Search Results (166)

Bubbles in submarine cold-seep plumes cause strong scattering, attenuation, and poor continuity in water-column seismic responses, hindering continuous two-dimensional characterization of acoustic properties. Using multichannel seismic data from the Shenhu area, northern South China Sea, this study proposes a conductivity–temperature–depth (CTD)-constrained, layer-wise two-angle allied elastic impedance (AEI) inversion method to reconstruct water-column velocity. Based on the scattering-equivalent AEI theory, a two-angle equation is derived for layer-wise estimation of water-column velocity, thereby providing a theoretical basis for the subsequent inversion. A relative-amplitude-preserving workflow mitigates bubble-induced attenuation and random scattering, while pre-stack time migration produces common reflection point (CRP) angle gathers. Coherent scattered energy after imaging is parameterized by the local scattering half-angle. Small-angle and large-angle gathers are selected according to effective angular coverage for AEI inversion. CTD-derived seawater velocity and density provide AEI constraints, and the normalized two-angle AEI equation yields a continuous velocity section. Results show velocities of 1460–1560 m/s, with pronounced vertical stratification, relatively weak lateral variation, and agreement between inverted and CTD-derived velocity trends. Within the 0–1800 ms water-column time window, the root-mean-square difference between the inverted and CTD-derived velocities is 18.19 m/s. Combining the lateral continuity of multichannel seismic data with high-accuracy CTD-derived physical-property constraints enables continuous two-dimensional velocity reconstruction in bubble-scattering water columns and offers a new approach for observing cold-seep velocity structure and geophysically characterizing bubble-plume activity. Full article

Remote identification of color fronts, defined as transition zones with sharp gradients in water optical properties, has suffered from non-transferable thresholds, severe areal over-detection, and weak cross-estuary generalization. To address these issues, we propose a framework that integrates multi-scale spectral–spatial features with DenseNet-121. We constructed a 165-D vector, seven window scales (three × three to 15 × 15) × two statistical descriptors (means and standard deviations) × 11 bands + 11 bands, then rearranged it into a 3D tensor and resized it to a 2D image for DenseNet-121 transfer learning with red-band post-processing. On in-distribution tests, the model achieves 0.953 accuracy, 0.953 F1, outperforming random forest. Cross-estuary generalization yields a mean F1 (0.744). Performance varies with optical compatibility: the Mississippi (runoff-dominated) gives the best F1 (0.874), while the Pearl (multi-channel, runoff-tide co-controlled) drops to 0.607 due to heterogeneity and reversed reflectance patterns. The red-band constraint can help reduce areal false alarms and improve spatial coherence of frontal regions, but its effectiveness depends on optical separability. The output width reflects superposition of transition zone and window scale. We demonstrate the potential and boundary conditions of this approach for cross-estuary color front identification, offering insights for physically consistent and generalizable ocean color monitoring. Full article

To address the challenges associated with severe lost-circulation formations, including large loss-channel dimensions, rapid fluid losses, and the tendency of conventional bridging or physical-filling materials to accumulate at fracture entrances rather than penetrate deeply into fractures and form stable pressure-bearing plugs, a slag–fly ash–gypsum–sodium ethylenediamine tetramethylene phosphonate (EDTMPS) controlled-setting lost-circulation system was developed in this study. A 4 wt% bentonite base slurry was used as the dispersion medium, while a slag–fly ash blend served as the principal cementitious component. Gypsum was used to regulate the setting reaction, and EDTMPS was employed to control the slurry-thickening process, thereby coordinating slurry pumping, fracture filling, and in situ setting. The operational feasibility, consolidation capability, and short-term pressure-bearing performance of the system were evaluated through formulation screening, tests of slurry placement and hardened-material properties, water-based drilling-fluid contamination evaluation, and pressure-bearing tests in regular fractures with apertures of 1–5 mm. The results showed that, at a slag-to-fly-ash mass ratio of 6:4, the hardened material exhibited a compressive strength of 12.91 MPa. Within the investigated dosage range, the highest observed 1 d and 3 d compressive strengths were both obtained at a gypsum dosage of 2.0%. Under conditions of 150 °C and 50 MPa, the addition of 1.5 g of EDTMPS extended the slurry thickening time from 2.6 h to 7.3 h, while the compressive strength of the hardened material after 24 h of curing reached 17.18 MPa. At a water-based drilling-fluid contamination ratio of 30%, the compressive strength of the hardened material was 13.86 MPa, corresponding to a strength-retention ratio of 80.7%. In straight, constant-aperture fractures with apertures of 1.0, 2.0, 3.0, 4.0, and 5.0 mm, the maximum pressures sustained by the plugs before breakthrough were 16, 15, 14, 13, and 12 MPa, respectively, and decreased with increasing fracture aperture. These results indicate that, within the formulation range, water-based drilling-fluid contamination conditions, and short-term pressure-bearing conditions in regular fractures investigated in this study, the system can coordinate slurry-thickening control, 24 h hardened strength, contamination tolerance, and pressure-bearing performance in regular fractures. The findings provide a verifiable formulation-design approach for reconciling the placement-time window of lost-circulation materials for severe lost circulation with their post-placement pressure-bearing capacity. Full article

