrological conditions. Full article

With the continuous decline in easily recoverable reserves of conventional oil and gas, the focus of oil and gas exploration and development has gradually shifted toward complex reservoirs, such as low-permeability, tight, high-temperature and high-pressure (HTHP), and strongly heterogeneous reservoirs. Such reservoirs are characterized by complex pore–throat structures, poor seepage capacity, extreme temperature and pressure conditions, and strong heterogeneity. Traditional acidizing technologies face multiple challenges, including short effective penetration distances, system instability at high temperatures, uneven stimulation, and a tendency to induce secondary formation damage, making it difficult to meet the requirements for highly efficient reservoir stimulation. This paper systematically analyzes the petrophysical properties and acidizing challenges of four typical types of complex reservoirs. It reviews the reaction mechanisms, research and development progress, and reservoir adaptability principles of six acid systems, and elaborates on the application value of molecular simulation technology in acid–rock reaction analysis, formulation optimization, and injection regulation. The review indicates that, under extreme operating conditions, existing technologies still suffer from inadequate system stability, limited deep mass transfer, and environmental concerns. Future research should deepen the understanding of multi-scale acid–rock reaction mechanisms, develop composite acid systems tolerant to extreme conditions, and drive the development of acidizing technologies toward refinement and intelligentization. Full article

Fimbristylis ochotensis (Meinsh.) Kom. is a rare and vulnerable plant species of significant scientific and ecological importance. It is an obligate thermophyte that grows exclusively around hot springs and geothermal fields on the Kamchatka Peninsula and in northern Japan. For the first time, the elemental composition of Fimbristylis ochotensis and its associated soils was determined using neutron activation analysis, supplemented with ICP-OES and a direct mercury analyzer, on samples collected from three hot spring zones on the Kamchatka Peninsula. In total, 43 elements were quantified in soil samples and 31 elements in plant samples. Both soil and plant material collected near different hot springs exhibited significant variation in elemental composition. Mass fractions of most elements were higher in soil samples, with the exceptions of Zn, Br, Rb, Cs, and Cd. Enrichment factor values indicated moderate to extreme soil enrichment in As, Br, Cd, Sb, and W, while transfer factors greater than unity were obtained for K, Rb, Cs, Br, Zn, S, and Cd, suggesting efficient accumulation of these elements by the plant from the soil. Full article

The Standardised Precipitation-Evapotranspiration Index (SPEI) is widely used for drought monitoring, but reanalysis datasets like ERA5-Land contain biases that affect its reliability. Conventional bias correction, which adjusts variables independently, can violate the physical relationship between precipitation and PET. We introduce a Physics-Guided Neural Network (PGNN) that corrects both variables simultaneously while enforcing a water-balance constraint within the loss function. Using a rigorous temporal split (training 1950–2010, validation 2011–2018, testing 2019–2025), the PGNN framework shows improved performance compared with quantile distribution mapping (QDM). On the validation period (2011–2018), the correction reduces precipitation RMSE by 32% (from 40.4 to 27.6 mm month⁻¹) and increases PET R² from −2.820 to 0.890, compared with QDM’s 0.326. For the 3-month agricultural drought index, PGNN reduces RMSE by 27% (from 0.440 to 0.320), increases R² from 0.737 to 0.857, and improves correlation from 0.890 to 0.943, whereas QDM shows only marginal improvement (R² = 0.715). Consistent gains are observed across all timescales, with PGNN-corrected SPEI achieving R² exceeding 0.78 for the 1-, 3-, and 6-month agricultural indices. At longer timescales, PGNN shows improved skill, achieving R² = 0.101 and 0.113 for 12- and 24-month SPEI, respectively—outperforming QDM (R² = 0.043 and 0.032). The period-specific analysis reveals two distinct degradation mechanisms: error accumulation in the water balance explains the decline from short to long timescales, while climate non-stationarity explains the degradation from the validation period to the test period. In the independent test period (2019–2025), both methods show substantial degradation: PGNN precipitation RMSE increases from 27.6 to 32.0 mm month⁻¹ (+16%), PET R² drops from 0.890 to 0.458 (−49%), and longer timescales (9–24 months) yield negative R² values. Categorical metrics for extreme drought detection show mixed results on the validation period: the Critical Success Index (CSI) for Extreme Dry conditions (3-month SPEI) decreases from 0.269 (raw) to 0.164 for PGNN, while QDM achieves 0.277. This unexpected behaviour is attributed to the differing event distribution in the validation period. However, PGNN improves detection for severe and moderate dry classes, and Cohen’s Kappa increases from 0.552 (raw) to 0.646 (PGNN) compared with QDM’s 0.493. The water-balance constraint ensures climatic consistency and preserves the annual cycle (PET peaks in October at 148.1 mm month⁻¹, close to the CRU reference of 152.2 mm month⁻¹), while QDM introduces a two-month phase shift. The framework offers a potential scalable pathway for operational drought monitoring, but the test-period degradation suggests a need for adaptive correction methods that can track evolving climate baselines. Full article

