Search Results (896)

Plasma nitriding is an advanced method to increase the hardness and wear resistance of different metal parts with complex shapes and geometries. The modelling is an appropriate approach for better understanding and improving such technologies based on multi-physical processes. Mathematical models of the coupled electromagnetic, fluid flow, and thermal processes in vacuum chambers for the low-temperature plasma treatment of metal parts have been developed. They were solved numerically via ANSYS/CFX software for a discretized solid and gas space of a plasma nitriding chamber. The specific electrical conductivity of the gas mixture, containing plasma, has been calibrated on the basis of an electrical model of the chamber and in situ measurements. The three-dimensional fields of pressure, temperature, velocity, turbulent characteristics, electric current density, and voltage in the chamber have been simulated and analysed. Methods for further development and application of the models and for technological and constructive enhancement of the plasma treatment technologies are discussed. Full article

The reliance on unstable hydrogen peroxide (H₂O₂) adversely affects the robustness and simplicity of chemiluminescence (CL)-based immunoassays. We report a novel external H₂O₂-free Fenton CL system integrated into a highly sensitive non-enzymatic immunoassay for the detection of SARS-CoV-2 nucleoprotein, utilizing cuprous–polyethylenimine–lipoic acid nanoflowers (Cu(I)-PEI-LA-Ab NF) as a non-enzymatic tag. The signaling polymer (PEI-LA) was synthesized via EDC/NHS coupling, which conjugated approximately 550 LA units to the PEI backbone. This polymer formed antibody-conjugated NF with various metal ions, and the Cu(I)-based variant was selected for its intense and sustained CL with luminol. The mechanism relies on an in situ Fenton reaction, in which dissolved oxygen is reduced by Cu(I) to H₂O₂, which reacts with oxidized Cu(II), producing hydroxyl radicals that oxidize luminol. Direct calibration of the SARS-CoV-2 nucleoprotein fixed on microplate wells demonstrated excellent linearity in the range of 0.01–3.13 ng/mL (LOD = 3 pg/mL). In a final competitive immunoassay format for samples spiked with the antigen, a decreasing CL signal that correlated with increasing antigen concentration was obtained in the range of 0.1–20.0 ng/mL, achieving excellent recoveries that were favorable compared with those of the sandwich ELISA kit, establishing this H₂O₂-independent platform as a powerful and robust tool for clinical diagnostics. Full article

A novel capacitive interdigital electrode (IDE) sensor for the in-situ measurement of soil moisture is presented. Two planar electrode configurations, spiral and embracing, were designed and evaluated through modeling, simulation, fabrication, and experimental validation. Compared with conventional circular and square electrodes, the proposed structures exhibited higher sensitivity and greater electric field penetration, with the spiral configuration offering the advantage of easier fabrication. The experimental results demonstrated that the calibrated spiral IDE sensor achieved a coefficient of determination (R²) of 0.9976 and a mean squared error (MSE) of 0.859, indicating good stability and repeatability over the tested period. Furthermore, comparison with a commercial moisture sensor showed that the proposed sensor reached a higher R² value of 0.9995, exhibiting closer agreement with gravimetric measurements. These findings confirm that the developed sensor holds strong potential for in situ monitoring of soil moisture and can provide valuable technical support for landslide monitoring and prevention. Full article

Cabbage stubble left in fields after harvest forms a mechanically complex stubble–soil composite that hinders subsequent tillage and crop establishment. Although the Discrete Element Method (DEM) is widely used to model soil-root systems, calibrated contact parameters for taproot-dominated vegetables like cabbage remain unreported. This study addresses this gap by calibrating a novel DEM framework that couples the JKR model and the Bonding V2 model to represent adhesion and mechanical interlocking at the stubble–soil interface. Soil intrinsic properties and contact parameters were determined through triaxial tests and angle-of-repose experiments. Physical pullout tests on ‘Zhonggan 21’ cabbage stubble yielded a mean peak force of 165.5 N, used as the calibration target. A three-stage strategy—factor screening, steepest ascent, and Box–Behnken design (BBD)—identified optimal interfacial parameters: shear stiffness per unit area = 4.40 × 10⁸ N·m⁻³, normal strength = 6.26 × 10⁴ Pa, and shear strength = 6.38 × 10⁴ Pa. Simulation predicted a peak pullout force of 162.0 N, showing only a 2.1% deviation from experiments and accurately replicating the force-time trend. This work establishes the first validated DEM framework for cabbage stubble–soil interaction, enabling reliable virtual prototyping of residue management implements and supporting low-resistance, energy-efficient tillage tool development for vegetable production. Full article

