Search Results (7,228)

Deep shale reservoirs in the tectonically complex margin of the southern Sichuan Basin have experienced multistage deformation, resulting in strong spatial heterogeneity of the present-day geostress field. However, the influence of stress heterogeneity on multiscale pore structure evolution and reservoir quality remains poorly constrained. Here, we integrate in-situ stress measurements, overburden porosity and permeability experiments, CO₂/N₂ adsorption, high-pressure mercury intrusion, SEM-MAPS (Scanning Electron Microscopy-MAPS) pore imaging, stress well profile interpretation, and multifractal analysis to quantify the controls of present-day geostress heterogeneity on pore structure evolution in deep Longmaxi Formation shale. The results show that the present-day stress regime is characterized by a strike-slip pattern (σ_H > σ_v > σ_h), with significant variations among different structural deformation zones. Increasing structural deformation results in enhanced differential stress, increasing by 30–80% from gentle structures to tight folds and fault-affected zones, accompanied by a 60–70° rotation of the maximum principal stress orientation. Differential stress, effective stress, differential stress coefficient, and stress structure index exhibit strong negative correlations with porosity, whereas permeability decreases nonlinearly with increasing stress, indicating progressive pore-throat compression and connectivity degradation under heterogeneous stress conditions. Multifractal analysis reveals that pore-size domains exhibit different sensitivities to stress heterogeneity. The macropore fractal dimension (DN3) shows the strongest response, followed by mesopores (DN2), whereas micropores (DN1) exhibit relatively limited variations. Fault-affected zones and strongly deformed regions display higher DN3 values (>2.8), reflecting enhanced complexity of macropore and fracture networks. In contrast, gentle structural zones characterized by curvature values <0.10 km⁻¹ and distances >500 m from faults exhibit relatively low and stable fractal dimensions (<2.73), indicating more homogeneous pore structures. Increasing stress heterogeneity induces the transformation of organic matter pores from regular subcircular shapes to flattened and slit-like morphologies, accompanied by pore-size migration toward smaller scales (<15 nm) and enhanced pore heterogeneity (Df > 1.35). These findings reveal that present-day geostress heterogeneity governs shale pore fractal evolution and promotes the transition from micropore-dominated to heterogeneous macropore–fracture systems. This study provides quantitative insights into stress-controlled pore evolution and reservoir quality evaluation in deep shale reservoirs under complex tectonic settings. Full article

Inland waterbodies are a significant emitter of greenhouse gases, especially methane. They contribute 15.2% of all anthropogenic methane emissions. Major uncertainties are still present in emissions estimates for these systems; as a result, more in situ measurements are required to constrain them, especially when ebullition, a direct release of methane, is present. The Monitub Automated Floating Chamber (AFC) was developed to address existing challenges in floating chamber deployments, a common emissions measurement method, through the use of a custom floating chamber, datalogger, and generic calibration of the TGS2611-E00 sensor. This system is able to perform long-term, real-time monitoring of methane emissions from waterbodies at a low cost. This allows for the correlation of environmental drivers to further constrain uncertainties related to emissions, due to its high temporal frequency, and help to answer questions about the spatial heterogeneity of emissions, as more systems can be deployed at a lower cost. The Monitub AFC was deployed to an urban lake for testing, showing its ability to differentiate between diffusion and ebullition, and its capabilities as a long-term monitoring system. Full article

Periodontitis and dental caries remain among the most prevalent chronic diseases, and their local treatment is limited less by the choice of active agent than by the difficulty of sustaining therapeutic concentrations against salivary and crevicular clearance, the mucosal barrier and the biofilm matrix. This narrative review (PubMed/MEDLINE, Scopus, Web of Science; 2010–2026; 118 sources) maps five mechanistic classes—mucoadhesive, in situ gelling, stimuli-responsive, nano-/microparticulate and biomimetic—alongside marketed sustained-release products onto the specific barrier each addresses and onto an explicit translational gradient. The barriers are quantified rather than described: a pocket of ≈0.5 µL perfused at ≈20 µL/h turns over some 40 times hourly, giving an intra-crevicular half-life of about one minute, and the inflamed pocket is neutral-to-alkaline (pH 7.4–8.5), so acid-triggered release is a cariogenic and not a periodontal strategy, whereas alkaline-triggered release remains an open design space. A dose calculation from these figures identifies payload potency and deliverable mass, not carrier retention, as the binding constraint on phytocompound delivery. Because no single class overcomes all barriers, hybrid nano-in-macro architectures are analysed as the structural solution. Measured against a marketed benchmark of ≈0.3 mm additional probing-depth reduction, progress now depends on consolidation rather than on novelty. Full article