Earthquakes exhibit well-documented statistical regularities at the catalogue level, such as the Gutenberg–Richter magnitude–frequency relation and the Omori–Utsu aftershock decay, often interpreted as signatures of seismicity as a driven, dissipative system far from thermodynamic equilibrium. However, whether comparable signatures, such as scale invariance and anomalous diffusion, can be detected directly within individual ground motion recordings remains an open question. This work investigates whether acceleration, velocity, and displacement signals recorded during the 2009 $M_w$ 6.1 L’Aquila earthquake display statistical properties consistent with non-equilibrium complex systems, and whether different seismic phases carry distinct, reproducible statistical signatures. P- and S-wave onset times are estimated using AR-AIC, with adaptive search windows centred on theoretical arrivals from the CRUST1.0 velocity model. Coda onset is determined using three complementary criteria combined into a median ensemble, enabling the segmentation of each recording into up to five temporal windows. Displacement moment scaling is analysed for each window and signal type, within the framework of strong anomalous diffusion, yielding the scaling exponents $\zeta \left(q\right)$. Robustness is systematically assessed against the choice of coda onset method, the empirical thresholds defining coda onset and end, the sub-interval of $\tau$ used in the moment scaling fit, and the filter band applied to the ground motion signals. Evidence for anomalous diffusion is nuanced: both its sign and magnitude depend on the seismic phase, with only a subset of configurations remaining stable across all segmentation schemes tested. These results indicate that anomalous scaling signatures, when present, are not universal, and that systematic robustness analyses are essential to distinguish genuine physical effects from segmentation artefacts. Full article

Light-responsive materials enabling external modulation of bioactivity and spatial control are highly requested for photopharmacology—a booming research area of modern medicine. We present organo-inorganic hybrids of photoswitchable, bioactive symmetric diamine-phosphine oxides conjugated with luminescent carbon quantum dots (CQDs). The phosphonate compound was found to undergo Z-E isomerization upon 266 nm laser irradiation and exhibit butyrylcholinesterase (BChE) inhibition that increases twofold (15–30%) after photoconversion. Hybrids were fabricated via physical adsorption and chemisorption using different CQD surface groups, and characterized by UV-Vis, luminescence, and FTIR spectroscopy, confirming hybrid formation and retention of functional properties. In both binding modes, the molecules retained photoswitching capability despite steric constraints. All hybrids displayed orthogonal functions: luminescence (excitation at 350 nm) and photomodulation of BChE inhibition (at 266 nm). Remarkably, the binding mode dictated the bioactivity window—chemisorbed hybrids showed narrow 1.5-fold modulation, whereas physisorbed hybrids exhibited ultra-wide >10-fold modulation. This tunable responsiveness, achieved simply by altering the binding mode, demonstrates the exceptional potential of this hybrid design strategy for developing photoswitchable materials with tailored photopharmacological performance. Full article

