Search Results (468)

Improving surface-soil carbon accumulation is important for restoring reclaimed alpine mining soils. We evaluated nine treatments in the Muli mining area, Qinghai, China: a control (CK) and eight sheep manure cake rates (0.025–0.060 m³·m⁻²). Soil total organic carbon (TOC), particulate organic carbon (POC), microbial biomass carbon (MBC), dissolved organic carbon (DOC), 0–10 cm soil organic carbon (SOC) stock, carbon pool management index (CPMI), carbon fixation genes (cbbL and cbbM), and microbial community sequencing profiles were assessed. Sheep manure cake significantly increased the measured carbon fractions and CPMI. The 0–10 cm SOC stock was highest under SM3 (75.82 ± 3.52 Mg C ha⁻¹), followed by SM4 (65.99 ± 3.49 Mg C ha⁻¹), compared with 20.74 ± 1.52 Mg C ha⁻¹ in CK. cbbL and cbbM gene copy numbers peaked under SM2 (0.030 m³·m⁻²), reaching 1.51 × 10⁹ and 1.91 × 10⁷ copies·g⁻¹, respectively. Under the prespecified equal criterion-layer weights, TOPSIS ranked SM2 first; however, sensitivity analysis showed that the leading treatment changed when criterion weights were altered. Thus, SM3–SM4 showed the strongest measured surface-carbon accumulation, whereas SM2 was the highest-ranked treatment only within the adopted TOPSIS weighting scheme. Full article

Global Navigation Satellite Systems (GNSS) signals are delayed by the atmosphere, and the resulting zenith total delay (ZTD) provides valuable information on atmospheric water vapour for numerical weather prediction (NWP). Despite the demonstrated benefits of GNSS ZTD assimilation, the southern part of the Meteorological Cooperation on Operational Numerical Weather Prediction (MetCoOp) domain remains sparsely observed, particularly along the main southwesterly moisture-transport pathway into Fennoscandia. This study investigates the impact of assimilating additional ZTD observations from northern Germany—provided by the Helmholtz Centre for Geosciences (GFZ)—into the MetCoOp system. By extending ZTD coverage upstream of the forecast domain, the study addresses a documented gap in previous MetCoOp assimilation research, which has largely focused on densely observed regions or event-specific cases. Since moisture transport into Fennoscandia is climatologically dominated by southwesterly flow from the North Sea, particularly during summer, strengthening ZTD coverage in the southern MetCoOp domain therefore provides critical upstream constraints on humidity and temperature advection. Using the convection-permitting High-Resolution Limited Area Model—Applications of Research to Operations at Mesoscale (HARMONIE–AROME) model coupled with the Surface Externalisée scheme (SURFEX), two experiments were run for June–August 2025: a baseline configuration and one including GFZ ZTDs. Assimilating GFZ data significantly refines the mid-to-lower-tropospheric moisture field (500–925 hPa). Although domain-averaged differences remain modest—specific humidity variations of ~1.3 g kg⁻¹ and temperature deviations near 1 K—spatial analyses reveal sharper moisture gradients and improved boundary-layer structure over land. These findings demonstrate that enhanced upstream ZTD density provides valuable constraints on moisture advection and latent-heat-related processes, thereby improving short-range humidity analyses in a high-resolution NWP system and complementing earlier studies conducted in Fennoscandia. Full article

