Search Results (46,159)

The increasing scale and structural flexibility of modern wind turbine rotors have made real-time monitoring and active control of blade tip deflection a critical requirement for ensuring operational safety, particularly regarding blade-tower clearance. Since direct measurement through physical sensors is often impractical due to high costs, installation difficulties and maintenance challenges, this work proposes a data-based framework for out-of-plane blade tip deflection estimation. The approach introduces a systematic and hierarchical input selection framework that evaluates sensor signal groups, ranging from standard SCADA measurements to configurations including auxiliary nacelle/tower sensors and dedicated blade-root instrumentation. By combining Spearman correlation and spectral coherence, the proposed framework ensures consistent representation of key turbine dynamics across all operating regions. This framework provides a structured trade-off between implementation feasibility and estimation fidelity, enabling tailored solutions for applications such as structural health monitoring and safety-critical active control. Compact Feedforward Neural Network (FNN) and Time-Delay Neural Network (TDNN) architectures, whose hyperparameters are optimized via Bayesian optimization, are employed to achieve high estimation accuracy while preserving computational efficiency. Evaluated through high-fidelity aeroelastic simulations of the NREL 5 MW turbine using the industry-standard FAST (Fatigue, Aerodynamics, Structures, and Turbulence) tool across all operating conditions, the approach achieves $R^2=0.894$ using SCADA-only inputs, $R^2=0.973$ when augmented with nacelle and tower-top sensors and a peak fidelity of $R^2=0.989$ using blade-root bending moment data. These results demonstrate that high-fidelity virtual sensing is attainable without blade instrumentation, providing a viable pathway for real-time tip clearance monitoring and fatigue mitigation. This directly enhances the operational resilience of wind energy systems and their contribution to the stability of renewable-dominated power grids. Full article

Seed germination and early root growth are decisive for crop establishment, yet responses to ionic environments can be strongly genotype-dependent. This study evaluated the effect of supplementing an agar-based in vitro system with a commercial NPK fertilizer on the germination dynamics and early seedling traits of Raphanus sativus L. Seeds were tested in two solid media: A (1.3% agar, no fertilizer) and AF (1.3% agar supplemented with 0.45 g of granular NPK fertilizer (15–30–15) per 200 mL medium), using a completely randomized 3 × 2 factorial design. Germination percentage and synchrony are key constituents of seedlot evaluation because they jointly capture both viability and the temporal coordination of emergence. However, final germination percentage alone does not reflect the timing and uniformity of germination, which can be critical for predicting establishment and subsequent performance. Therefore, indices such as mean germination time (MGT), coefficient of velocity of germination (CVG), and interval germination rates are frequently employed to describe germination dynamics. In addition to germination dynamics, early seedling morphometry (e.g., root and hypocotyl traits) can provide complementary information on early vigor and stress sensitivity under contrasting media or environmental conditions. Root elongation was significantly reduced by fertilization in ASD and GE, whereas AS exhibited consistently shorter roots with no significant response. PCA summarized 86.3% of the total variance in the first two components, separating treatments along a vigour/architecture axis and a germination capacity axis (%G), and hierarchical clustering identified five response groups. Overall, a low-cost agar + fertilizer system effectively discriminated genotype-specific sensitivity to an ionic environment during early establishment, highlighting the need to consider variety-dependent thresholds when using commercial fertilizers for in vitro screening. Full article

Drought reduces soil moisture and impairs root function, posing a significant threat to rice production in arid regions. The influence of soil amendments on early rice root development under semi-dry cultivation remains insufficiently characterized, especially when assessed using non-destructive rhizotron techniques. This study employed a scanner-based rhizotron system to evaluate early root responses of rice seedlings to six amendments under semi-dry irrigation: vermicompost and peat moss, spirulina powder, gypsum, rice husk biochar, zeolite, and an unamended control. The vermicompost plus peat moss (VC+PM) treatment demonstrated the highest water-holding capacity (26%), root projected area (9.60 cm² plant⁻¹), and root surface area (84.79 cm² plant⁻¹). VC+PM also promoted extensive lateral branching (233 secondary and 1709 tertiary roots) and the greatest total lateral root length (363.09 cm plant⁻¹), resulting in superior biomass (shoot: 140.00 mg plant⁻¹; root: 56.70 mg plant⁻¹) and the lowest root-to-shoot ratio (0.90). These improvements are attributed to the enhanced moisture retention of peat moss and the nutrient and phytohormone contributions of vermicompost. In contrast, rice husk biochar exhibited the lowest water-holding capacity (14%), while other amendments produced moderate or limited effects. The results establish a direct relationship between improved soil water retention and early-stage drought-avoidant root development. The combination of VC and PM emerges as a promising approach to enhance root plasticity and seedling establishment in water-saving rice systems. As this study was conducted under controlled rhizotron conditions and limited to the seedling stage (20 days after sowing), future research should prioritize multi-season field trials to assess yield translation and economic feasibility assessments to support farmer adoption. Full article