The maturity method (ASTM C1074) predicts in situ concrete strength from a recorded temperature history but assumes a constant apparent activation energy, contradicting the experimental evidence that the activation energy falls as hydration shifts from kinetics control to diffusion control—an effect that differs between binder chemistries when supplementary cementitious materials (SCMs) are used. This study develops a physics-based maturity model in which the apparent activation energy varies linearly with temperature, Q(T) = Q₀ + β_Q(T − T_ref), coupling a variable-energy Arrhenius equivalent age to a hyperbolic strength–maturity relationship. The model was calibrated on 196 mean-strength observations (588 cube tests) from seven self-compacting concrete mixtures cured isothermally at 10, 20, 35 and 50 °C and tested at seven ages (1–90 days). All four SCM systems (fly ash, GGBS, silica fume and rice husk ash) returned a negative coefficient (−210 to −974), enclosing the temperature sensitivity implied by independent calorimetric measurements on Portland cement paste (≈−580 J/(mol·K)), whereas the ordinary Portland cement control returned a positive point estimate (+101) that is not statistically distinguishable from zero. The model achieved R² = 0.929 (RMSE = 4.74 MPa), outperforming the constant-energy ASTM C1074 baseline in both accuracy and the Akaike Information Criterion while eliminating its systematic bias at the temperature extremes. Five-fold cross-validation confirms the out-of-sample accuracy (R² = 0.901, RMSE = 5.59 MPa), and bootstrap analysis shows the negative coefficients of the fly ash, GGBS and rice husk ash systems to be statistically significant. External validation on 120 independent literature observations gave R² = 0.881. Full article

Remote-sensing-derived spectral indices and in situ measurements provide complementary information for aquatic monitoring, but their practical integration is complicated by asynchronous observations, heterogeneous tables, missing acquisitions, and non-reproducible preprocessing. This study presents AI-Aquatica-RS, a modular Python framework for spectral-index calculation, station-aware nearest-neighbor temporal alignment, feature-set construction, regression benchmarking, command-line execution, and structured result export. The software was evaluated using a fully synthetic controlled benchmark; no real satellite scenes or field-monitoring measurements were used. The benchmark comprised 1080 daily in situ-like observations from six stations and 181 unique remote-sensing-like acquisitions generated as water-like surface-reflectance proxies. A ±3-day alignment tolerance produced a shared complete-case cohort of 954 records. To ensure a fair comparison, the in situ-only, spectral-index-only, and fused configurations were evaluated on exactly the same 667 training and 287 validation records. The fused configuration achieved the best performance using ridge regression (RMSE = 3.481 NTU, MAE = 2.768 NTU, R² = 0.729), compared with RMSE values of 5.086 NTU for the in situ-only configuration, and 5.538 NTU for the spectral-index-only configuration. The benchmark demonstrates reproducible execution and recovery of complementary information under controlled conditions; it does not constitute environmental validation. AI-Aquatica-RS provides an extensible software layer for future studies using real satellite products, monitoring networks, sensor-specific preprocessing, and spatially blocked validation. Full article

The performance of physical workers is significantly influenced by the extent and quality of their motions. However, accurately measuring and assessing these motions remains a challenge due to the limitations in conventional instrumentation; wearable sensors require calibration and restrict mobility, while marker-based motion capture systems are costly and impractical for field deployment. Recent advancements have enabled in situ video analysis for the real-time observation of worker behaviors. To address these measurement constraints, this paper introduces a novel framework for tracking and quantifying upper and lower limb motions, issuing alerts when critical thresholds are reached. Using joint position data from posture estimation, the framework employs Hotelling’s $T^2$ statistic to quantify and monitor motion amounts. A significant positive correlation was noted between motion warnings and the overall NASA Task Load Index (TLX) workload rating (r = 0.218, p < 0.005). A supervised Random Forest model trained on the collected motion data was benchmarked across multiple datasets, including the in-house assembly dataset, G-AI-HMS, UCF Sports Action, UCF50, and PE-USGC. The proposed framework identified motion anomaly patterns with a maximum accuracy of 94% on the in-house assembly dataset, while performance varied across the external benchmark datasets. Full article