The Lower Kura Basin is a tectonically active sedimentary region of Azerbaijan where geological and hydrogeological conditions may favor geothermal fluid circulation. This study develops a Geographic Information Systems (GIS)-based multi-criteria framework for regional geothermal favorability mapping by integrating five spatial criteria: land surface temperature (LST), fault density, slope, drainage density, and elevation. LST was derived from cloud-masked Landsat 9 Collection 2 Level-2 surface-temperature products acquired during summer 2025, while the topographic and hydrological criteria were prepared using a 30 m digital elevation model. All thematic layers were standardized to a common 30 m grid and reclassified into five favorability classes using explicitly defined classification rules. Criterion weights were determined using the Analytic Hierarchy Process (AHP), and the resulting pairwise comparison matrix yielded a consistency ratio of 0.024, indicating acceptable internal consistency. LST and fault density received the highest weights because they represent surface thermal expression and potential structural controls on subsurface fluid circulation, respectively, whereas slope, drainage density, and elevation were treated as secondary or indirect criteria. The baseline model identified 3681.9 km², corresponding to 27.71% of the study area, as having high or very high geothermal favorability. Two alternative weighting scenarios were subsequently evaluated to assess the sensitivity of the model to criterion weights. Despite substantial changes in the weighting scheme, 3624.0 km² (27.27% of the study area) consistently remained within the high or very high classes across all three scenarios and were therefore interpreted as robust geothermal favorability zones. Geological consistency was assessed through spatial comparison with regional geological evidence and mapped mud-volcano occurrences, while faults were not treated as an independent validation dataset because fault density was included as a model criterion. The proposed framework supports the identification of priority areas for further geothermal investigation and may contribute to the broader development of clean and renewable energy resources in Azerbaijan. The identified zones represent priority areas for further geological, hydrogeological, geophysical, geothermal-gradient, and exploratory investigations rather than confirmed geothermal resources. Full article

Selecting physics parameterizations for meteorological simulations is typically guided by univariate metrics, which are computed separately for each variable and station and frequently yield contradictory rankings and force subjective decisions. Although the Mahalanobis distance (MD) has previously been used for multivariate analysis, to the best of our knowledge, it has not previously been used as a decision criterion for selecting physics parameterizations in meteorological simulations. This study proposes the MD as a single multivariate performance metric that is dimensionless, invariant to measurement scales, and corrects for inter-variable correlations. The framework was demonstrated using 40 meteorological simulations combining microphysics, radiation, surface-layer, planetary boundary layer, and cumulus schemes, evaluated against hourly observations of temperature, relative humidity, and wind at surface stations in the Biobío region, south-central Chile. The traditional univariate metrics nominated different optimal configurations depending on the variable and station. In contrast, the MD identified a single configuration attaining the minimum distance at three of the four stations and the minimum cross-station mean, and revealed that performance was governed primarily by the surface-layer/PBL pairing rather than by microphysics or radiation choices. The MD thus provides an objective, reproducible, and easily interpretable criterion for parameterization selection, directly applicable to meteorological inputs for air quality modeling. Full article

Accurately simulating convective initiation (CI) in capped High Plains dryline environments remains a significant challenge for convection-permitting numerical weather prediction. As a follow-up work to Lu and White, this study utilizes the Model for Prediction Across Scales (MPAS) at a 3 km grid resolution to evaluate the sensitivity of dryline morphology and CI to two planetary boundary layer (PBL) parameterization schemes: the non-local Yonsei University (YSU) and the local Mellor-Yamada-Nakanishi-Niino (MYNN) frameworks. Radar observations and simulated maximum reflectivity show that while the YSU scheme successfully replicates the timing and spatial development of convective cores triggered along the elevated terrain slope at 21:30 UTC, the MYNN scheme completely suppresses deep convection throughout the study period. Vertical thermodynamic profiles indicate that YSU establishes a deeply mixed boundary layer that weakens the regional capping inversion, enabling surface parcels to break the stable lid and reach their level of free convection (LFC). Conversely, the MYNN scheme confines moisture to a thin layer near the surface beneath an unyielding temperature inversion, preventing parcels from achieving free buoyancy. For the 3 km “grey zone” of turbulence resolution, both PBL schemes successfully resolve horizontal convective rolls (HCRs) near the primary dryline boundary. YSU’s non-local mixing permits these HCR perturbations to couple vertically into deep, cap-breaching updraft plumes, while MYNN’s local turbulent kinetic energy (TKE) closure traps them as shallow horizontal waves. It was shown that the MYNN failure is driven by an intrusive synoptic wind bias, generating anomaly wind velocities of 24–28 m/s throughout the column. These winds act as a mechanical sweeper across the terrain slope which shears, flattens, and dilutes the moisture pool below 2000 m Mean Sea Level (MSL) and physically reduces fuel from the western initiation zone. In contrast, the YSU scheme maintains a well-regulated, moderate wind profile (8–12 m/s aloft), preserving a state of mesoscale equilibrium that allows moisture to ascend the terrain slope and continuously feed developing convective cells. Our findings demonstrate that the choice of PBL parameterization plays significant role in not only local vertical mixing but also the structural translation of macroscale synoptic forcing versus localized thermodynamic regulation in complex terrain. Full article

