Search Results (590)

Monitoring the condition of rolling bearings is critical for industrial reliability, yet traditional contact-based accelerometers can be impractical in confined or hazardous environments. This study investigates the use of microphones as a non-invasive diagnostic alternative, focusing on the impact of sensor distance and spatial placement on fault detection sensitivity across various rotational speeds and load conditions. Using an accelerometer mounted directly on the bearing as a benchmark, acoustic data were acquired on a test bench under different speed and load conditions. The experimental setup evaluated three distinct microphone positions and five distances relative to the source to assess spatial influence. Analysis was conducted comparing scalar indicators, such as Root Mean Square (RMS), kurtosis and Crest Factor (CF) values, with advanced diagnostic techniques, specifically the High-Frequency Resonance Technique (HFRT) for envelope spectrum extraction. Results indicate that while the signal-to-noise ratio (SNR) predictably decreases with distance, diagnostic performance is significantly compromised by acoustic shielding effects caused by bearing housing. Moreover, while simple statistical factors (RMS, kurtosis, CF) show limited reliability across varying distances and noise floors, HFRT-based envelope analysis yields robust fault identification even at the maximum sensor distance. The study concludes that optimal microphone placement is essential for reliable remote monitoring. Particularly, these findings suggest that a preliminary spatial characterization of the acoustic field can significantly enhance the effectiveness of non-contact diagnostic systems in industrial applications. Full article

Drought stress poses a significant challenge to food security in sub-Saharan Africa, particularly for smallholder farmers in dryland systems. Bambara groundnut (Vigna subterranea (L.) Verdc.), an underutilised legume with inherent drought tolerance, remains underexplored in terms of its root system traits. This greenhouse study investigated the early root and shoot responses of six Bambara groundnut genotypes under well-watered (100% field capacity) and water-stressed (50% field capacity) conditions using rhizotron-based phenotyping. Significant genotypic differences (p < 0.01) were observed in root traits such as root system depth (RSD: 11.0–19.9 cm), root system width (RSW: 6.96–12.2 cm), and root dry mass (RDM: 0.42–1.27 g). The ARC genotype exhibited a strong drought-avoidance strategy, increasing RSD from 12.2 to 19.9 cm and RDM from 0.42 to 1.16 g under stress. The Tiga Nicuru DIP-C-F7471 genotype showed adaptive plasticity, maintaining deeper roots (11.0–14.5 cm), high convex hull area (CHA), and root–shoot ratio (RSR) values, despite a reduction in RDM, suggesting a resource-conserving strategy. Principal Component Analysis (PCA) captured 93.6% of the total variability among genotypes. Root traits, particularly total root length (TRL), convex hull area (CHA), root system width (RSW), and root dry mass (RDM), were the main contributors to genotype differentiation. Strong positive correlations (r = 0.88–0.97) between root and shoot traits suggest that genotypes with more developed root systems also supported greater shoot growth, highlighting the coordinated response of above- and below-ground traits under drought stress. These findings provide valuable targets for breeding and highlight the value of rhizotron-based screening for root trait selection. Future field validation and full-season studies are recommended to confirm their relevance for improving yield stability in dryland agriculture. Full article

