Search Results (786)

Low-rank ultrafine flotation clean coal typically yields filter cake moisture above 20% due to abundant oxygen-containing functional groups and strong surface hydrophilicity. Conventional polyacrylamide (PAM) flocculants are hydrophilic and improve dewatering only by altering cake porosity, not by reducing particle surface hydrophilicity, so they remove little adsorbed water. In this study, hydrophobically modified anionic polyacrylamides (HMAPAM) were synthesized by grafting lauryl acrylate onto APAM. FTIR, ¹H NMR, XPS, and SEM confirmed the grafting and progressive enrichment of hydrophobic alkyl chains on the surface. Moderate hydrophobic modification markedly improved solid–liquid separation. HMAPAM-D (APAM/LA = 4.5:0.5) achieved a settling velocity of 0.817 cm/s at 9 mg/L, 50.2% higher than APAM, and reduced filter cake moisture to 16.64% at 1 mg/L under 0.6 MPa versus 19.39% for unmodified APAM. Excessive modification (HMAPAM-E, 4:1) promoted intramolecular self-association, producing heterogeneous flocs and higher filtration resistance that degraded dewatering efficiency. The performance gain stems from hydrophobic association combined with adsorption bridging. These results clarify how hydrophobic group content controls flocculation and dewatering, informing the design of better flocculants for this type of coal slurry. Full article

Conventional multi-mission altimetry fusion tends to attenuate short-wavelength sea surface height anomaly (SLA) signals when high-density two-dimensional SWOT observations are incorporated into a single smoothing framework. To address this limitation, this study proposes a scale-separated, scale-wise fusion framework for high-resolution SLA reconstruction that jointly exploits multi-mission nadir altimetry and SWOT wide-swath observations. Multi-mission Level-3 observations from Sentinel-3A/B, HY-2B, SARAL/Altika, and SWOT are first harmonized through quality control, spatiotemporal reference unification, and cross-calibration referenced to Jason-3; Jason-3 was not used as a fusion input; instead, it served as the cross-calibration reference and as an external validation source after excluding calibration-involved samples. The SWOT-observed SLA field is then decomposed using an 80 km Lanczos filter—chosen as a practical working scale reflecting SWOT’s effective resolution rather than a universal physical boundary—into a large-scale background component and a mesoscale–submesoscale perturbation component. The large-scale component is reconstructed using adaptive optimal interpolation with latitude-dependent covariance scales, whereas the mesoscale–submesoscale component is refined through a physically regularized Transformer-based learning branch that recovers organized sub-80 km variability as a relative enhancement with respect to the AVISO/CMEMS reference. The two components are finally recombined on a 0.08° × 0.08° grid to generate a global SLA product. Validation from August 2023 to August 2024 shows that the proposed product maintains strong large-scale consistency with AVISO/CMEMS, with a mean daily spatial correlation of approximately 0.85. Sample-independent cross-validation against concurrent Jason-3 along-track observations yields a mean daily RMSE of 4.9 cm. Regional case studies in the Kuroshio Extension and the Scotia Sea further show that, relative to a conventional unified fusion scheme, the proposed framework better preserves organized sub-80 km structures, including fronts, eddy boundaries, and filamentary features, without degrading the large-scale background. Two specific technical contributions are (i) a reproducible scale-separated workflow that decouples large-scale OI mapping from fine-scale learning-based reconstruction, and (ii) a physically regularized loss formulation that constrains spatial gradients and Laplacian smoothness to suppress nonphysical artifacts during small-scale enhancement. These results suggest that scale-separated fusion provides an effective and operationally practical strategy for next-generation high-resolution SLA products and for improved observation of dynamically significant short-wavelength ocean variability. Full article