Loess mountainous regions in northern China suffer frequent landslides, collapses and debris flows controlled by complex geological settings, seasonal rainstorms, freeze–thaw cycles and large-scale human engineering activities. Multi-geohazard susceptibility evaluation can provide fundamental data support for regional disaster prevention and territorial planning. Taking Shanxi Province, a typical loess-mountain transition zone, as the study area, this paper establishes an evaluation framework for landslides, collapses and debris flows. Ten conditioning factors are selected, including lithology, terrain parameters, distance to faults, distance to rivers, NDVI and RX1day (annual maximum 1-day precipitation). A 30 m grid unit is adopted as the basic evaluation unit. A total of 2598 verified geohazard points are taken as positive samples. Negative samples with equal quantity are extracted from low and very low susceptibility areas of the preliminary zoning map generated by the Frequency Ratio (FR) method, with an 800 m minimum separation distance between sampling points to reduce spatial autocorrelation. Two models, Logistic Regression (LR) and Support Vector Machine (SVM), are constructed, and five-fold cross-validation is used to test model performance through five statistical indicators and AUC values. The results show that, under the specific model configurations and sampling strategy adopted in this study, the LR model achieved higher predictive performance (average test AUC = 0.995) than the SVM model (average test AUC = 0.752) in the comparative assessment. Statistical analysis of the final susceptibility map derived from the LR model indicates that high and very high susceptibility zones account for 80.94% of the total provincial area and contain 87.45% of all recorded geohazard points, which confirms the consistency and reasonableness of the zoning results. Spatially, high-susceptibility areas are concentrated in the western and northwestern loess tablelands, the Fenhe River fault basin, and fault-developed sections of the Lüliang and Taihang Mountains. Thick loess layers, river undercutting and coal mining activities jointly reduce slope stability in these zones. Compared with conventional susceptibility modeling workflows, this study incorporates the RX1day extreme precipitation index and implements Frequency-Ratio-constrained stratified negative-sample selection to reduce training-sample bias. The produced susceptibility maps can provide technical support for differentiated geological hazard risk management, ecological restoration and territorial spatial planning for loess-mountain transition regions in northern China, thereby directly contributing to regional sustainable development and disaster resilience. Full article

Mediterranean cities with complex terrain are increasingly exposed to overheating. This study investigates Surface Urban Heat Island Intensity (SUHII) in Cosenza, southern Italy, in relation to land-use, neighborhood-scale morphology, extreme-temperature conditions and local wind. Summer ECOSTRESS Land Surface Temperature (LST) observations from 2018 to 2025 were integrated with CORINE Land Cover, CLC+ Backbone, urban morphological indicators, and meteorological data. Continuous urban fabric exhibited the strongest SUHII, with median values reaching about 6.5 °C at midday, whereas less compact urban areas exhibited lower thermal contrasts. A bin-based analysis revealed clear within-class morphological gradients, particularly in discontinuous urban fabric and industrial/commercial areas, where increasing building height, building surface fraction, and impervious surface fraction were generally associated with higher SUHII; these relationships were weaker and less systematic in compact urban fabric. TX90p conditions were associated with lower SUHII than No-TX90p conditions, likely reflecting stronger warming of rural reference surfaces and a reduced urban–rural thermal gradient. Wind-related differences were generally modest and non-systematic across land-use classes, TX90p categories, and diurnal periods. Overall, SUHII variability reflects both categorical land-use differences and continuous neighborhood-scale morphological gradients, while extreme-temperature and ventilation conditions further modulate urban–rural surface thermal contrasts in this Mediterranean valley city under complex topographic conditions. Full article