This work presents a mechanistic modeling approach for simulating methane emissions from triethylene glycol (TEG) dehydrators used in oil & gas (O&G) operations. The model was developed as a modular component of the Mechanistic Air Emissions Simulator (MAES) tool, incorporating species-specific absorption and emission dynamics through two-level, second-order polynomial regression (PR) models trained on ProMax simulation data: (1) species-level regression models that track the transfer rates of individual gas species within the dehydrator unit streams, and (2) outlet flow stream regression models that predict the fraction of inlet gas distributed among the outlet streams of the dehydrator unit. These behaviors were characterized over a range of glycol circulation ratios, wet gas pressures, and temperatures. The model was validated using root mean square error (RMSE) analysis. The species-level PR achieved low root mean square error (RMSE) values (<0.03) for light hydrocarbon species across all dehydrator components, ranging from 0.0009 for methane to 0.029 for normal pentane. Similarly, the outlet-level PR yielded RMSE values below 0.002 for the dry gas fraction, 0.001 for the flash tank fraction, and 0.002 for the still vent fraction, demonstrating strong agreement between predicted and reference ProMax values. When deployed at field facilities, the model significantly improved MAES-simulated dehydrator emissions, revealing that gas-assisted glycol pump emissions are the dominant contributors to both dehydrator-level and site-level methane emissions under uncontrolled conditions. Further analysis of the 154 dehydrator units reported by operators under the AMI 2024 project showed that 54 units (31%) used gas-driven glycol pumps, of which 6 units (11%) operated with uncontrolled flash tanks, and 22 units (40.7%) were identified as potentially oversized. Of the six dehydrator units with uncontrolled gas-assisted pumps, pump emissions accounted for 90.25% of total dehydrator emissions and 63.10% of total site-level emissions. These findings highlight substantial opportunities for emissions mitigation through equipment upgrades. Full article

Reliable precipitation data are fundamental for climate and hydrological research, especially in regions with sparse ground-based observations. This study evaluates and compares the accuracy of two satellite-based precipitation products—CMORPH and GPCP—across daily, monthly, and annual scales over Iran. Daily, monthly, and annual precipitation estimates from CMORPH and GPCP were validated against observations from 128 meteorological stations distributed throughout the country. The assessment employed two statistical indices—correlation coefficient (CC) and root mean square error (RMSE)—alongside three categorical indices: probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI). At the daily scale, CMORPH outperformed GPCP in terms of CC, RMSE, POD, and CSI, while GPCP exhibited a lower FAR. At the monthly scale, correlations between satellite-derived and station-based precipitation were stronger than those at the daily scale; CMORPH achieved the highest correlation (CC = 0.84), whereas GPCP yielded a lower RMSE, with a mean value of 26.2 mm. At the annual scale, GPCP demonstrated better performance in CC, while CMORPH showed superior accuracy in RMSE. CMORPH consistently underestimated precipitation, whereas GPCP tended to overestimate rainfall across Iran. Although both datasets provided reliable precipitation estimates at the national scale, CMORPH demonstrated higher overall accuracy and efficiency. Its superior performance across most indices makes CMORPH the more suitable dataset for precipitation monitoring in Iran, despite its tendency to underestimate rainfall relative to ground observations. Full article