Bone densitometry in osteoporosis diagnosis via dual-energy X-ray absorptiometry (DXA) can benefit from advances in imaging detector technology. We devised a compact imaging scanner—DXA4A—using a photon-counting and energy-sensitive Timepix4 hybrid pixel detector (512 × 448 pixels, 55 µm pitch), for areal bone mineral density (aBMD) assessments in the distal radius and tibia in the clinic and for future in-flight astronauts’ bone health assessment. We present the design and Monte Carlo simulations of the scanner. A Timepix4 detector with a 1 mm thick CdTe sensor was tested in the laboratory with X-ray tube sources, acquiring first images of test samples. Monte Carlo simulations were implemented for scanner design and performance prediction, using 50 kVp unfiltered and 100 kVp Sm K-edge filtered spectra. With a digital twin of the scanner and patient wrist, we set up a virtual imaging study and determined the aBMD in the forearm of a patient (0.515 ± 0.048 g/cm²), in agreement with the clinical DXA value (0.571 g/cm² for the total forearm). This study highlights the feasibility of realizing a compact DXA scanner for the distal tibia and radius with spectral capabilities, exploiting Timepix4 hybrid detectors for its peculiar energy sensitivity and photon event timing properties for tissue identification. Full article

Soil salinization threatens agricultural sustainability and food security, especially in arid and semi-arid regions, yet how salinity gradients reshape multi-trophic networks and their associations with functional genes remain unclear. We investigated soil bacteria, fungi, protists, nematodes, and the carbon-fixation gene cbbL along a natural salinity gradient (electrical conductivity: 1.2–12.4 mS cm⁻¹) in Karamay, Xinjiang. Salinity acted as a key environmental filter, significantly differentiating biotic communities into low- and high-salinity groups. Compared with bacteria and fungi, protists and nematodes exhibit higher sensitivity to salinity shifts from non-saline to slightly saline soils, with their Shannon diversity decreasing by 74.2% and 50.4%, respectively (p < 0.05). High salinity significantly reduced the connectivity, modularity, and robustness of soil multi-trophic co-occurrence networks, resulting in 36.8% fewer edges, 24.2% lower modularity, and diminished network robustness compared to low-salinity conditions. Crucially, salinity was associated with functional decoupling, defined as a shift in the dominant drivers of microbial carbon sequestration potential. At low salinity, biotic factors explained 94.2% of cbbL variation, whereas at high salinity abiotic factors governed 86.1%, as shown by GBM (Gradient Boosting Machine) analyses. Our findings indicate that protists and nematodes can act as early warning indicators for soil salinization, and biotic network complexity represents a core metric for assessing saline soil ecosystem stability. This study reveals salinization-induced biota–function decoupling patterns and provides insights for saline soil health assessment and biotic restoration. Full article

Large-scale language models can produce anomalous outputs that cannot be explained solely by the quality of input data. This article presents a systematic review and descriptive quantitative synthesis of published evidence on reasoning anomalies in LLMs. The study does not report original experiments, does not evaluate new model outputs, and does not implement the proposed framework. Instead, it consolidates numerical results manually extracted from published papers, public benchmarks and official system cards, with the derived datasets and figure-generation scripts. The review organizes reasoning-related anomalies into a taxonomy that distinguishes factual hallucinations, self-contradictions, unfaithful Chain-of-Thought traces, semantic rollback, snowball errors, distractor susceptibility and sycophancy bias. Published evidence indicates that irrelevant context can reduce accuracy below 30% in controlled mathematical reasoning settings, and that knowledge and reasoning tasks can differ by more than 12 percentage points in reported biomedical benchmarks. Existing mitigation techniques, including self-consistency, semantic entropy, process reward models and formal verification, are compared descriptively across heterogeneous studies and domains. Formal verification results, such as the 91.7% reported for VERGE on AR-LSAT, are explicitly limited to structured logical reasoning and should not be generalized to open-domain natural language reasoning. Finally, the article proposes a four-layer conceptual architecture grounded in access-consciousness and monitoring-consciousness operators, C_A(t) and C_M(t), for filtering context, generating reasoning paths, monitoring inferential discrepancies and activating selective correction. The framework is presented as an implementable research roadmap whose empirical validation, computational overhead and activation thresholds remain future work. Full article