This research focusses on three satellite-derived bathymetry methods and optical satellite instruments: (1) a stereo photogrammetry bathymetry module (SaTSeaD) developed for the NASA Ames stereo pipeline open-source software (version 3.6.0) using stereo WorldView data; (2) physics-based radiative transfer equations (PBSDB) using Landsat data; and (3) a modified composite band-ratio method for Sentinel-2 (SatBathy) with an initial simplified calibration, followed by a more rigorous linear regression against in situ bathymetry data. All methods were tested in three different areas with different geological and environmental conditions, Cabo Rojo, Puerto Rico; Key West, Florida; and Cocos Lagoon and Achang Flat Reef Preserve, Guam. It is demonstrated that all satellite derived bathymetry (SDB) methods have increased accuracy when the results are aligned with higher-accuracy ICESat-2 ATL24 track bathymetry data using the iterative closest point (ICP). SDB vertical accuracy depends more on location characteristics than the method or optical satellite instrument used. All error metrics considered (mean absolute error, median absolute deviation, and root mean square error) can be less than 5% of the maximum bathymetry depth penetration for at least one method, although not necessarily for the same method for all sites. The SDB error distribution tends to be bimodal irrespective of method, satellite instrument, alignment, site, or maximum bathymetry depth, leading to the potential ineffectiveness of traditional error metrics, such as the root mean square error. However, our analysis demonstrates that performing detrending where possible can achieve an error distribution as close to normality as possible for which error metrics are more diagnostic. Full article

This study presents a validated numerical investigation into the seismic liquefaction potential of fine-grained reclaimed sediments commonly encountered in coastal, containment, and reclamation projects. Fine-grained reclaimed sediments pose a particular challenge for seismic liquefaction assessment due to their low permeability, high fines content, and complex cyclic response under earthquake loading. A fully coupled, nonlinear finite element model was developed using the Pressure-Dependent Multi-Yield (PDMY) constitutive framework, calibrated against laboratory Cyclic Direct Simple Shear (CDSS) tests and verified using in situ Cone Penetration Tests with pore pressure measurement (CPTu). The model effectively captured the dynamic response of saturated sediments, including excess pore pressure generation, cyclic mobility, and post-liquefaction behavior, under three earthquake ground motions: Livermore, Chi-Chi, and Loma Prieta. Results showed that near-surface layers (0–2.3 m) experienced full liquefaction within two to three cycles, with excess pore pressure ratios (R_u) approaching 1.0 and peak pressures closely matching laboratory data with less than 10% deviation. The numerical approach revealed that traditional CPT-based cyclic resistance methods underestimated liquefaction susceptibility in intermediate layers due to limitations in accounting for pore pressure redistribution, evolving permeability, and seismic amplification effects. In contrast, the finite element model captured progressive strength degradation, revealing strength gain in deeper layers due to consolidation, while upper zones remained vulnerable due to low confinement and resonance effects. A critical threshold of R_u ≈ 0.8 was identified as the onset of rapid shear strength loss. The findings confirm the advantage of advanced numerical modeling over empirical methods in capturing the complex cyclic behavior of reclaimed sediments and support the adoption of performance-based seismic design for such geotechnically sensitive environments. Full article