As water resources are becoming increasingly scarce in the North China Plain, irrigation strategies that simultaneously improve grain yield and reduce irrigation water input are needed for winter wheat (Triticum aestivum L.) production. Current irrigation decision rules are based either on fixed soil moisture thresholds or on evapotranspiration (ET)-based ratios applied uniformly across the growing season, limiting their flexibility for growth stage-specific irrigation management. In this study, a multi-objective simulation optimization framework was developed to jointly optimize soil moisture lower control limits (irrigation trigger thresholds) and evapotranspiration-based irrigation replenishment ratios across key winter wheat growth stages. The framework integrated the AquaCrop-OSPy crop model with the PyFAO56 soil moisture balance, irrigation scheduling model and the NSGA-II evolutionary optimization algorithm. A field experiment was conducted during the 2024–2025 growing season in Laoling City, Shandong Province, China, employing a four-dense–one-sparse strip cropping pattern with two irrigation treatments: T1 (subsurface sprinkler irrigation) and T2 (shallow subsurface drip irrigation). The AquaCrop-OSPy model was calibrated and validated using measured canopy cover, aboveground biomass, grain yield, and soil moisture content in the 0–60 cm soil layer. Simulated canopy cover and grain yield showed good agreement with observations, with the coefficient of determination (R²) ranging from 0.87 to 0.94. For grain yield, the normalized root mean square error (NRMSE) ranged from 2.24% to 3.75%, and the root mean square error (RMSE) ranged from 0.29 to 0.54 t·ha⁻¹. For aboveground biomass, R² was 0.99, while RMSE ranged from 1.02 to 1.11 t·ha⁻¹, and NRMSE ranged from 14.25% to 15.49%. The PyFAO56 irrigation strategy model simulated average root-zone soil-moisture dynamics with satisfactory accuracy, with an R² of 0.86 and an RMSE of 5%. Multi-objective optimization (maximizing yield while minimizing irrigation volume) generated 23 Pareto-optimal irrigation strategies, with irrigation volumes ranging from 51 to 128 mm, corresponding yields ranging from 9.8 to 10.8 t·ha⁻¹, and irrigation water use efficiency (IWUE) ranging from 0.08 to 0.19 t·ha⁻¹·mm⁻¹. Correlation analysis within the Pareto set indicated that soil-moisture control lower limits during the regreening–jointing stage and higher soil-moisture control lower limits during the flowering–maturity stage were key controlling factors for achieving high yields and irrigation water use efficiency. The Entropy-Weighted Ranked Minimum Distance method identified an optimal irrigation scheme involving two irrigations (one at the end of the jointing stage and another at the beginning of the grain filling stage) involving an irrigation depth of 75 mm, achieving a simulated yield of 10.4 t·ha⁻¹ and an IWUE of 0.16 t·ha⁻¹·mm⁻¹. The proposed AquaCrop-PyFAO56-NSGA-II framework provides a flexible, process-based workflow for jointly optimizing irrigation control thresholds and evapotranspiration-based irrigation replenishment ratios across different winter wheat growth stages. Under the monitored conditions of the 2024–2025 wet season, the framework identified a two-irrigation strategy that balanced grain yield and irrigation input. This study should, therefore, be regarded as a proof-of-concept evaluation conducted in a well-instrumented single-site field setting rather than as a universally transferable recommendation. Because model calibration, within-season validation, and optimization were all based on one wet growing season at one site, the derived stage-specific thresholds, Pareto front, and S5 recommendation are most applicable to hydro-climatic conditions similar to the study year and should be further tested across contrasting year-types and locations before broader extrapolation. Full article

Entrained gas in hydraulic oil undermines system stability. A rapid engineering method for predicting the separation efficiency of gas–liquid cyclone separators is still lacking. This study proposes an engineering-oriented predictive framework by combining the split ratio, the characteristic scale of the locus of zero vertical velocity envelope, and the axial residence time. A relative migration index, derived from maximum tangential velocity and axial residence time, is coupled with a relative overflow-pipe insertion indicator to characterize the interaction between swirl intensity and effective separation space. The separation-capability transition is described using a coupled logistic mapping. Model coefficients are identified via Eulerian–Eulerian simulations on a calibration set. The model was evaluated on isolated simulation validation sets with varying geometries and inlet gas volume fractions, yielding an R² of 0.762 and a root mean square error (RMSE) of 0.07. Particle Image Velocimetry validation tests on one representative prototype geometry gave RMSE values of 0.061 for simulation versus test and 0.108 for prediction versus test. The framework captures the macroscopic trend of separation efficiency within the investigated range, with the caveat that part of the model coefficients and intermediate inputs remain conditioned by simulation-derived quantities. Full article

Biostimulants offer a sustainable strategy to improve plant growth and stress resilience, particularly under limited water availability. We evaluated seven biostimulant treatments, including beneficial bacteria, mycorrhizal fungi, seaweed extract with humic acid, and their combinations, on early growth and physiological responses of tomato (Solanum lycopersicum L.) under well–watered and drought-stressed conditions. Plants were assessed before and after a seven-day controlled drought period using a range of morphological and physiological traits, including height, effective quantum yield of PSII (Φ_PSII), stomatal conductance (g_s), and leaf pigment profile. Results showed that microbial treatments that included Bacteria + Mycorrhizae (B + M) maintained Φ_PSII above 0.60 and preserved height gain relative to the control, while seaweed-based formulations with humic acid (S + H) exhibited significant reductions in height of up to 35% compared with full irrigation. In addition, the bacterial treatment (B) significantly increased the root/shoot ratio under drought, indicating enhanced carbon allocation to roots. These findings demonstrate that specific microbial-based biostimulant combinations can better maintain physiological performance and growth under water limitation, supporting their potential use in sustainable tomato production systems. Full article