The photoreceptor bacteriorhodopsin (HsBR) from Halobacterium salinarum is a model system for studying ultrafast photoinduced reactions in proteins. Recent time-resolved serial femtosecond crystallography (TR-SFX) experiments require high pump energies, raising concerns about nonlinear excitation and multi-photon effects. Here, we systematically investigate the influence of excitation energy, pulse duration and the sign of the chirp on the initial HsBR photo-reaction using femtosecond Vis-pump IR-probe spectroscopy in the retinal C=C stretching region. An acousto-optic programmable dispersive filter enabled independent control of pulse energy and chirp. Within the tested range, the retinal dynamics were independent of pulse duration and chirp, indicating that fluence alone does not fully describe excitation conditions. Increasing excitation energy leads to nonlinear saturation of the retinal signals and the appearance of an additional band near 1550 ${\mathrm{cm}}^{-1}$. However, this band rises linearly with the excitation energy. Hence, the additional band is not directly caused by non-resonant multi-photon absorption. Spectral decomposition reveals two components: a low-energy contribution consistent with the known retinal isomerization dynamics and a high-energy contribution attributed to a small population of photo-damaged HsBR likely formed via a resonant two-photon process. These findings clarify the role of excitation conditions in ultrafast HsBR spectroscopy and suggest that spectral changes at high pump energies mainly arise from damaged species upon resonant two-photon excitation. Full article

To address wellbore instability and the technical challenges associated with high-density water-based drilling fluid loss control in deep shale gas formations of the Sichuan-Chongqing region in China, a novel nano-micro sealant designated CLG-Seal was synthesized via molecular structural optimization. The molecular structure of newly developed CLG-Seal exhibits distinct core–shell structural characteristics. The inorganic nano-silica constitutes the rigid core of CLG-Seal, which guarantees its plugging performance. The hydrophobically associating polymer which is coated on the surface of nano-silica constructs the flexible shell of CLG-Seal, endowing the CLG-Seal with excellent gel-forming capacity, adhesion film-forming capacity, deformability and perfect dispersibility. Transmission electron microscopy and scanning electron microscopy were employed to characterize the morphology of the CLG-Seal nanomicron-scale plugging agent. The sealing performance and underlying mechanisms of CLG-Seal were subsequently evaluated via particle plugging apparatus tests, displacement experiments, and etched glass micromodel simulations. Field trials conducted in the third section of Well WY3-2-3HF validated the application effectiveness of this agent in drilling fluid systems. The results indicate that the nano-micro sealant CLG-Seal exhibits a median particle size of D₅₀ is 146 nm, which can be modulated by adjusting the synthesis conditions. The nano-micro sealant CLG-Seal significantly mitigates fluid loss in low-permeability microfractures and fissures. Notably, a concentration of merely 3% is sufficient to achieve optimal nano-micro plugging performance. The results of the mechanism study indicate that while the CLG-Seal particles are close to each other, the polymer chains with flexible long chain structure which are coated on the surface of nano-silica constructs tend to be intertwined, forming a cross-linked network structure of gel film, thereby increasing the interaction between nano-micron particles and forming an impermeable plugging film. In addition, due to the nanoscale effect, the CLG-Seal has a strong tendency to adsorb onto the surface of shale rock through hydrogen bonding with the shale matrix. The hydrophobically associating polymer with high elastic modulus and excellent mechanical properties can enhance the pressure-bearing capacity of the filter cake through elastic deformation. Therefore, these nano-micron particles can form a strong sealing film on the filter cake and at the micropores of shale rock, thereby creating a dense mud cake on the outside of the shale formation. Field trial results demonstrate that the incorporation of the nano-micro sealant CLG-Seal into the drilling fluid for the third section of Well WY3-2-3HF reduced the PPA fluid loss to 4.6 mL. This value represents a substantial reduction compared to adjacent wells and signifies a remarkable improvement over the drilling fluids previously employed in the Longmaxi Formation of this block. Furthermore, the treated drilling fluid exhibited a superior filtration control pressure capacity of 10.5 MPa. The operation was completed successfully without any lost circulation or wellbore instability, and achieved a drilling footage of 42 h with an average penetration rate of 7.81 m/h. The mud weight was reduced by approximately 0.08–0.10 g/cm³ compared to offset wells. These results confirm the excellent application efficiency of the newly developed CLG-Seal in field operations. Full article