Accurate prediction of soil texture is essential for effective soil management, precision agriculture, and hydrological modeling. This study proposes a novel, data-driven approach for estimating soil texture without the need for laboratory-based analysis. High-frequency in situ soil moisture measurements from EnviroSCAN (Sentek Technologies, Stepney, Australia) sensors and satellite-derived vegetation indices (NDVI) from Sentinel-2 were collected across 25 sites in Hungary. Temporal soil moisture dynamics were encoded using a Long Short-Term Memory (LSTM) neural network, designed to capture soil-specific hydrological response behavior from time-series data. The resulting latent embeddings were subsequently used within an ordinal regression framework to predict ordered soil texture classes, explicitly enforcing physical consistency between classes. Model performance was evaluated using leave-one-soil-out cross-validation, achieving an overall classification accuracy of 0.54 and a mean absolute error (MAE) of 0.50, indicating predominantly adjacent-class errors. The proposed approach demonstrates that soil texture can be inferred from dynamic environmental responses alone, offering a transferable alternative to fraction-based regression models and supporting scalable sensor calibration and digital soil mapping in data-scarce regions. Full article

Vector magnetometer arrays are essential for ferromagnetic target detection and MGT measurement, but their performance is limited by proportional factor errors, triaxial non-orthogonality, soft/hard iron interference, and inconsistent array orientations. Traditional rotation-based scalar calibration requires magnetic-free turntables or manual multi-orientation operations, introducing mechanical noise, orientation perturbations, and poor repeatability. This paper proposes an in situ rapid calibration method for MGT systems using triaxial Helmholtz coils. By generating three-dimensional magnetic field sequences of constant magnitude and random directions while keeping the sensors stationary, the method replaces conventional rotational excitation. A two-stage rapid calibration algorithm is developed to achieve individual sensor error modeling and array relative calibration. Experimental results show substantial improvements. The tensor invariant C_T decreased from 6287.84 nT/m to 7.57 nT/m, with variance reduced from 1.46 × 10⁶ to 13.47 nT²/m²; inter-sensor output differences were suppressed to 1–3 nT; and the magnetic field magnitude error dropped from ~940 nT to 3 × 10⁻⁴ nT, achieving a 5–6-order-of-magnitude enhancement. These results verify the method’s effectiveness in eliminating rotational errors, improving array consistency, and enabling high-precision in situ calibration with strong engineering value. Full article

In early 2025, the Jason-3 satellite’s orbit shifted from an “interleaved” to a tandem configuration with Sentinel-6A, and its Geophysical Data Records (GDR) were upgraded from Version F to G. This study evaluated GDR-G via eight processing approaches, using Jason-3’s last six GDR-F cycles (#394–#399) and first six GDR-G cycles (#501–#506), integrating histogram/geographical distribution analyses of Sea Surface Height Anomaly (SSHA), Significant Wave Height (SWH), Wind Speed (WS), and multi-method validation (e.g., self-cross-calibration). Key findings include the following: GDR-G had significantly lower SSHA noise than GDR-F, with up to ~4 cm SSHA bias from different retrackers/corrections; Adaptive retracker + 3D Sea State Bias (SSB) correction achieved optimal accuracy. Adaptive retracker’s SWH/WS anomalies linked to invalid MLE4 results and non-Brownian waveforms (coastal/sea ice). A detrending method was proposed, and the 41-point Lanczos window was optimal for smoothing. The results from the “detrending method” were consistent with the results based on the SSHA spectrum and classic self-cross-calibration methods. A ~5 mm drop was observed in Jason-3 GDR-G MLE4 baseline SSHA, probably caused by GDR upgrade or geographic sampling mismatch, while Sentinel-6A’s GDR-G upgrade might induce ~1 cm jump. The jumps along with GDR version upgrade highlighted the value of timely in situ absolute calibration. Full article