High-frequency induction hardening enhances the surface wear resistance and contact fatigue life of high-speed linear guides, but simultaneously produces an inhomogeneous, layered cross-sectional structure comprising a high-strength, low-ductility outer hardened layer and a low-strength, high-ductility inner core. This structural heterogeneity renders conventional straightening stroke prediction models—predicated on homogeneous material assumptions—fundamentally inadequate. Moreover, the iterative trial-bending operations ubiquitous in industrial practice progressively accumulate plastic strain, causing guide rails to exhibit erratic positive-to-negative deflection reversal during sequential straightening passes. To address these critical challenges, this study proposes a novel two-stage hybrid straightening strategy based on inverse finite element analysis (FEA) and closed-loop experimental feedback. An equivalent hardened layer depth (HD₀) is introduced as a parametric descriptor to construct a layered elastoplastic finite element model, and an inverse simulation strategy is developed to generate a comprehensive three-dimensional stroke–residual deflection prediction dataset encompassing both vertical and lateral straightening conditions across multiple support spans. Displacement-controlled three-point bending experiments validate the layered model and elucidate the mechanism by which cumulative plasticity progressively amplifies cross-sectional plastic sensitivity under repeated loading. Grounded in this physical insight, a hybrid straightening algorithm is formulated, combining dataset-driven initial stroke prediction for rapid large-deformation elimination with an upper-bound constraint and a measurement-feedback-driven sequential reduction compensation scheme for fine-tuning. Comparative experiments demonstrate that the proposed strategy effectively suppresses the oscillatory over-straightening characteristic of conventional empirical trial-and-error approaches, consistently reducing residual deflection below 0.05 mm within two to three loading cycles. This work bridges the gap between theoretical simulation and the complex physical state of actual machining, substantially improving both the efficiency and precision of straightening for guide rails with induction-hardened layers. Full article

Cross-chain bridges represent one of the most damaging attack surfaces in decentralized finance, with major exploits (e.g., Ronin, Wormhole, Nomad, Multichain) arising not from broken signature schemes but from failures in proof verification, replay protection, and signer-set management, gaps that conventional threshold-signature multisignature wallets do not address. This study presents an incident-aware multisignature architecture combining three on-chain predicates—block-height freshness windows, epoch-bound signer sets, and Merkle inclusion-proof verification—with a non-authoritative off-chain LightGBM classifier that generates SHAP-attributed risk explanations to support governance actions such as pausing, vetoing, or rotating signers, without directly blocking or approving execution. The framework was evaluated on a simulated benchmark of 78,600 Ethereum testnet transactions containing six injected anomaly classes (gas spikes, nonce jitter, malformed call data, stale intents, proof-delivery delays, and epoch-rotation replays). The LightGBM advisor achieved ROC-AUC 0.92 (95% CI [0.906, 0.926]) and F1 0.73 ([0.712, 0.749]), outperforming five baselines—logistic regression, Random Forest, XGBoost, isolation forest, and a rule-based detector—with the highest F1 (0.731) and PR-AUC (0.799), while the rule-based detector, which by construction covers only the anomaly classes addressed by the deterministic predicates, attained F1 0.282. Differences were statistically significant except for the LightGBM–XGBoost PR-AUC comparison. The deterministic layer itself is verified through 28 property-level contract tests covering all seven modeled attack objectives, with measured per-function gas costs (execute_Intent: 118,756 gas, of which 28,432 gas is Merkle-proof verification). Within this controlled setting, the results indicate that a machine learning advisor can extend anomaly-prioritization coverage beyond the scope of the deterministic predicates while leaving execution control fully deterministic. This work is presented as a controlled proof of concept: the reported metrics quantify recovery of scripted injection patterns, and validation against real-world exploit traces remains future work. Full article