Leaf Area Index (LAI) is critical for monitoring cassava growth and yield prediction, yet ground measurements are time-consuming and labor-intensive for large-scale applications. While satellite-based vegetation indices (VIs) offer a scalable alternative, their performance for cassava LAI estimation remains poorly documented, and optimal index selection for different growth stages is unclear. This study evaluated the predictive performance of 13 Sentinel-2-derived VIs for estimating ground-measured LAI across cassava growth stages. Ground-LAI was measured monthly using a SunScan Canopy Analyzer from January to June 2022 (2–7 months after planting; MAP) in 47 cassava plots in Nakhon Ratchasima Province, Thailand. Linear mixed-effects models and stage-specific regressions assessed VI predictive performance using Coefficient of determination (R²) and Root Mean Squared Error (RMSE). The Green Normalized Difference Vegetation Index (GNDVI) and Normalized Difference Water Index (NDWI) demonstrated superior performance across all growth stages (R² = 0.524; RMSE = 0.350), followed by Sentinel-2 LAI Green Index (SeLI R² = 0.521, RMSE = 0.357). Stage-specific analysis revealed that Ratio Vegetation Index performed best during early growth (2 MAP, R² = 0.671; RMSE = 0.164) while GNDVI and NDWI excelled during mid-growth (3–5 MAP) and SeLI at late growth (7 MAP, R² = 0.393; RMSE = 0.422). While the presence of large trees altered the ranking of VI predictive performance, it did not substantially affect estimation errors, suggesting a relatively small impact of spatial heterogeneity on LAI estimation accuracy. These findings identify GNDVI and NDWI as the most operationally suitable Sentinel-2 indices for cassava LAI estimation and demonstrate that stage-specific index selection can improve monitoring accuracy, providing validated tools for regional-scale cassava crop monitoring using freely available satellite data. Full article

Biostimulant applications may alleviate various stresses and improve the yield of crops, thus contributing to the promotion of crop growth and development in agricultural systems. Despite these potential benefits, the effects of biostimulants on root morphological traits remain poorly understood. In the present study, a global meta-analysis of 111 peer-reviewed publications was conducted to quantify the effects of biostimulant applications on various root morphological traits and identify the determining factors. Compared to untreated controls, biostimulant applications significantly increased the primary root length by 14.7%, total root length by 17.7%, root biomass by 24.5%, root activity by 21.7%, root diameter by 4.0%, root-to-shoot ratio by 2.4%, root volume by 25.7%, root surface area by 15.6%, root tips by 15.4%, and root forks by 15.6%. The biostimulant type and crop species were identified as the main moderators of root morphological responses. Among various biostimulants, humic acid showed the most consistent and pronounced positive effects. Additionally, orchard and vegetable crops exhibited greater responsiveness than grain crops. These findings provide quantitative evidence that biostimulants promote root system development across diverse crop species. They also underscore the potential of biostimulants to enhance nutrient acquisition and support more sustainable agricultural production. Full article