Current treatments for ulcerative colitis (UC) often fail to adequately address its multifactorial pathogenesis, which involves oxidative stress, barrier dysfunction, and gut microbiota dysbiosis. This study evaluated the therapeutic potential and multi-targeting mechanism of a ginseng, salvia root, and notoginseng oral solution (GSNS) in a mouse model of colitis induced by dextran sulfate sodium (DSS). Based on high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) technology, 25 major bioactive components were identified. Following the induction of colitis with 3.5% DSS in C57BL/6J mice, the animals were treated with the GSNS (40, 80, or 160 mg/kg/day) or 5-Amino Salicylic Acid (5-ASA). The therapeutic efficacy was assessed via disease activity, histopathological staining, cytokines and oxidative stress analysis, and a barrier integrity test. Combined data from Western blot, qPCR, immunohistochemistry, electron microscopy, and 16S rRNA sequencing indicate that the therapeutic effect of the GSNS against colitis is attributable to its dual role in dampening pro-inflammatory cytokines and potentiating antioxidant defenses via the Nrf2/HO-1 signaling pathway. It also upregulated Occludin expression, repaired tight junctions, and was associated with beneficial alterations in the gut microbiota, as evidenced by increased Prevotellaceae and suppressing Escherichia-Shigella. These findings demonstrated that the GSNS exerts a multi-target effect against colitis by synergistically enhancing antioxidant defense, repairing the intestinal barrier, and modulating microbial ecology, supporting its potential as a promising natural compound-based candidate for DSS-induced colitis treatment. Full article

In the social sciences, the mobilities paradigm studies people in movement in contrast to a sedentary paradigm that treats dwelling as being at peace and at home. If the world of movement in places such as stations, hotels, motorways, resorts, and airports is seen as parallel to the sedentary world, it raises a question of whether there are any forms of sacred space equivalent to those found in fixed settlements. In the world of movement, in general, religious activity is lacking or takes attenuated or simplified forms. Sacred affordances can, however, be found in unexpected places. Dedicated sacred space is mostly absent except for multifaith spaces (MFS), a new form of sacred space that has evolved since the year 2000. One way to understand MFS is to see them as adapted to the mobile world in the same way that other services have been commodified for universal use in a world in motion. Full article

Disparities in root fungal endophyte (RFE) communities are well documented among plant species, yet differences among plant functional groups (PFGs) remain unclear. Given that RFE community structure is influenced by host plant abundance and species-specific root functional traits, and that PFGs exhibit divergent relative abundances and root traits, we hypothesize that PFGs harbor unique RFE communities, potentially aligned with their functional traits. We investigated RFE communities in 45 alpine meadow species representing four PFGs (grasses, legumes, dicot forbs, and monocot forbs), using high-throughput sequencing. Ascomycota dominated all groups (>50%) except monocot forbs (38.9%). Distinct differences in the RFE community species composition were found among PFGs. In particular, the differences were significant between dicot forbs and monocot forbs, and between monocot forbs and grasses, which contradicted with conventional PFG classification that combined monocot and dicot forbs as a single PFG. Moreover, marker operational taxonomic units (OTUs) with symbiotic lifestyles were more abundant in legumes, and their functional composition differed significantly from grasses. Roots’ nitrogen concentration was the strongest predictor of RFE variation, followed by root length, biomass, and species abundance. These results emphasize the importance of integrating microbial partners into understanding plants’ functional diversity and ecosystem resilience in alpine environments. Full article

Accurate estimation of vehicle sideslip angle is vital for the stability and safety of four-wheel independent drive electric vehicles (4WIDEVs), but it faces challenges, including model uncertainties caused by tire yaw stiffness variations and system delays. This paper proposes a novel adaptive fusion strategy that combines the dynamic robust observer (DRO) and the improved adaptive square-root unscented Kalman filter (ASUKF). The DRO is designed based on a two-degrees-of-freedom vehicle model and ensures stability through linear matrix inequalities (LMIs), effectively handling parameter uncertainties and time delays; the ASUKF utilizes a three-degrees-of-freedom model and the magic formula tire model, combined with Sage–Husa adaptive filtering, to address the nonlinear tire dynamics. The key innovation of this paper is the introduction of a fuzzy-rule-based adaptive weighting mechanism that dynamically adjusts the fusion weights of the DRO and ASUKF in real time, thereby exploiting their complementary advantages under uncertainty and nonlinear conditions. The simulation and experimental validations demonstrate that this method significantly improves estimation accuracy, reducing the estimation error of vehicle sideslip angle by an average of 9.36%, and maintains robust performance and dynamic adaptability in various conditions, providing a reliable solution for the real-time state estimation of intelligent electric vehicles. Full article