Accurately simulating the spatiotemporal distribution of global atmospheric CO₂ remains challenging yet essential for reducing uncertainties in carbon source-sink inversions, quantifying the climate effects of heterogeneous CO₂ fields, and supporting the development of CO₂ observation networks. In this study, we simulated global atmospheric CO₂ concentrations (2010–2019) at a horizontal spatial resolution of 1° × 1° using the Aerosol and Atmospheric Chemistry Model of the Institute of Atmospheric Physics (IAP-AACM) without data assimilation, with initial fields and flux data from the CarbonTracker CT2022 (CT2022) reanalysis product. The simulations were comprehensively evaluated against CT2022 and observations from ground-based (NOAA GML), airborne (ObsPack), and satellite (OCO-2) platforms. The results indicate that across all evaluated surface stations, CT2022 exhibits poorer overall statistical performance (R = 0.69, RMSE = 5.37 ppm, MB = 2.05 ppm) primarily due to noticeable overestimations at unassimilated ground stations, while IAP-AACM maintains robust performance across the surface network (R = 0.84, RMSE = 2.62 ppm, MB = 0.28 ppm). Vertically, airborne observations across eight global campaigns confirm that IAP-AACM accurately reproduces the vertical distribution of CO₂, maintaining strong correlations (R = 0.72–1.00) and performance comparable to the CT2022 reanalysis (R = 0.86–1.00). In terms of total column CO₂ concentrations (XCO₂), IAP-AACM exhibits strong agreement with satellite retrievals annually (R = 0.97, RMSE = 1.08 ppm, MB = 0.26 ppm), with seasonal metrics remaining consistently robust across all four seasons (R = 0.96–0.97, RMSE = 0.98–1.20 ppm, MB = 0.13–0.37 ppm), demonstrating large-scale transport fidelity on par with the CT2022 reanalysis. Finally, across representative ObsPack land sites, unassimilated IAP-AACM achieves a high median correlation (R = 0.97), low error (RMSE = 2.01 ppm), and low mean bias (MB = −0.45 ppm), closely approaching the assimilated CT2022 reanalysis product (R = 0.98, RMSE = 1.40 ppm, MB = −0.07 ppm). Further analysis indicates that the optimized IAP-AACM exhibits robust performance under stable boundary layer conditions, where the revised diffusion scheme produces higher vertical diffusion coefficients that help mitigate excessive near-surface CO₂ accumulation during nighttime. Overall, the optimized IAP-AACM effectively simulates the spatiotemporal distribution of global atmospheric CO₂, serving as a reliable tool to support advanced research. Full article

Organic pollutants in industrial wastewater present a severe threat to both the environment and human health. Photocatalytic technology, recognized for its eco-friendliness and high efficiency, has become a leading approach for degrading such pollutants. In this work, BiOCl nanosheets were first synthesized using a hydrothermal method. Subsequently, an anion exchange reaction with TAA in an oil bath generated a Bi₂S₃ intermediate layer on the BiOCl surface, followed by the in situ growth of ZIS nanostructures, successfully constructing a BiOCl@Bi₂S₃@ZIS double Z-scheme heterojunction. By adjusting the amount of BiOCl, the interface contact and dispersion of the heterojunction were optimized. Characterization results demonstrate that the BiOCl@ZIS-25 heterojunction possesses the highest specific surface area (103.5 m²·g⁻¹) and the most efficient charge separation. Under visible light irradiation, it achieved 97.88% degradation of methylene blue within 20 min, with a reaction rate constant 8 and 4 times higher than those of pure BiOCl and ZIS, respectively. Mechanistic investigations indicate that Bi₂S₃ interlayer acts as an electron-transfer bridge between BiOCl and ZIS, establishing a double Z-scheme charge transfer pathway that significantly enhanced the separation and utilization efficiency of photogenerated charge carriers. This study offers valuable insights for designing highly efficient and stable photocatalytic composite materials. Full article