Perennial groundcover (PGC) systems integrate perennial grasses with annual crops such as corn (Zea mays L.) to provide continuous soil cover and enhance soil health. However, the proximity to groundcover vegetation can alter light quality perceived by developing seedlings, inducing shade avoidance response (SAR), a phytochrome-mediated developmental response that modifies plant architecture and may compromise yield. Identifying the distance at which SAR is initiated and the extent to which management practices modulate this response is critical for optimizing PGC systems. This growth chamber study aimed to (1) identify the distance at which SAR occurs in corn seedlings, (2) determine whether the thiamethoxam seed treatment mitigates SAR expression, and (3) compare hybrid physiological responses to PGC-induced SAR. The experiment was arranged in a randomized complete block design with four replications across three periods and included two corn hybrids (P1185, P1197), two seed treatments (untreated and thiamethoxam at 0.25 mg seed⁻¹), and four perennial ryegrass (Lolium perenne L.) distances [0, 6, 25 cm, and a control (no-grass)]. Reduced red to far-red light ratios associated with closer proximity to ryegrass induced SAR responses. Corn plants at 6 cm from PGC exhibited significant stem and height elongation beginning at 8 days after planting (DAP), followed by reduced growth by 14 DAP, confirming an early SAR response. Plants grown at 0 cm exhibited reduced height and growth compared to other distances at all growth stages. Hybrid responses differed, and Hybrid P1197 showed enhanced stem elongation, a characteristic SAR response. The thiamethoxam seed treatment did not mitigate SAR. These results indicate that SAR causes stem elongation without altering root or shoot biomass. Full article

Soil salinization has become a major global constraint threatening ecosystem stability and agricultural production. As a prominent salt-tolerant turfgrass, Paspalum vaginatum (seashore paspalum) serves as an excellent material for exploring salt tolerance mechanisms. In this study, PvHAK12, a high-affinity K⁺ transporter (HAK) family gene isolated from seashore paspalum, was functionally characterized. PvHAK12 encodes a 788 amino acid protein with 13 transmembrane domains, belonging to the plasma membrane-localized ion transporters. It exhibits high sequence conservation with other HAK transporters and is predominantly expressed in roots and stems, with distinct tissue- and time-specific induction under salt stress. Yeast complementation assays revealed that PvHAK12 has no obvious K⁺ transport capacity but may mediate Na⁺ transport. Overexpression of PvHAK12 in Arabidopsis thaliana significantly reduced salt tolerance at germination, seedling and rosette stages, as reflected by lower germination rate, fresh weight, survival rate, the maximum quantum yield of photosystem II (Fv/Fm) value and chlorophyll content, accompanied by higher ion leakage. Under salt stress, transgenic plants accumulated more Na⁺ and less K⁺, leading to an elevated Na⁺/K⁺ ratio. Moreover, transgenic lines displayed weaker antioxidant enzyme activities and higher reactive oxygen species (ROS) accumulation. Transcript analysis further demonstrated that PvHAK12 overexpression suppressed the induction of multiple ion-transport and stress-responsive genes under salt conditions. These results indicate that PvHAK12 negatively regulates plant salt tolerance by disrupting ion homeostasis, antioxidant capacity and stress-related gene expression. Full article

This study examines the role of volatility mean reversion in option pricing and evaluates the performance of commonly used volatility estimators within a broad market context. Using a comprehensive dataset of end-of-day option chains for the 100 most actively traded U.S. equities from 2018 to 2023, we apply several established statistical techniques—including unit root tests, variance ratio analysis, Hurst exponent estimation, and GARCH modeling—to quantify the presence and strength of mean reversion in volatility. To assess the accuracy and practical usability of volatility metrics for option valuation, we compare realized volatility, GARCH-based forecasts, range-based estimators, and widely used implied volatility measures such as the VIX and daily implied volatility averages, benchmarking each against contract-specific implied volatility. The results indicate that more than 65% of the analyzed tickers exhibit statistically significant mean-reverting behavior, and that the 30-day average implied volatility consistently provides the most reliable predictive performance among the tested metrics, while range-based estimators perform poorly when applied to end-of-day data. Finally, backtests of six delta-neutral option strategies informed by these findings did not yield consistent profitability or statistically significant outperformance, suggesting that although volatility mean reversion is measurable, its direct application to systematic trading remains challenging. Full article