This study investigated physically and chemically activated chimney soot to evaluate the influence of the activation process on the textural properties and porous structure of materials intended for use in supercapacitors. The materials were characterized by Raman spectroscopy to analyze molecular structure and composition, as well as BET analysis to determine porosity and structural properties based on N₂ adsorption-desorption isotherms. Their electrochemical characteristics were evaluated using a two-electrode configuration with cyclic voltammetry, galvanostatic charge–discharge measurements, and long-term cycling tests of up to 10,000 cycles within a voltage window of 0.05–1.0 V in a 1 M KOH electrolyte. Soot-based symmetric supercapacitors activated with KOH at a ratio of 3:1 exhibit a specific capacitance of 74–78 F g⁻¹ at 0.2 A g⁻¹ and achieve an energy density of 2.3 Wh kg⁻¹ at a power density of 32 W kg⁻¹. The results demonstrate that, by carefully controlling the type and quantity of the activating agent, chemical activation is an effective method for increasing the specific surface area and improving the electrochemical characteristics of the resulting materials. Chemical activation significantly expands the microporous framework, optimizing ion transport at the electrode–electrolyte interface. The results demonstrate the successful valorization of chimney soot as functional carbon electrodes for next-generation energy storage applications. Full article

Drying is widely used to stabilize foods, but long processing times and thermal exposure increase energy demand and can impair color, texture, nutrients, and flavor. Cold plasma (CP) pretreatment has attracted interest as a nonthermal strategy for accelerating moisture removal while maintaining product quality. This critical review examines CP pretreatment-assisted drying across food materials by linking structural remodeling with redox regulation and metabolic responses. Current evidence shows that changes in surface wettability, cuticular barriers, cell-wall and membrane integrity, and pore connectivity can facilitate water migration and shorten drying. CP-associated modulation of browning enzymes, oxidative status, and bioactive or flavor-related metabolites may also influence color, antioxidant capacity, nutrient retention, and flavor. However, these effects vary with discharge mode, treatment intensity, gas composition, pressure, temperature, and food-matrix properties. Excessive exposure can instead aggravate oxidation and diminish product quality. Current mechanistic evidence is strongest for plant foods and edible fungi and remains limited for animal-source foods. Together, these findings link plasma-generated chemical and physical agents to structural, biochemical, and drying responses. They provide a basis for defining material-specific operating windows and developing reproducible, safe, and scalable CP pretreatment-assisted drying of foods. Full article

This study presents a methodology for predicting the geometric features of single tracks of 316L stainless steel produced by Powder-Bed Arc Additive Manufacturing (PBAAM) from four independent process parameters using a multi-modal Physics-Informed Neural Network (PINN). PBAAM shares the same powder-deposition and layering scheme as Laser Powder Bed Fusion (LPBF) but uses a low-current micro-TIG arc rather than a laser as the heat source. A multi-task PINN architecture was developed that simultaneously predicts five geometric features measured from two imaging modalities (top-view and side-view arc), namely the arc core diameter (D_q), the arc cone angle (α_c), the heat-affected zone width (w_HAZ), the track core width (d_core) and the areal equivalent track width (w_iz), from four input parameters (arc current, traverse speed, work angle and working distance). The model was assessed on a full-factorial training matrix of 36 experiments and on four pure speed extrapolation experiments above the training range. A composite quality score filter classified 23 of the training experiments as stable and 13 as unstable. On the pure validation set, the mean absolute percentage error (MAPE) was 4.25% (95% confidence interval 0.91–8.49) for the arc core diameter, 6.29% (5.07–7.59) for the arc cone angle, 8.06% (6.08–9.82) for the heat-affected zone width, and 17.02% (10.77–21.62) for the track core width. Classical regression baselines attain comparable aggregate errors on this narrowly distributed validation set; the distinguishing property of the proposed model is the joint, physically ordered prediction of all five outputs. The Ayrton voltage sub-module of the model converged to U(I) = 11.33 + 97.13/I without any direct voltage measurement, purely through the physics loss term; this function is consistent with the order of magnitude expected from the physics of low-current TIG arcs. The results indicate that physics-informed learning can be applied to the PBAAM process parameter space under small-sample conditions. This capability is demonstrated for 316L stainless steel, for the micro-TIG electrode configuration and the process window investigated here, for single tracks rather than multi-layer builds, and against a validation set of four experiments varying in a single direction. Full article