Angle-resolved photoemission spectroscopy (ARPES) clearly resolves surface-localized (SL) states, yet conventional density functional theory (DFT) band structures from slab calculations do not readily distinguish weakly confined SL states on surfaces such as Al(001), as traditional layer-threshold criteria fail for surfaces with long surface-state decay lengths. We establish a dual-criterion scheme using the surface ratio R and the localization integral L weighted by the inelastic mean free path (IMFP) to quantitatively distinguish SL states: R quantifies the surface-projected charge fraction, while L incorporates the photoelectron IMFP to mimic ARPES surface sensitivity, both evaluated within a fully converged 81-layer Al(001) slab that eliminates artificial inter-surface coupling. Band structures color-coded by R and L intuitively highlight SL states as bright yellow-white bands against red bulk backgrounds. Our calculations show that continuum SL features arise from multi-band hybridization (sharp surface resonances). Notably, R and L alone cannot separate absolute surface states from resonances. All DFT calculations were performed using the PBEsol exchange-correlation functional within the GGA framework, the PAW formalism, and an 81-layer Al(001) slab model. This work reveals the electronic nature of surface features on Al(001) and provides a quantitative SL-state identification tool that is conceptually transferable to other crystalline surfaces. Full article

The global demand for hydrogen peroxide (H₂O₂) continues to increase, and photocatalytic H₂O₂ production is regarded as a promising alternative technology due to its mild, safe, and environmentally friendly characteristics. ZnIn₂S₄ has demonstrated promising application potential in photocatalytic H₂O₂ production owing to its unique two-dimensional layered structure and broad spectral response. However, its performance is severely limited by rapid charge recombination and sluggish charge migration. To address this challenge, a ZnIn₂S₄/C₃N₄ S-scheme heterojunction was successfully constructed via a simple oil-bath method by assembling ZnIn₂S₄ nanoflowers on C₃N₄ nanosheets. Systematic structural characterizations and performance evaluations demonstrate that the construction of the S-scheme heterojunction effectively promotes the spatial separation and surface migration of photogenerated charge carriers, thereby significantly enhancing photocatalytic activity. Under optimal conditions, the ZIS/CN-10 sample (C₃N₄ to ZnIn₂S₄ mass ratio of 10%) achieves the highest photocatalytic H₂O₂ production rate of 825.8 μmol g⁻¹ h⁻¹. This work provides new insights and theoretical guidance for the rational design of efficient and stable ZnIn₂S₄-based photocatalysts. Full article

Coastal upwelling and typhoon-driven mixing can cool a buoy record by several degrees within hours. Sensor faults do the same. Quality-control schemes that flag anomalies by residual magnitude alone therefore risk discarding real events. We analysed 30 min temperature records from six buoys and three depths off the east coast of Korea, spanning 2008–2024, and built a machine-learning-assisted, physics-guided, event-preserving quality-control framework that adds new labels without altering any observation or existing flag. Cooling events were catalogued from changes in the observed surface temperature and in the surface-to-bottom temperature difference and classified using three physically interpretable axes: spatial coherence with neighbouring buoys, vertical consistency between layers, and atmospheric forcing from ERA5 and typhoon best-track data. A station-wise ridge prediction model, fitted to the surface layer at five of the six stations, supplied prediction residuals that served only to flag candidates. Residual magnitude separated sensor-like anomalies from regional-event candidates poorly (direction-free AUC 0.52–0.56); upwelling-candidate and typhoon-related events produced residuals as large as those of the sensor-like reference group, or larger. The physical axes showed much stronger internal operational separability, reaching pairwise AUC values up to 1.000 and a multivariate cross-validated mean AUC of 0.987. These values do not represent external validation because the groups were partly defined using the same axes. The framework preserved regional-event candidates while affecting derived monthly means by at most about 0.0005 °C, yet retained event-scale cooling of up to about 8 °C. Prediction residuals are therefore useful for broad anomaly-candidate detection but insufficient for final event classification, which should rely on physically interpretable, multi-station criteria. Full article