Based on Jaffer’s (2023) heat engine analysis of natural convection, this investigation mathematically derives a novel, comprehensive formula predicting the natural convective heat transfer from an inclined cylinder given its length, diameter, angle, and Rayleigh number and the fluid’s Prandtl number and thermal conductivity. The present formula was tested with 93 inclined cylinder measurements having length-to-diameter ratios between 1.48 and 104 in nine data-sets from three peer-reviewed studies, yielding (data-set) root-mean-squared relative error values between 1.9% and 4.7%. Full article

Soil salinity is rapidly spreading across agricultural regions and has become one of the most critical constraints on soybean growth, yield, and sustainable production. Despite the central role of transcription factors (TFs) in coordinating plant responses to abiotic stresses, the molecular mechanisms by which RWP-RK domain-containing TFs regulate salt-tolerant responses in soybean remain poorly understood. Our previous genome-wide characterization identified 28 RWP-RK TFs in soybean exhibiting abiotic stress-responsive expression, yet their biological functions under salt stress have not been experimentally validated. Here, we investigated a 981-bp GmRWP-RK1 encoding region and demonstrated its regulatory role in enhancing salt tolerance by activating antioxidant defence, Na⁺/K⁺ homeostasis, and transcriptional control of salt-responsive genes using a cross-species overexpression approach. The two Arabidopsis lines (OE1 & OE4) overexpressing GmRWP-RK1 demonstrated significantly improved salt tolerance, as evidenced by ~18% greater survival and enhanced germination compared to non-transgenic plants under salinity stress. This phenotype was supported by stronger antioxidant protection, as indicated by elevated proline levels, reduced MDA accumulation, and increased SOD and POD activities. At the molecular level, the transgenic lines also showed up-regulated expression of key stress-responsive genes (AtACS10, AtSUMO1, AtGBF1), confirming the regulatory influence of GmRWP-RK1 on salt-adaptation pathways. Consistent with the Arabidopsis results, GmRWP-RK1 overexpression in soybean hairy roots also led to improved salt-stress tolerance by accumulating significantly reduced ROS contents (27.38% lower H₂O₂ and 33.98% lower O₂⁻), and maintained a balanced Na⁺/K⁺ ratio compared to that of non-transgenic hairy roots under salinity. Furthermore, GmRWP-RK1-overexpressing transgenic soybean hairy roots showed increased expression of stress-responsive genes, especially GmATG-5, GmOLP-1, and GmOLP-2. Overall, our results support a possible role of GmRWP-RK1 in soybean salt tolerance and provide a foundation for future functional and breeding-oriented studies. Full article

To address the challenges commonly encountered in civil aircraft operating under multi-mode, strongly coupled closed-loop control—namely scarce fault samples, pronounced distribution shift, and root-cause explanations that are easily confounded by covariates—this paper proposes a root-cause analysis method that integrates ensemble learning with constraint-guided reasoning by large language models (LLMs). First, for Full Authority Digital Engine Control (FADEC) monitoring sequences, a feature system comprising environment-normalized ratios, mechanism-informed mixing indices, and multi-scale temporal statistics is constructed, thereby improving cross-mode comparability and enhancing engineering-semantic expressiveness. Second, in the anomaly detection stage, a cost-sensitive LightGBM model is adopted and a validation-set-based adaptive thresholding strategy is introduced to achieve robust identification under highly imbalanced fault conditions. Furthermore, for Root Cause Analysis (RCA), a “computation–reasoning decoupling” framework is developed: Shapley Additive exPlanations (SHAP) are used to generate segment-level contribution evidence, while causal chains, engineering prohibitions, and structured output templates are injected into prompts to constrain the LLM, enabling it to infer root-cause candidates and produce structured explanations under mechanism-consistency constraints. Experiments on real flight data demonstrate that our method yields an anomaly detection F1-score of 0.9577 and improves overall RCA accuracy to 97.1% (versus 62.3% for a pure SHAP baseline). Practically, by translating complex high-dimensional data into actionable natural language diagnostic reports, the proposed method provides reliable and interpretable decision support for rapid RCA. Full article