The upper atmospheric microwave sounding channels data are important for atmospheric data assimilation and retrieval. However, radiative transfer simulation accuracy is constrained by the precise characterization of the Zeeman splitting effect. This study investigates key influencing factors in upper-atmospheric microwave radiance simulations, focusing on the geomagnetic field parameters and the Zeeman splitting absorption coefficients. A three-dimensional (3D) atmosphere-magnetic coupling dataset is constructed using the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) version 2.0 Level 2A atmospheric profiles and the International Geomagnetic Reference Field (IGRF-13) as input for the microwave Line-by-Line (LBL) model. Observations from Special Sensor Microwave Imager/Sounder (SSMIS) channels 19 and 20 are used to quantitatively compare the effects of 2D and 3D geomagnetic fields on simulations and evaluate the impact of updated Zeeman splitting coefficients. Quantitative analysis reveals that the average vertical attenuation rate of geomagnetic field strength between 50 and 0.001 hPa is 2.98%, and using 3D magnetic field parameters improves the observation and simulation bias (O-B) for SSMIS channels 19 and 20 by approximately 3.67% and 3.52%, respectively. The updated microwave LBL model, incorporating molecular self-spin interactions and higher-order Zeeman effects, reduces the mean absolute error (MAE) and root mean square error (RMSE) of the SSMIS channel 20 by approximately 2.7% and 2.25%, respectively. Experimental results indicate that the 7⁺ line within a 2 MHz frequency shift is sensitive to moderate magnetic field strength (0.35–0.55 Gauss), while the 1⁻ line is sensitive to strong magnetic fields (0.5–0.7 Gauss). This study demonstrates that optimizing geomagnetic field representation and Zeeman splitting coefficients can improve upper atmospheric microwave radiance simulation accuracy by detailed comparison with observations. Full article

Rural households in cold regions still rely heavily on coal for cooking and domestic hot water, while single renewable energy sources suffer from intermittency and limited system-level assessment. This study proposes a solar–biogas complementary energy supply system integrating evacuated-tube solar collectors, an above-ground anaerobic digester, thermal storage, and biogas utilization for rural residential applications in Minqin, Northwest China. A dynamic system-wide model was developed by coupling TRNSYS with nonlinear representations of anaerobic fermentation and biogas boilers, enabling hour-by-hour simulation of energy production, conversion, storage, and consumption. Field measurements were used for validation, and the root mean square deviation between simulated and measured temperatures and gas production remained below 10%. During the heating season, the solar subsystem supplied 10% of the digester heating demand and 90% of the domestic hot-water load, while the biogas subsystem contributed 9.29% and 90.71%, respectively. The system delivered 4728.96 MJ of heat against a seasonal demand of 4636.22 MJ, fully meeting user requirements. A comprehensive 3E (energy–environment–economic) assessment shows that, compared with traditional rural energy supply modes, the proposed system reduces CO₂ and NOx emissions by 65.85% and 98.13%, respectively, and demonstrates favorable economics with a benefit–cost ratio of 2.41 and a discounted payback period of 3.27 years. The proposed modeling and evaluation framework provides a replicable solution for clean energy substitution and circular waste utilization in rural areas. Full article

Ciprofloxacin (CIP) is an antibiotic that is widely used in humans and animals. However, the compound has been detected in animal-derived products and the environment due to its extensive use, causing serious concern for public health and environmental safety. The issue raises the urgent need to develop innovative techniques to monitor CIP. Therefore, this study aims to develop a simple and sensitive CIP sensor called the boron-doped diamond nanoparticle-modified screen-printed electrode (BDD NPs/SPE) and the nickel oxide nanoparticle-modified BDD NPs/SPE (NiO NPs/BDD NPs/SPE). NiO NPs were synthesized via green synthesis using Spatholobus littoralis Hassk. root extract as the reducing agent. The formation and characteristics of NiO NPs were then confirmed through a UV-Vis spectrophotometer, XRD, PSA, FT-IR, and XPS. The successful modification of SPE was confirmed through SEM-EDX, followed by measurements using square-wave voltammetry. The results showed that the modified SPE could detect CIP over a concentration range of 0.1–100 µM and produced a low detection limit of 0.109 µM for BDD NPs/SPE and 0.054 µM for NiO NPs/BDD NPs/SPE. The proposed method was successfully applied to the determination of CIP in commercial tablets, milk, and human urine, with a satisfactory % recovery from 95 to 100%. The current study successfully developed a simple yet highly sensitive sensor that enabled robust, reliable, and efficient detection of CIP, showing its strong potential for practical applications. Full article