As generative AI chatbots become a primary information channel, users increasingly accept answers without verification, and citations can raise trust even when sources are irrelevant or fabricated. How source-attribution visualization shapes the visual preconditions of verification remains unknown: users can notice, read, or compare a source without clicking. This within-subjects eye-tracking study (N = 23; 92 trials) evaluated four attribution visualizations abstracted from commercial AI chatbots and rendered as simulated screens: inline component (sentence-end chips), card list (cards above the answer), side panel (adjacent panel), and raw hyperlink (bare URLs), combining gaze metrics, surveys, and interviews. Repeated-measures ANOVAs revealed strong layout effects on source discoverability and engagement, largely robust to sensitivity checks (the panel’s discovery latency was order-sensitive): the card list was discovered almost immediately, with the raw hyperlink last. Yet no self-reported measure differed detectably. The most frequently nominated format, the inline component, attracted about half the dwell time of the stand-alone formats, whose prolonged fixations suggested citation-to-text mapping cost rather than genuine engagement. This attention–preference gap means both must be measured jointly. We contribute a four-layout gaze-based comparison, a reproducible participant-level analysis workflow, and three design principles (pre-click identifiability, sentence-level claim–source mapping, and in situ preview) within a proposed two-stage attribution architecture. Full article

Supraglacial lake depth is a key variable for quantifying surface meltwater storage and assessing ice-shelf stability, yet spatially continuous and reliable bathymetric information remains difficult to obtain in polar regions because in situ measurements are scarce and optical imagery cannot directly provide water depth. This study develops an integrated framework for supraglacial lake identification and bathymetry retrieval by combining ICESat-2 ATL03 photon-counting lidar data with Sentinel-2 multispectral imagery. ICESat-2 lake photons were used to constrain lake-region extraction from Sentinel-2 imagery, and the photon-derived along-track depths were corrected for scattering and refraction before being converted into Sentinel-2 pixel-level depth labels. Based on these labels, four retrieval models were constructed and evaluated, including an empirical model, CatBoost, a convolutional neural network (CNN), and a residual dense network (RDN). CatBoost generated initial depth estimates, while CNN and RDN further incorporated the CatBoost-derived depth prior and Sentinel-2 multispectral features for pixel-level depth prediction. Experiments over four investigated supraglacial lakes showed that RDN achieved the best average performance across the investigated lakes, with mean R², RMSE, and MAE values of 0.927, 0.187 m, and 0.144 m, respectively. For the investigated lakes, the integration of ICESat-2 and Sentinel-2 extended discrete along-track reference-depth observations to spatially continuous bathymetry maps. Because the training and validation samples were obtained from different spatial blocks within the same four lake scenes, the reported performance primarily reflects within-lake spatial generalization under the investigated conditions, and transferability to unseen lakes remains to be evaluated. These maps may provide inputs for future lake-volume estimation and ice-shelf hydrological analyses, while their applicability to lakes with different morphological and optical conditions requires further evaluation. Full article

High-resolution online sensors are now common in environmental process systems, yet turning their non-stationary, heterogeneous observation streams into reliable predictions of the underlying process state remains difficult. The statistical distribution of a sensor stream changes over time, the measured variables do not coincide with the state variables of interest, and repeatedly running a mechanistic process model for forward prediction is computationally costly. We present FT-Crossformer, a time–frequency Transformer that acts as a data-driven surrogate between multi-sensor observations and multivariate process-state prediction. To handle distribution shift in the sensor streams, a time-domain distribution-transformation module, together with an inverse-mapping module, performs an affine bias correction that removes per-window non-stationary statistics at the input and restores them at the output, so the gap between training and test distributions is reduced without discarding non-stationary information. We show that this affine correction, including its learnable per-variable scale and shift, acts in the frequency domain on every non-zero frequency component as one common scaling factor that does not depend on the frequency index, so it cannot change the relative magnitudes among the components. A frequency-stability measurement module and a frequency-weighting module therefore re-weight the spectral components of the observation signal so that the stable, task-relevant ones contribute more to the reconstructed signal. The cross-dimension attention of the Crossformer backbone serves as a multi-sensor fusion mechanism that models the dependencies among the measured variables. We validate the method on public benchmark datasets from different domains as a check of generality and, most relevantly, for environmental modeling on two real cases: a wastewater nitrogen-and-phosphorus-removal process and chlorophyll forecasting from an in situ estuary sensor mooring in San Francisco Bay. On the estuary chlorophyll data, which carries a strong train-to-test distribution shift, the full FT-Crossformer demonstrates superior accuracy among the evaluated models at the next-day nowcasting horizon, and an ablation shows that both the time-domain trans- formation and the frequency-domain weighting contribute to this accuracy. FT-Crossformer produces forward predictions from distribution-shifted sensor data with a single fixed-cost forward pass in place of a repeated mechanistic solve, which makes it a practical building block for sensor-data integration and assimilation in environmental process modeling. Full article