Wellbore instability is a critical challenge in deep coalbed methane (CBM) development, especially for extended-reach horizontal wells subjected to pronounced horizontal in situ stress anisotropy. This study integrates uniaxial and triaxial laboratory testing of deep coal samples with an analytical Mohr–Coulomb-based model to quantify how horizontal stress contrast redistributes near-wellbore stresses and controls collapse pressure. Mechanical parameters from core experiments and log-derived stresses are embedded into the model and applied to six representative horizontal wells in the Ordos Basin. At 2000 m depth, circumferential stress perpendicular to the minimum horizontal stress direction exceeds orthogonal directions by 20 MPa (wells 1–3) and 40–50 MPa (wells 4–6). As the horizontal stress ratio n = ${\sigma }_H$/${\sigma }_h$ (where ${\sigma }_H$ and ${\sigma }_h$ are the maximum and minimum horizontal in situ stresses, respectively) increases from 1.07 to 1.28, the equivalent mud density required to prevent collapse rises from 1.53 to 1.77–1.81 g/cm³, representing a 15–18% increase. These results demonstrate that explicitly accounting for horizontal stress anisotropy—calibrated by uniaxial and triaxial tests—is essential for reliable collapse-pressure estimation in extended-reach wells drilled in deep coal seams, without invoking additional trajectory-optimization assumptions. Full article

Background/Objectives: Cement augmentation of cephalomedullary head elements can improve the purchase of osteoporotic bone; however, it does not eliminate the need for accurate implant positioning or the preservation of sliding. We report the case of an 87-year-old woman who underwent intramedullary nailing with a cement-augmented helical blade for intertrochanteric fracture. Methods: This is a single-patient case report. Calibrated radiographic measurements—tip–apex distance (TAD), calcar-referenced TAD (CalTAD), neck–shaft angle (NSA), and telescoping—were obtained immediately postoperatively and at 4, 7, 12, and 15 months. CT was performed at postoperative week 1 and at failure, and MRI was performed for clinical deterioration. In addition, a targeted narrative review summarizes the evidence on the head-element position, sliding behavior, reduction alignment, and augmentation. Results: Immediate postoperative indices were within the accepted targets: TAD 22.6 mm, CalTAD 22.8 mm, NSA 134°, with the head element inferior on the anteroposterior view and central on the lateral view. Rehabilitation proceeded with full weight bearing as tolerated. Early telescoping was minimal (3.8–3.9 mm). Between 7 and 15 months, progressive varus with shortening of TAD/CalTAD and little additional telescoping was observed, radiographically consistent with relative proximal migration of the head–cement complex and a cleavage plane along the inferior cement mantle, culminating in a subcapital femoral neck fracture with the implant in situ. Emphasis should be placed on accurate implant positioning and preservation of sliding capacity, because cement augmentation alone may not prevent mechanical failure when the implant position or load transfer is suboptimal. Conclusions: Cement augmentation stiffens the interface and reduces micromotion but does not neutralize malposition-induced stresses. Accurate positioning, preservation of sliding, and timely conversion when sliding fails to progress are advisable; these findings are hypothesis-generating from a single case. We propose a position- and sliding-based decision guide to support clinical decision-making; its usefulness remains to be validated in larger studies. Full article

In the bioprocessing industry, real-time monitoring of bioreactors is essential to ensuring product quality and process efficiency. Conventional monitoring methods can satisfy some needs but suffer from calibration drift, limited spatial coverage, and incompatibility with harsh or miniaturized environments. Optical fiber sensors, with their high sensitivity, remote monitoring capability, compact size, and multiplexing, have become a promising technology for in situ bioreactor monitoring. This review summarizes recent progress in optical fiber sensors for key bioreactor parameters, with an emphasis on pH and dissolved oxygen (DO), and briefly covers temperature and pressure monitoring. Different sensing mechanisms, materials, and fiber architectures are compared in terms of sensitivity, response time, stability, and integration strategies in laboratory and industrial-scale bioreactors. Finally, current challenges and future trends are discussed, including multi-parameter sensing, long-term reliability, and the integration of optical fiber sensors with process analytical technology and data-driven control for intelligent bioprocessing. Full article