Reducing the harm caused by natural disasters requires an accurate, defensible assessment of where social vulnerability is concentrated. Existing social vulnerability studies rely predominantly on conventional statistical techniques, such as principal component analyses and simple additive indices, and rarely derive indicator weights and an overall ranking within a single, internally consistent decision framework. This study addresses that gap by proposing a hybrid multi-criteria decision-making (MCDM) model that combines hierarchical data envelopment analysis (H-DEA) with the reference ideal method (RIM). H-DEA objectively derives the relative importance of four dimensions and twelve indicators of social vulnerability directly from the data, avoiding the subjectivity inherent in expert-elicited weights, while RIM ranks alternatives against a decision-maker-defined reference ideal range rather than an extreme value, thereby mitigating the rank-reversal problem associated with conventional distance-based methods. The model is applied to 161 townships and districts in Taiwan using government-sourced mitigation data covering exposure, mitigation and preparedness, response capacity and recovery. The results show that recovery capacity—in particular, local public finance and social support—contributes most to social vulnerability, followed by mitigation and preparedness. The townships and districts of highest vulnerabilities are heavily concentrated in Pingtung County. A comparison against H-DEA alone, TOPSIS and VIKOR, using Spearman’s rank correlation, confirms the stability of the proposed ranking, while a comparison against an unweighted average demonstrates the practical value of objective weighting. The findings offer local governments and disaster-management authorities an evidence-based basis for prioritizing mitigation investment. Full article

Based on CN05.1 observational data, this study systematically evaluates the performance of the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) and the original Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating extreme high-temperature (EHT) events over the Sichuan–Chongqing region during 1981–2014. Furthermore, it projects changes in EHT indices under different emission scenarios for the mid-and late-21st century using the NEX-GDDP-CMIP6 dataset. The results indicate that NEX-GDDP-CMIP6 exhibits higher spatial correlation (>0.90) and lower root-mean-square error (RMSE) than CMIP6 in depicting the spatial distribution of maximum temperature (Tmax), with mean biases reduced from approximately −2 °C to within 1 °C. This dataset also captures the spatial patterns of EHT frequency (EHTF) and intensity (EHTI), although it exhibits a systematic underestimation of their magnitudes due to the inherent smoothing effect of the Bias Correction and Spatial Disaggregation (BCSD) method. The comprehensive TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) ranking shows that NEX-GDDP-CMIP6 generally ranks higher than the original CMIP6 in simulating EHT indices across all three weighting schemes and exhibits higher inter-model consistency, which further validates its overall applicability in the Sichuan–Chongqing region. Under the Shared Socioeconomic Pathway (SSP) scenarios SSP2-4.5 and SSP5-8.5, all 22 models project positive changes in Tmax, EHTF, and EHTI for both the mid- and late-21st century. The ensemble median increases reach approximately 5.5 °C for Tmax, 28 days per year for EHTF, and 1.8 °C for EHTI, which are 2–3 times the mid-century values and roughly double the corresponding increments under SSP2-4.5. The extreme heat hotspots are primarily located along the Sichuan–Chongqing border, with greater intensification and broader spatial coverage under SSP5-8.5 than under SSP2-4.5, indicating a progressive worsening of heatwave conditions across the study region. Full article