This study devised a blasting experiment to establish a dynamic quantified relationship between blasting and movement, failure, and ejection of rock masses. The experiment was conducted using the 3D digital image correlation method (3D-DIC), which provided high-speed images capturing the process of shallow-buried tunnel blasting. The study analyzed the mechanical behavior of full-section rock mass under blasting action through cross-scale image processing. Yield and elastic points were distinguished based on the time–displacement curve. Then, the spatial vector method was employed to deduce flying rock trajectory and throwing distance, enabling the subdivision of underground space based on risk assessment. The results show that the rock in the cut zone starts moving within 3 ms after initiation, ultimately exhibiting a maximum visible off-plane displacement of 215 mm. Displacements are related to delay time and distance. Different zones show distinct dominant directions of rock mass displacement. The rock mass becomes flying rocks separated from the cross-section. The initial velocity of the flying rocks ranges from 11.8 m·s⁻¹ to 29.9 m·s⁻¹. Around 85% of the flying rocks fall within the range of 0 to 40.8 m. Only 5% of the flying rocks fall outside 64.3 m. Appropriate protective measures should be taken for equipment during experiments. Full article

The pressuremeter test (PMT) measures in situ soil properties under the original stress state with minimal disturbance. However, interpreting PMT data for constitutive parameters remains reliant on empirical correlations, and a key challenge is the poorly understood size effect arising from the equipment dimensions.‌ This study aims to systematically quantify such size effects to provide a scientific basis for optimizing the design of laboratory PMTs. A series of 36 PMT simulations were performed using the ‌finite element method (FEM), incorporating the Duncan–Chang E-B hyperbolic model. Six cylindrical soil models of diameters ranging from 0.6 m to 2.4 m were established for both sand and clay, under three overburden pressures (200 kPa, 1000 kPa and 3000 kPa). The radial stress, strain distributions and borehole wall displacement were systematically analyzed. The analysis reveals that the size effect originates from the truncation of the radial strain integration path. In all cases, borehole wall displacement increases with model diameter, characterized by a steep rise for diameters below 1.2 m and a plateau for those above 1.2 m. Although clay produces larger displacements than sand, and higher stress produces larger displacements than lower stress, the identified pattern remains robust. Considering both the displacement–diameter relationship and practical cost constraints, an optimal equipment diameter of 1.2 m is recommended. Full article

Stachys fontqueri is a strict endemic species of the Moroccan Rif that depends on specific ecological conditions. To understand the effect of climate change on the projected change in potential distribution under current and future climatic scenarios, ecological niche modelling was performed using MaxEnt algorithm based on 70 occurrence records, and 5 bioclimatic variables at a spatial resolution of 30 arc-seconds. To model the effect of climate change, four climatic scenarios were used, namely CSM2-SSP1-2.6, CSM2-SSP5-8.5, MIROC6-SSP1-2.6, and MIROC6-SSP5-8.5, for the period 2061–2080, and the Maximum Test Sensitivity Plus Specificity threshold was used to distinguish suitable from unsuitable habitats. The results demonstrated high model performance, with an AUC ranging from 0.921 to 0.930 and a TSS from 0.81 to 0.83. Three bioclimatic variables contributed significantly to determining the suitable potential distribution area of the species, namely Precipitation Seasonality (Bio15), Temperature Annual Range (Bio7), and Annual Mean Temperature (Bio1). The suitable area covered 3244 km² under the current climate and is projected to decrease by 23.25% to 29.59% under future climate scenarios. This contraction of suitable habitat due to climate change could be exacerbated by human activities, thereby requiring urgent in situ and ex situ conservation measures to ensure the species’ resilience. Full article

Pronounced spatio-temporal variability of rainfall in semi-arid basins remains a central challenge for rainfall–infiltration–runoff modeling, particularly in ungauged or newly instrumented catchments where continuous soil-moisture data are unavailable. This study evaluates the comparative predictive performance of the SCS-CN and Green-Ampt (GA) infiltration methods, implemented within HEC-HMS at the sub-basin scale, using eight rainfall–runoff analysis time windows recorded during the 2020–2025 rainy seasons within a monitoring network operational since October 2019 in an instrumented semi-arid basin in Mexico. A blind-validation framework was adopted: parameters for both methods were derived a priori from tabulated sources indexed by land use, hydrologic soil group, soil textural class, and locally supported by textural analysis at three depths per sub-basin, in situ testing of saturated hydraulic conductivity, and gravimetric determination of field capacity; the initial moisture content required by GA was set equal to the measured field capacity (θ_i = θ_fc) to equate initial conditions between the two methods. Spatially distributed rainfall was captured by four monitoring stations under a one-to-one gauge–sub-basin assignment scheme, with monthly rainfall depth varying from 22.8 to 204.9 mm across the four sub-basins. Both methods reproduced observed discharge with varying levels of agreement: SCS-CN yielded very good performance (Pearson R = 0.95; Nash–Sutcliffe efficiency NSE = 0.76), whereas Green-Ampt yielded moderate correlation but unsatisfactory NSE (R = 0.70; NSE = 0.45) against the Levelogger records. Contrary to the initial expectation that the physically based GA would outperform SCS-CN, SCS-CN yielded substantially higher performance across windows, with the two simulated discharge series differing by a mean absolute deviation of 36.9 m³/s. A systematic sensitivity analysis (±6%, ±10%, ±20% perturbations) revealed an asymmetric response: SCS-CN was highly sensitive to Curve Number perturbations (mean-deviation amplitude 65.9 m³/s), whereas Green-Ampt was nearly insensitive to its compound soil-hydraulic parameterization (amplitude 3.2 m³/s), indicating a structural limitation of the physically based method under blind validation. Full article