Fischer–Tropsch (FT) wax is widely used as an organic warm-mix asphalt (WMA) additive, lowering binder viscosity during construction while improving high-temperature deformation resistance in service; however, the comparative responses of SBS-modified asphalt to different FT wax grades remain insufficiently understood. Sasobit and three FT waxes with different carbon-chain ranges (FT 80, FT 90, FT 100) were incorporated into SBS-modified asphalt at about 7.0 wt%, and their effects on macroscopic performance, rheology, molecular packing, and diffusion were evaluated using physical-property tests, rotational viscosity, dynamic shear rheometer (DSR) testing, and molecular dynamics (MD) simulation. In the MD analysis, the wax additives were represented by linear alkane molecules with different chain lengths, and the systems were subjected to structural optimization, annealing, and NPT equilibration using the COMPASS III force field before the molecular descriptors were evaluated. The experimental results showed that all four additives produced a trade-off between increased high-temperature stiffness and reduced low-temperature ductility. Sasobit gave the strongest viscosity reduction (>70% above 165 °C), while FT 90 and FT 100 showed more stable, predictable viscosity–temperature behavior favorable for a wider construction window. DSR results showed higher complex modulus and lower phase angle for all modified binders at low frequencies, suggesting an increased elastic contribution and greater resistance to deformation under the tested rheological conditions; FT 80 produced the greatest stiffening but also the largest free volume and loosest molecular packing, whereas FT 100 increased cohesive energy density and reduced free volume, reflecting denser packing and stronger intermolecular cohesion. MD simulations revealed that FT wax enhanced short-time local molecular mobility and segment diffusion in its molten state (explaining the warm-mix viscosity reduction), whereas macroscopic stiffening and ductility loss at ambient temperatures were dictated by wax microcrystallization and physical network constraints that restricted long-range chain relaxation. By comparing three FT wax grades and Sasobit under the same experimental dosage and testing framework, this study provides a controlled assessment of the relationships among wax-grade characteristics, binder-scale rheological responses, and MD-derived molecular descriptors. Full article

Electric vehicle (EV) charging has a large, spatially clustered, schedulable load whose vehicle-to-grid flexibility can be sold back to the power system. That flexibility has grid value only when the committed quantity can be reliably delivered under uncertainty. Open forecasting benchmarks operators rely on report-only point predictions. The dispatch models that turn forecasts into firm commitments assume a constant round-trip efficiency, so the committed flexibility is systematically over-scheduled. This study contributes two complementary modules, validated separately on public data. The first is a calibrated probabilistic charging forecaster that provides, to our knowledge, the first prediction intervals with reported empirical coverage on the UrbanEV benchmark. It is a gradient-boosted quantile-regression model that combines each zone’s own-history lags with adjacency-weighted neighbor-mean features and exogenous price and calendar inputs. It is calibrated by conformalized quantile regression and scored over thirty zones across a 120-day hourly window. The second is a deliverable-flexibility envelope whose returnable-energy bounds are set by measured, state-of-charge- and rate-dependent vehicle-to-grid (V2G) discharge efficiency rather than a constant round-trip number. These bounds are fit to the measured discharge traces of three V2G-capable vehicles in the Esser bidirectional-charging dataset. Chosen as a lightweight, reproducible baseline, the forecaster keeps its prediction intervals within a five-percentage-point coverage tolerance at both the 80% and 90% nominal levels. Measured coverage is 0.823 and 0.911. It also improves on the continuous ranked probability score of its conformalized-point counterpart at matched point accuracy. This calibration holds across the hyperparameter neighborhood and under data deficiency. On the delivery side, a leave-one-vehicle oracle shows the efficiency-aware envelope short-delivers less than the constant-average-efficiency aggregator on held-out vehicles. Its residual shortfall is 1.21% against the aggregator’s 2.03% at the conservative operating point. The margin widens as commitments grow more aggressive and discharges reach the lowest states of charge. Each of these two measured properties, calibrated demand-side uncertainty and state-dependent discharge physics, imposes a material, separately validated constraint on how much contracted EV flexibility can be delivered, a constraint the point-forecasting frontier leaves unaddressed. Full article