In China, tillage depth is a core performance indicator for certificating tillage machinery. The current manual measurement in field suffers from high subjectivity and poor traceability. This study proposed a tillage depth detection method called MSKe_PC_Transformer (Multi-Scale Kalman-enhanced Physical constraint Transformer). Multi-scale Kalman filtering extracts macroscopic trends, mesoscale fluctuations, and microscale details from soil penetration resistance sequences to construct a multi-scale feature representation. An attention-gating mechanism dynamically and adaptively fuses these features across scales. A physical constraint loss function based on prior knowledge of soil mechanics ensures that the model’s output conforms to the laws of soil mechanical behavior. Using custom-developed equipment, 99 sets of laboratory data and 300 sets of field data were collected for training and testing the MSKe_PC_Transformer model, which achieved an accuracy of 92.59% and a recall of 90.35%. Ablation experiments confirmed the contributions and necessity of each module. In field tests conducted in two regions, the accuracy rate for detection errors less than 1.5 cm was 93%, with the MAE and RMSE 1.03 cm and 1.19 cm, respectively. The results confirm the feasibility of deploying the proposed method as an objective and traceable alternative to manual inspection in tillage machinery certification. The established framework is extendable to other implements, such as subsoilers and moldboard plows, supporting the broader standardization of agricultural machinery certification in China. Full article

Historic building documentation requires both complete spatial coverage and reliable geometric detail, but a single surveying technique often cannot meet both requirements in complex heritage scenes. This study proposes a robustness-oriented TLS–UAV point cloud registration and fusion workflow for historic building documentation. The workflow combines feature-based coarse registration with an improved point-to-plane ICP strategy incorporating normal consistency, radiometric correspondence filtering, dynamic distance thresholds, and multi-resolution refinement. The method was evaluated using the Yao’an Lu Junmin Zongguan Fu, a timber–brick courtyard complex in Yunnan, China, under small, medium, and large initial perturbations. Under small and medium perturbations, Point-to-Plane ICP achieved lower RMSE values, while the proposed method produced comparable results. Under large perturbation, the proposed method achieved the highest success rate and the lowest RMSE of 119.0 cm, indicating stronger robustness under challenging initialization. The fused TLS–UAV model achieved checkpoint-based RMSE values of 1.73 cm horizontally and 0.75 cm vertically. Spatial deviation maps showed that residual errors were mainly concentrated around roof edges, eaves, wall corners, and roof–facade transition zones. Cross-scene validation on the Church of Agios Mamas dataset achieved a registration RMSE of 1.2 cm without parameter adjustment. The results show that the proposed workflow is suitable for offline conservation-oriented documentation where registration robustness, model completeness, and component-level geometric interpretation are required. Full article

Methodological variability remains a major source of uncertainty in environmental microplastics (MPs) analysis, particularly for micro-Fourier transform infrared (μ-FTIR) spectroscopy operated in reflection mode. This study quantitatively evaluates how key analytical procedures influence recovery efficiency and identification performance in μ-FTIR-based MPs analysis. Polystyrene (PS) standard particles (90 μm and 30 μm; density 1.05 g/cm³) were employed in direct titration and standard addition experiments. The effects of filter materials, rinsing suspensions, filtration approaches, and identification protocols were systematically assessed. Under the tested reflection-mode μ-FTIR conditions, silicon and stainless-steel filters provided sufficient spectral readability and microscopic particle visibility for PS particle recognition and were therefore selected for subsequent recovery evaluation. Rinsing with 50% ethanol reduced aggregation and enhanced recovery. Pump-assisted filtration (200 mmHg) achieved high PS recovery (88 ± 7.25%), and standard addition in river samples reached 91 ± 14.5%. Manual calibration using reference standards further improved classification consistency. These findings quantitatively link procedural choices to recovery and identification outcomes, providing practical guidance to improve reliability and inter-study comparability in μ-FTIR-based MPs analysis. Full article