Benzophenone-3 (BP-3), an organic UV filter extensively applied in sunscreens, cosmetics and daily plastic products, is classified as a typical emerging endocrine-disrupting micropollutant. This compound is highly susceptible to bioaccumulation in aquatic organisms, triggers coral bleaching, and incurs oxidative damage to algae, fish and invertebrates. Conventional wastewater treatment processes cannot efficiently eliminate BP-3 from aqueous media, thereby imposing severe ecological risks on freshwater and marine ecosystems. In this study, iron-impregnated biochar (Fe-BC) was synthesized via an impregnation–pyrolysis route using powder of cultivated Chlorella pyrenoidosa (green microalga) as raw feedstock. Batch adsorption experiments revealed that iron impregnation remarkably enhanced the removal efficiency of BP-3. The maximum Langmuir saturated adsorption capacity of Fe-BC reached 91.7 mg/g, considerably exceeding the value of 51.5 mg/g for pristine biochar. Kinetic data exhibited favorable fitting with the pseudo-first-order kinetic model, demonstrating that Fe-BC rapidly captures BP-3 and achieves adsorption equilibrium within 120 min. Solution pH exerted a prominent influence on adsorption performance: the material maintained high BP-3 adsorption capacity at pH 7–10, whereas adsorption capacity declined drastically under strongly acidic (pH < 5) and extreme alkaline conditions (pH > 10.5). Fourier-transform infrared spectroscopy (FTIR) validated the successful loading of iron species onto the biochar surface, as well as the binding of BP-3 onto Fe-BC. Combined with pH-controlled experimental results, hydrogen bonding, hydrophobic interactions, and pore-filling effects are inferred as the dominant adsorption mechanisms for BP-3 removal. Furthermore, Fe-BC retained favorable BP-3 removal performance in simulated seawater matrices, endowing it with preliminary potential for wastewater treatment in coastal zones and tourist scenic areas. This work offers basic laboratory insights into BP-3 adsorption, while further verification concerning environmental low-concentration conditions, authentic water matrices, material reusability and stability is essential for its practical application. Full article

Microplastics (MPs) widely distribute in marine environments and generate severe hazards to marine biota, yet little is known regarding the toxic impacts of aged MPs on benthic invertebrates and their surrounding sediment microbiota. This work aimed to fill this research gap by exploring the comprehensive toxicity of aged polymethylmethacrylate microplastics (PMMA-MPs) toward Urechis unicinctus and its sediment habitat. A 21-day laboratory exposure trial was performed with two PMMA-MP treatments: environmentally realistic low concentration (10 μg/L) and extreme high concentration (1000 μg/L). Aged PMMA-MPs accumulated in the body wall and intestine of U. unicinctus, with markedly higher body wall MPs loads in the high-dose group (p < 0.05). High-concentration PMMA-MPs significantly raised intestinal total protein and triggered intense oxidative stress (p < 0.01), and the Integrated Biomarker Response value rose dose-dependently. Sediment microbial β-diversity was obviously reshaped (p < 0.05), with enriched pathogens including Klebsiella and Enterobacteriaceae under high MP exposure, while the worm’s intestinal α-diversity and core microbiome remained stable, showing host adaptability. This study reveals multi-level toxic outcomes of aged PMMA-MPs on benthic worms and their sediment microhabitat, offering fundamental data to evaluate the ecological risks of weathered MPs in marine benthic systems. Full article

The construction industry’s dependence on ordinary Portland cement (OPC) makes low-carbon binder systems an urgent priority; yet, the nonlinear interactions among multiple supplementary cementitious materials (SCMs) and nanomaterials complicate rational mix design. This study fuses explainable artificial intelligence (XAI) with a hierarchy of closed-form mathematical formalisms and an experimental durability programme for a quaternary sustainable concrete in which OPC is partially replaced by sugarcane bagasse ash (SCBA, 40 kg/m³), ground granulated blast furnace slag (GGBS, 60 kg/m³), natural zeolite (20 or 40 kg/m³) and nano-silica (0–20 kg/m³) at a constant water–binder ratio of 0.45. Thirteen mixes were tested for compressive and flexural strength, rapid chloride penetration (RCPT) and sulfuric acid resistance at 7, 28 and 56 days. The optimum blend (12 kg/m³ nano-silica) reached 45.0 MPa at 28 days, 49.5% above the control, while reducing chloride charge by 70% and acid mass loss by 65%. Information theoretic discrimination among three competing hydration kinetics laws selects the hyperbolic rate model with an Akaike weight of 1.000 (ΔAIC_c > 32), showing the blend raises the ultimate strength ceiling by 46% while delaying half-strength by only two days. Within this mix series, effective binder (k-value) analysis indicates that, at low dosage, one kilogram of nano-silica contributes 28-day strength broadly comparable to that of several tens of kilograms of OPC (a dataset-specific, dose-dependent estimate rather than a general mass equivalence), and three independent estimators—the experimental peak, the response surface stationary point (12.8 kg/m³) and the marginal efficiency zero (13.2 kg/m³)—converge on an optimum nano-silica dosage of 3.0–3.3% of binder. Principal component analysis compresses the six-dimensional strength–durability response into a single latent statistical axis (interpreted as an indicator of pore connectivity) carrying 91.5% of the variance, and a Fickian error function solution seeded by Berke–Hicks conversion of RCPT charge projects a 3.4-fold extension of the chloride-initiation service life (36.7 versus 10.8 years at 50 mm cover). Six machine learning models were benchmarked; extremely randomized trees performed best (R² = 0.9905, RMSE = 0.920 MPa; leave-one-out R² = 0.986; bootstrap 95% CI on R² [0.981, 0.996]), and SHAP force plot attributions were triangulated with Sobol global sensitivity indices (curing age 75.4%, nano-silica 23.1% of output variance) and response surface significance tests. The optimized mixes cut embodied CO₂ by 26–32% and improve eco-strength efficiency 2.1-fold; grey relational analysis over six strength, durability and carbon criteria ranks the 12 kg/m³ nano-silica mixes first. The framework demonstrates how interpretable machine learning, information theoretic model selection, diffusion theoretic service-life projection and experimental durability evidence can be unified into a transparent, physically validated basis for sustainable concrete mix design. Full article