The complex, heterogeneous nature of many subsurface environments makes accurate Rate of Penetration (ROP) prediction both critical and challenging for achieving drilling efficiency, cost control, and operational safety. Although artificial intelligence has demonstrated strong potential in extracting nonlinear patterns from drilling and well-log data, its application to heterogeneous formations remains limited by: (i) overreliance on operational parameters that lack formation-physics context, (ii) rigid train–test splits that ignore geological variability, and (iii) heuristic hyperparameter selection practices that are not reproducible. This study presents a geomechanically augmented deep learning framework applied to two vertical wells in a Middle East carbonate-clastic field (Well A: 9375 records, 1000–3370 m; Well B: 4443 records, 1945–3131 m). Five contributions are introduced: (1) a physics-informed input space integrating lithology-specific geomechanical properties (UCS, CCS, Young’s modulus, shear modulus, friction angle), validated against core measurements (R² = 0.79–0.95); (2) a heterogeneity-adaptive train–test partitioning strategy demonstrating that formation complexity, rather than a fixed universal ratio, governs the optimal split; (3) a residual Fully Connected Neural Network (FCNN) with Swish activation and systematic hyperparameter sensitivity analysis; (4) a rigorous preprocessing pipeline comprising 99th-percentile Winsorization, interaction-term feature engineering (WOB × CCS, RPM × UCS), Lasso selection, Z-score normalization, and Gaussian noise augmentation, with all transforms fitted exclusively on training data to prevent leakage; and (5) a hybrid LSTM-FCNN that processes depth-ordered sequences via Savitzky–Golay denoising and a ten-step sliding window. The standalone FCNN achieved R² = 0.8641 (Well A) and R² = 0.9062 (Well B). The LSTM-FCNN improved intra-well accuracy to R² = 0.9877 and R² = 0.9551 and resolved a severe cross-well transfer asymmetry (B → A: R² = 0.0388 for FCNN versus R² = 0.8217 for LSTM-FCNN; A → B: R² = 0.8963), confirming that depth-sequential modeling captures transferable formation patterns across contrasting lithological profiles. Full article

Polyhydroxybutyrate (PHB), a microbial polyester belonging to the polyhydroxyalkanoate (PHA) family, has emerged as one of the most promising biodegradable alternatives to conventional petroleum-derived plastics. Its inherent marine biodegradability (typically mineralizing within months depending on material geometry and ambient temperature), biocompatibility, and ability to be synthesised from renewable and waste-derived feedstocks position PHB as a key candidate for supporting the transition towards a circular bioeconomy. Despite these advantages, widespread commercial adoption remains limited by high production costs, processing challenges, and performance constraints relative to established commodity plastics and competing biopolymers. This review critically evaluates the current state of PHB development from the perspective of sustainable commercialisation. Key aspects discussed include microbial biosynthesis pathways, feedstock selection, upstream fermentation strategies, downstream recovery technologies, and technoeconomic considerations influencing industrial feasibility. The intrinsic thermal, mechanical, and degradation characteristics of PHB are examined alongside modification approaches such as copolymerisation, polymer blending, plasticisation, and composite reinforcement that have been developed to overcome certain inherent physical–mechanical properties, narrow processing windows, and limited functional performance. Furthermore, characterisation methodologies, environmental degradation behaviour, and emerging industrial applications are assessed within the context of market requirements and sustainability objectives. Particular emphasis is placed on identifying the interconnected technical and economic bottlenecks that continue to hinder large-scale deployment, including feedstock costs, fermentation scalability, downstream processing expenses, and material performance trade-offs. By integrating advances across the entire PHB value chain, this review highlights current opportunities, remaining challenges, and future priorities required to enable the sustainable and economically viable commercialisation of PHB-based materials. Full article