HY–4A is China’s first ocean salinity remote-sensing satellite, launched into orbit in November 2024 and currently in operational service. The LASMR (L-Band Aperture Synthesis Microwave Radiometer) is the L-band synthetic aperture radiometer onboard the HY–4A satellite. This study validates the LASMR Level-2 SSS (sea surface salinity) product using in situ salinity observations from Argo floats, covering the period from November 2024 to December 2025. Global analysis indicates that the LASMR SSS retrieval uncertainties show a distinct zonal distribution, which primarily reflects the impact of sea surface temperature (SST) and sea surface wind speed on SSS retrieval accuracy. A lower SST reduces the sensitivity of brightness temperature (TB) to SSS variations, and a high wind speed degrades the sea surface roughness correction. Both factors lead to increasing uncertainties in SSS retrieval. Furthermore, atmospheric parameters including water vapor content and precipitation also affect the SSS retrieval uncertainty. The influence of water vapor may originate from its coupling with SST/wind speed and inherent uncertainties in the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data. The effect of precipitation is more complex: it increases ocean TB through rain-induced surface freshening and additional rain-induced roughening, which aliases into the satellite signal. Moreover, precipitation-enhanced vertical salinity gradients amplify the vertical representativeness error arising from the depth difference between satellite sensing and Argo measurements. Meanwhile, impacted by land brightness temperature contamination and radio-frequency interference (RFI), the SSS retrieval accuracy of HY–4A decreases significantly in coastal waters compared with the open ocean. Since the traditional buoy–satellite dual-matching method tends to overestimate uncertainties in satellite data, an Argo/HY–4A/SMAP (Soil Moisture Active Passive) triple-collocation dataset is used to estimate the LASMR SSS retrieval uncertainties. The triple-collocation method yields robust uncertainty estimates for both satellites (HY–4A and SMAP) over the global ocean and high-salinity-variability regions. In conclusion, the global uncertainty of the HY–4A LASMR SSS product is 0.35 psu. These results provide a reference for future product refinement and improvements in HY–4A SSS retrieval algorithms. Full article

Widening participation spans the whole student lifecycle, from access through to success within higher education and progression beyond it, yet policy and evaluation have concentrated on the access stage alone. Far less is known about how students admitted through widened access routes come to understand the legitimacy of their place, and the terms of their belonging, once inside an elite university. Reporting twenty-one (n = 21) walking interviews with students who entered the University of Cambridge through its Foundation Year, a one-year pre-degree programme for applicants whose educational opportunities have been constrained by structural disadvantage, this article examines a tension at the heart of meritocracy. Belonging is approached throughout as a relational and processual accomplishment practised in situ rather than as a psychological state to be measured, and the article asks on what terms it is made available along a visibly marked route. Participants recognized that admissions standards are shaped by unequal opportunity, though the recognition appeared not to weaken their commitment to meritocratic judgement, and they endeavored to suffuse their belonging with proof of deservingness. Such a pattern sits uneasily with Bourdieusian accounts of symbolic domination grounded in misrecognition, and points towards a compliance sustained through lucid perception of meritocracy and its ideological demands. Analysis of the walks suggests that the route organizes this tension by marking students publicly as different, by tying belonging to a competitive progression threshold, by collapsing heterogeneous experiences into a single administrative category, and by locating institutional judgement in everyday encounters with teaching staff. Belonging emerges less as a settled outcome of admission than as a conditional status that must be repeatedly earned. Widening access may therefore secure entry without securing the later stages of participation, so long as the legitimacy of membership continues to be organized through meritocratic evaluation that students simultaneously critique and reproduce. Full article