Precise lagoon bathymetry remains scarcely available for most tropical islands despite its importance for navigation, resource assessment, spatial planning, and numerical hydrodynamic modeling. Hydrodynamic models are increasingly used for instance to understand the ecological connectivity between marine populations of interest. Island remoteness and shallow waters complicate in situ bathymetric surveys, which are substantially costly. A multisource strategy using historical point sounding, multibeam surveys and well calibrated satellite-derived bathymetry (SDB) can offer the possibility to map entirely extensive and geomorphologically complex lagoons. The process is illustrated here for the rugose complex lagoon of Gambier Islands in French Polynesia. The targeted bathymetry product was designed to be used in priority for numerical larval dispersal modeling at 100 m spatial resolution. Spatial gaps in in situ data were filed with Sentinel-2 satellite images processed with the Iterative Multi-Band Ratio method that provided an accurate bathymetric model (1.42 m Mean Absolute Error in the 0–15 m depth range). Processing was optimized here, considering the specifications and the constraints related to the targeted hydrodynamic modeling application. In the near future, a similar product, possibly at higher spatial resolution, could improve spatial planning zoning scenarios and resource-restocking programs. For tropical island countries and for French Polynesia, in particular, the needs for lagoon hydrodynamic models remain high and solutions could benefit from such multisource coverage to fill the bathymetry gaps. Full article

Buildings account for a substantial share of global energy use, with cooling and heating systems contributing significantly to this demand. Conventional fixed setpoint temperatures overlook occupants’ thermal adaptability, often resulting in unnecessary energy consumption. Although adaptive setpoint temperatures have been investigated in residential and conventional office buildings, their applicability to public buildings, where occupancy is highly variable and indoor–outdoor thermal exchange occurs frequently, remains insufficiently explored. This study examines the performance of an adaptive cooling setpoint strategy in a public building in South Korea through simulation and in situ evaluation. A calibrated simulation model was used to compare cooling energy consumption between fixed and adaptive setpoint temperatures. Simulations indicated an overall 9.0% reduction in cooling energy use, with monthly savings exceeding 11.0% during cooling-dominant months. Validation results confirmed a 7.7% daily energy reduction, while survey results verified that occupant thermal comfort was maintained. The study findings indicate that adaptive thermal comfort-based setpoint temperature control shows promise for effective application in public buildings with similar operational characteristics, improving energy efficiency without compromising occupant comfort. This approach offers a practical pathway for sustainable HVAC operation in buildings with dynamic occupancy and operation features. Full article

Educational chatbots are increasingly deployed to scaffold student learning, yet educators lack scalable ways to assess the cognitive depth of these dialogues in situ. Bloom’s taxonomy provides a principled lens for characterizing reasoning, but manual tagging of conversational turns is costly and difficult to scale for learning analytics. We present a reproducible high-confidence pseudo-labeling pipeline for multi-label Bloom classification of Socratic student–chatbot exchanges. The dataset comprises 6716 utterances collected from conversations between a Socratic chatbot and 34 undergraduate statistics students at Nanyang Technological University. From three chronologically selected workbooks with expert Bloom annotations, we trained and compared two labeling tracks: (i) a calibrated classical approach using SentenceTransformer (all-MiniLM-L6-v2) embeddings with one-vs-rest Logistic Regression, Linear SVM, XGBoost, and MLP, followed by per-class precision–recall threshold tuning; and (ii) a lightweight LLM track using GPT-4o-mini after supervised fine-tuning. Class-specific thresholds tuned on 5-fold cross-validation were then applied in a single pass to assign high-confidence pseudo-labels to the remaining unlabeled exchanges, avoiding feedback-loop confirmation bias. Fine-tuned GPT-4o-mini achieved the highest prevalence-weighted performance (micro-F1 $=0.814$), whereas calibrated classical models yielded stronger balance across Bloom levels (best macro-F1 $=0.630$ with Linear SVM; best classical micro-F1 $=0.759$ with Logistic Regression). Both model families reflect the corpus skew toward lower-order cognition, with LLMs excelling on common patterns and linear models better preserving rarer higher-order labels, while results should be interpreted as a proof-of-concept given limited gold labeling, the approach substantially reduces annotation burden and provides a practical pathway for Bloom-aware learning analytics and future real-time adaptive chatbot support. Full article