While traditional frameworks often simplify fluid dynamics, non-ideal thermodynamic characteristics, driven by intermolecular forces and spatial confinement, significantly alter bulk flow at the nanometer scale. To address this gap, we propose a novel transport model that rigorously couples nanoscale gas slippage with dynamic fluid properties. This approach uniquely integrates varying molecular interactions, seamlessly transitioning from attractive to repulsive regimes, alongside confinement-induced shifts in critical parameters. Taking the deep shale formation in the Sichuan Basin, China, as a representative geological context, accurately modeling methane transport is essential. Our analytical results reveal that incorporating non-ideal thermodynamics profoundly amplifies the predicted boundary slip. Specifically, the corrected mean free path is amplified by a factor of up to 4.7 relative to standard ideal gas assumptions. This enhancement scales strongly with elevated pressure but remains negligible near 1 MPa. Furthermore, we demonstrate that relying on ideal gas laws can inflate nanopore flow capacity estimates by more than 75%, an error primarily driven by density reductions and viscosity increases under high-pressure regimes. We also identify a specific thermodynamic window, sub-10 MPa pressures combined with temperatures below 290 K, where corrected transport metrics actually surpass conventional predictions, an anomaly governed predominantly by intensified slip dynamics. Ultimately, these findings highlight widespread inaccuracies in current permeability estimations, providing a more robust physical foundation for forecasting production and conducting numerical reservoir simulations. Full article

Developing advanced bioinspired photoprotective barrier from marine resources represents a critical frontier of bioprocessing. This study established a rational design and implementation of effective photoprotective surface-coating eumelanin from ink of an Indian squid (Uroteuthis duvaucelii). The Central Composite Design was developed to optimize extraction and functionalization parameters of eumelanin on quartz substrates, strategically developing the matrix for peak optical attenuation within the potential Far-UVC window (220 nm). Translational photoprotective efficacy of the surface, as well as finished eumelanin on quartz surface, was validated by subjecting them to a challenging macro-level biological assay using a hospital-grade 254 nm ultraviolet germicidal source (125 µWcm⁻²). Quantitative physical dosimetry established that the squid eumelanin coating (A254 = 1.00) reduced internal transmittance to approximately 10%, successfully dampening the incident fluence from 0.225 J cm⁻² down to a heavily attenuated 0.0225 J cm⁻² at the biological sample plane. While unshielded control indicator microbial strains suffered complete lethal inactivation, the eumelanin barrier maintained exceptional cell viability, yielding biological shielding efficiencies of 98% for Bacillus subtilis, 96% for Staphylococcus aureus, and 92% for Escherichia coli. Characteristic features from FE-SEM, FTIR, and XRD analysis established that this superior photoprotective property is governed by the extensively conjugated, π-π-stacked indolic architecture possessing a characteristic 3.4 Å interlayer d-spacing, which facilitates rapid, non-radiative energy dissipation. This work establishes an effective framework for translating squid biomass into high-value, transparent optical barriers, providing a potential sustainable alternative to synthetic ultraviolet absorbers. Full article

Woody breast (WB) myopathy is a major quality defect in modern broiler production, but its complex and heterogeneous pathophysiology continues to challenge objective and biologically meaningful detection. This review synthesizes 53 studies identified through a systematic search (January 2020 to May 2026), together with foundational pre-window works cited for context, organized across three main areas: physical and mechanical measurements, biochemical and physiological indicators, and imaging- and artificial intelligence-based approaches. Physical methods provide relatively interpretable measures of tissue properties, including stiffness, electrical behavior, and water mobility. Biochemical and physiological approaches offer greater insight into the mechanisms underlying WB development and may support earlier prediction, although their routine application remains limited. Imaging and AI-based methods appear to be the most scalable options for automated assessment, but their performance is still constrained by limited datasets and imperfect reference standards. Overall, no single modality fully captures the structural, functional, and metabolic complexity of WB. Future advances will require improved quantitative reference frameworks, more robust validation under commercial conditions, and multimodal strategies that better integrate biological relevance with practical applicability. Full article