Global climate change and extreme precipitation events increasingly challenge urban infrastructure resilience, particularly in topographically vulnerable regions like Taiwan. Traditional flood monitoring relies heavily on the manual visual interpretation of extensive surveillance networks, a process that imposes high cognitive loads and risks delayed emergency responses. This study presents a comprehensive Cyber-Physical System (CPS) architecture for an automated Water Image Monitoring Platform. Integrating approximately 10,000 cameras and multi-modal data—including precipitation records and spatial alerts—the platform leverages advanced semantic segmentation (DeepLabV3+ with Xception71) to delineate inundation boundaries. To ensure robustness under adverse conditions such as low illumination, fog, and specular glare, we implemented targeted optimizations, including HSV pre-processing, Deblur GAN architectures, and attention mechanisms. Results demonstrate a significant performance evolution, with the event recall rate rising from 88% in 2022 to 99.7% by 2025. A key driver of this success is the synergy between stationary nodes and vehicle-mounted CCTV units, which provide critical dynamic geographic coverage. Furthermore, the deployment of edge computing reduced warning latency 10 times—from 19.2 to 2 s—while virtual water level gauges maintained a mean error within ±10 cm. Despite these gains, a Human-in-the-Loop (HITL) architecture remains strategically necessary for ethical accountability and error filtering. This CPS provides a foundational model for autonomous, resilient urban disaster management. Full article

Root zone soil moisture (RZSM) is critical for understanding hydrological processes and monitoring agricultural drought, yet its accurate representation remains challenging in topographically complex regions. Using 40 cm in situ SM observations from 19 ground stations in Yunnan Province, China, during 2008–2012 as the reference, this study systematically evaluated the performance of five widely used multi-source soil moisture (SM) products and their different depth layers, including ERA5-Land, GLDAS Noah, GLEAM, ASCAT H141, and CCI SM. A CCI-derived RZSM proxy generated by exponential filtering, hereafter CCI RZSM, was also included. Product performance was assessed using original and deseasonalized time series, and the effects of land-use type, long-term wetness background, and short-term dry conditions on product performance were explicitly examined. The results showed that the intermediate and deeper layers of ERA5-Land and ASCAT H141, especially the 7–28 cm layers, exhibited better performance in capturing RZSM dynamics, achieving a favorable balance among temporal correlation (r > 0.6), random error and systematic bias. Surface-layer products showed limited direct representativeness, and effective RZSM representativeness differed substantially among nominal product layers. Deseasonalization showed that original-series correlations were partly supported by the shared seasonal wet–dry cycle, whereas most products had weaker skill in tracking non-seasonal RZSM anomalies. Environmental background substantially modulated error structures: stronger positive Bias generally occurred at drier stations, Grassland showed higher positive Bias, Cropland showed greater dispersion, and Forest displayed relatively balanced performance. Under dry conditions, temporal correlations declined for nearly all products, whereas increases in random error were mainly concentrated in surface layers. Exponential filtering improved the temporal consistency of CCI SM in representing RZSM, but the filtering with a fixed characteristic time parameter (T) performed worse than filtering with station-optimized T, indicating limited generalizability in ungauged regions. Overall, RZSM representativeness in Yunnan is jointly controlled by product structure, environmental background, and wet–dry conditions. ERA5-Land and ASCAT H141 intermediate-to-deep layers are therefore more suitable for RZSM anomaly and drought applications in Yunnan Province. Full article