This study evaluates high-silica fiber/silica aerogel composites (HSFACs) for the passive fire protection of bridge cables. The primary objective is to reveal the high-temperature degradation mechanism of HSFAC and quantitatively determine a reliable thickness scheme for long-term hydrocarbon-fire protection of bridge cables. HSFAC specimens were heat-treated and characterized by thermal conductivity, tensile testing, SEM/TEM, FTIR, and TG analysis. A self-built furnace was used to assess an HSFAC-based cable protection system under hydrocarbon-fire exposure. Increasing heat-treatment temperature enlarged the pore and particle sizes of HSFAC and reduced its thermal-insulation performance. During 120 min of fire exposure, the cable protected by a single 5 mm HSFAC layer reached 300 °C within 45 min, whereas the cable protected by a double-layer 5 + 5 mm HSFAC system remained below 300 °C throughout the test. Finite element simulations validated against the experimental results confirmed that increasing HSFAC thickness improved thermal protection. After 90 min, the predicted cable-surface temperatures were 556 °C and 314 °C for HSFAC thicknesses of 5 mm and 10 mm, respectively. By integrating high-temperature material characterization, HC-fire testing, and thickness-dependent numerical analysis, this study links material degradation to system-level fire performance and provides a quantitative basis for HSFAC thickness design. Full article

To address high-precision trajectory tracking challenges in multi-joint robots facing model uncertainties, complex friction, and abrupt disturbances, this paper proposes a radial basis function (RBF) neural network-improved integral terminal sliding mode control scheme optimized by state-aware adaptive particle swarm optimization (ASPSO), denoted as ASPSO-optimized RBF-IITSMC. First, a fractional-memory integral terminal sliding surface incorporating a boundary-layer saturation mapping is constructed. The proposed terminal mapping is shown to be globally Lipschitz continuous, and an explicit approximation-error bound relative to the conventional terminal power mapping is established. Second, an RBF neural compensator driven by the sliding variable is incorporated into the reconstructed sliding dynamics to estimate lumped uncertainties and reduce the compensation burden on the robust feedback term. Furthermore, a state-aware adaptive PSO variant combining population-diversity monitoring and differential mutation is developed to jointly tune the 15-dimensional controller parameter vector. The practical finite-time reachability of the sliding variable and the uniform ultimate boundedness of the sliding variable and neural-weight estimation error are analyzed using a Lyapunov framework. Simulation results on a six-degree-of-freedom (6-DOF) robotic arm demonstrate improved tracking accuracy and disturbance-rejection performance, together with reduced high-frequency torque oscillations, compared with the evaluated baseline controllers. Full article

Dynamic Positioning systems are crucial for modern marine vessels to maintain positions or track trajectories under environmental disturbances. Traditional model-based and neural network control schemes often suffer from heavy computational burdens, low-velocity nonlinearities, and chattering near decision boundaries during waypoint transitions, which can trigger actuator saturation. To address these challenges, this paper proposes an enhancing robust adaptive control algorithm. Specifically, a model-free control framework is developed by employing an asynchronous deep Actor–Critic neural network with multi-layer perceptron for high-precision policy approximation in continuous spaces. To accelerate convergence, an online meta-driven radial basis function network is proposed for adaptive reward shaping, optimized by the Adam scheme. Furthermore, at the guidance level, a hysteresis state machine and an adaptive damping reference model are designed to decouple wave-induced high-frequency chattering and eliminate thrust saturation. By applying dynamic surface control, the proposed scheme avoids complex thrust allocation calculations. The proposed method enhances system autonomy and ensures smooth transient behavior while maintaining compatibility with standard marine hardware. Full article