This article shows an analysis of interarea oscillations between the Iberian Peninsula and the rest of Europe using three reduced academic models of Red Eléctrica de España (REE). The original model has 10 nodes in 400 kV for REE, the other two models have 61 nodes in 400 kV for REE (with different meshing levels), and two 400 kV nodes represent Portugal and the rest of Europe in these models. The obtained frequency for interarea oscillations with these models is in the order of 0.22 Hz, which is very similar to the officially reported values during a recent real event, shortly before a general blackout, which were also in the order of 0.21 Hz. Thus, these reduced models are suitable for representing this phenomenon to some extent, and this fact is a contribution of this article. Two theoretical cases with extreme reductions in tie impedances with the rest of Europe were also simulated, and the results showed moderate increases in the frequency of interarea oscillations. Furthermore, the transient stability was also analyzed. Although the obtained critical clearing times and dynamic behaviors are relatively similar using the different analyzed models, the results confirm that a reduced dynamic model could be more suitable for the study of one phenomenon than for the study of another phenomenon. A novel simplified explanation of the frequency of the found interarea oscillations was developed and verified with results from numerical simulations; this is the main novelty of this article and it is very useful for an illustrative and conceptual description of this phenomenon since it is based on the well-known academic example of a single machine connected to an infinite bus. Full article

Extreme financial events are usually defined by loss magnitude alone, which quantifies severity but leaves open whether tail losses arrive in isolation or in temporally concentrated patterns. This paper develops a fractional tail-event timing diagnostic combining peaks-over-threshold (POT) generalized Pareto distribution (GPD) severity with Mittag-Leffler recurrence of interarrival times. We show that when exceedance times are driven by a stationary and ergodic latent intensity, the log-moment recurrence estimator has an explicit probability limit below the Poisson boundary that depends on the intensity only through its marginal log-variance, so volatility-driven dispersion of the arrival rate suffices to produce sub-Poisson estimates. In six daily series through 2026, the estimator is calibrated against a permutation null preserving the marginal loss distribution and the integer-gap discretization. That null sits near 1.05 rather than at unity, so the nominal Poisson boundary is not the correct reference for trading-day gaps. Against the calibrated null, the Deutscher Aktienindex (DAX), Nasdaq Composite, Nikkei 225, platinum, and the Standard & Poor’s 500 (S&P 500) display significant temporal concentration and coffee does not, whereas the monotone decline of the estimator across thresholds proves to be mechanical. Once goodness-of-fit tests are bootstrap-calibrated, no waiting-time law survives for four assets. The Mittag-Leffler parameter is therefore used only as an interpretable diagnostic index of temporal concentration; it is not a fitted-law claim and should not be interpreted as evidence of structural long memory or of a fractional data-generating mechanism. Full article