Capillary imbibition is the process by which liquids are absorbed into porous materials as a result of capillary pressure differences at the pore scale. Accurate characterization of imbibition dynamics, particularly in the presence of gravitational potential, is essential for understanding fluid transport in diverse systems such as soil, fractured rocks, filtration media, and plant roots. This study presents systematic imbibition experiments using filter papers with pore sizes of 2.5 µm, 11 µm, and 20 µm, each inclined at 80° to quantify the influence of gravitational potential on imbibition behavior. For horizontally positioned samples, the imbibition front propagated radially and symmetrically, exhibiting a power law dependence on time. The measured temporal exponents ranged from 0.386 to 0.403, consistently lower than the theoretical value of 1/2 predicted by the Lucas–Washburn law. With increasing permeability, the temporal exponent approached the Washburn limit, indicating a marked dependence of imbibition dynamics on pore structure. For the inclined configuration at an 80° angle, the imbibition fronts remained nearly circular but exhibited a pronounced displacement of the front center toward gravity. This displacement increased with permeability, from approximately 0.497 cm for the 11 µm filter paper to 3545 cm for the 20 µm filter paper, highlighting the combined effects of permeability and gravitational potential on fluid movement. Furthermore, the advance of the imbibition front was significantly slower in the smallest pores (2.5 µm) compared to the larger ones. Experimental results were evaluated against a theoretical model proposed by Medina, demonstrating moderate quantitative agreement at early times, when gravitational potential effects are less significant. These findings confirm that both the temporal scaling exponent and the spatial evolution of the imbibition front are governed by the porous medium’s permeability and inclination angle, providing experimental evidence of deviations from ideal Washburn behavior in real porous systems. Full article

High-quality water surface elevation (WSE) measurements are critical in hydrological applications, yet no systematic evaluation of the Surface Water and Ocean Topography (SWOT) mission exists for Brazil’s diverse lake systems, where satellite observations are essential given limited in situ monitoring. We evaluated WSE from SWOT over 132 Brazilian lakes, comparing LakeSP, Raster_250m, and Raster_100m products against field measurements over a 20-month period. The 68th percentile errors were under 29 cm for the full dataset, below 12 cm for Flag = 0, and below 21 cm for Flag = 1, indicating good agreement but also the presence of outliers and the need for data screening. A Random Forest analysis identified quality flags, lake geometry, and cross-track distance as key drivers of WSE precision. Flag = 0 is overly restrictive, retaining only 22% of observations, while Flag = 1 contains anomalous data. The SWOT Quality-Range Threshold for Lakes (SQRTL) filter combines Flag = 0 with cross-track constrained Flag = 1 observations. SQRTL more than triples data availability relative to Flag = 0, maintaining comparable precision (68th percentile below 16 cm) and reducing median revisit from 88–123 days to 16–18 days for raster products and from 25 to 14 days for LakeSP. These results provide the first large-scale SWOT WSE evaluation over Brazilian lakes and a transferable filtering framework applicable wherever SWOT and field observations overlap, with potential to extend monitoring to over 100,000 water bodies in the SWOT Prior Lake Database. Full article

The work environment in automated laboratories and industrial sites exposes workers to the risks associated with chemical gas and vapor leaks caused by unforeseen incidents. Such leaks may result in severe health hazards as well as damage to equipment or infrastructure at the leak site. Therefore, the development of systems capable of early detection and highly accurate localization of chemical leaks is of high importance for occupational safety. In this work, a low-cost, portable sensor node based on the Internet of Things (IoT) is proposed for the detection and localization of gas and chemical leaks in indoor environments. The sensor node features a modular design that enables flexible integration and replacement of gas and environmental sensors depending on the target application. In addition, the system includes an ultra-wideband (UWB)-based positioning and tracking unit, allowing operation across multiple indoor zones. The main contribution of this work lies in the combined integration of (i) multi-sensor-based environmental event detection and prediction and (ii) high-precision location within a dynamic multi-zone tracking architecture. The system automatically selects the most relevant anchors in each zone and applies trilateration and least-squares estimation, enhanced by Kalman filtering techniques. In particular, an extended Kalman filter (EKF) and an unscented Kalman filter (UKF) are employed, with sensor fusion incorporating inertial measurement unit (IMU) data to mitigate the effects of on-line-of-sight (NLoS) conditions and signal degradation caused by obstacles. Experimental results demonstrate that both the EKF and UKF significantly reduce positioning errors and improve tracking stability compared to baseline methods under challenging indoor conditions. The UKF shows superior performance in highly nonlinear scenarios. A quantitative evaluation using manually surveyed reference points showed that the UKF achieved the best overall performance, with a mean error of 39.72 cm and an RMSE of 43.03 cm. These findings confirm the effectiveness of Kalman filter-based sensor fusion for reliable indoor positioning and highlight the suitability of the proposed system for real-time safety monitoring applications. Full article