The microbial community surrounding plant roots plays a vital role in plant growth, nutrient uptake, stress resilience and other potential functions. This review synthesizes available evidence that plant growth-promoting microorganisms (PGPMs) not only directly benefit the plant but also modulate the rhizospheric microbiome by mediating metabolic reprogramming of the host plant. PGPMs modify the composition of root exudates through the regulation of phytohormone signaling and transcriptional networks, thereby promoting beneficial microbes and suppressing disease. Key mechanisms involve the jasmonate, ethylene, and strigolactones signaling pathways. Transcription factors MYB72, ERF1 regulate biosynthesis and secretion of metabolites like organic acids and coumarins. The exudates serve as specific signals for microbial community assembly and as enhancers of feedback loops that reinforce plant-microbe mutualism. We examine the ecological and agricultural significance of PGPM-induced metabolic reprogramming of the host due to PGPMs that enhances disease suppression, abiotic stress tolerance, and nutrient use efficiency. Lastly, we address advanced methods and strategies for transferring these biological pathways to the agricultural realm and on to a more sustainable agricultural practice with emphasis on the need to integrate multi-omics (whole genomics, transcriptomics, and metabolomics), synthetic microbial communities and plant genetic engineering for microbiome-assisted agriculture. This synthesis reveals that PGPM-induced metabolic reprogramming operates through an integrated cross-scale framework linking microbial perception, phytohormone signaling, transcriptional regulators, and transporter-mediated exudate efflux, with root exudates functioning as plant-controlled ecological filters that selectively shape the rhizosphere microbiome. We further identify key translational challenges, including context-dependent efficacy and the lab-to-field gap, and propose a roadmap combining multi-omics, synthetic communities, and genome editing to realize the potential of microbiome-assisted sustainable agriculture. Full article

This study utilizes machine-learning algorithms to reinterpret existing datasets originally plotted using Decline-Curve Analysis (DCA), aiming to enhance predictive accuracy without requiring new field-data acquisition. Historical production records were compiled: monthly oil/gas rates, bottom-hole pressures, and cumulative productions, which were fitted to Arps equations via least-squares optimization, and key decline parameters, such as initial rate, nominal decline rate, and hyperbolic exponent, served as input data. Four machine-learning models were trained and validated: Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Linear Regression (LR), using 80/20 train–test splits and 5-fold cross-validation. Models were evaluated using Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²). The ANN emerged as the best-performing method, achieving near-unity predictive accuracy (R² ≈ 1) on the independent test set, with low error values (MSE = 0.0012 Ncm²/month², RMSE = 0.035 Ncm/month, MAE = 0.028 Ncm/month) for oil production rates. Similar levels of accuracy were obtained for gas rates and pressures. These results reflect the strong and highly regular relationships present in the dataset analyzed rather than an exact zero-error fit. The multi-layer architecture of the ANN effectively captured the nonlinear interactions between Arps parameters and transient flow regimes, outperforming the empirical and physics-constrained approaches. Linear regression yielded strong results (R² = 0.98, RMSE = 0.15 Ncm/month) but faltered in high-decline scenarios, failing to model exponential tails accurately. SVM exhibited the highest deviations (RMSE = 0.42 Ncm/month, R² = 0.89), attributable to kernel sensitivity in sparse, noisy decline data. RF provided intermediate performance (R² = 0.97). This ANN-driven approach redefines decline analysis by automating parameter tuning and uncertainty quantification, reducing forecasting errors by 85% versus classical Arps methods. Full article

Sustainable agriculture is increasingly reliant on reducing anthropogenic inputs and recycling organic waste while protecting ecosystems. In this context, this study investigated the agro-environmental properties of biosulfate, focusing on its interaction with herbicides and its effects on soil fungi and horticultural plants. Two biosulfate samples obtained from urban sewage sludge from the Barletta (BIO-BA) and Foggia (BIO-FO) treatment plants were characterized by Fourier transform infrared–attenuated total reflectance (FTIR-ATR) spectroscopy and scanning electron microscopy (SEM). The adsorption/desorption of the herbicides metribuzin (MET), S-metolachlor (S-ME) and cycloxydim (CYC) on biosulfates was evaluated by studying adsorption kinetics and isotherms. All herbicides reached adsorption equilibrium within a few hours, according to pseudo-second-order kinetics, indicating a predominant chemical interaction between biosulfate and the molecules. Considering the organic C content of BIO-BA (~21%) and BIO-FO (~17%), which was less than half that commonly measured for other organic fertilizers, such as compost and digestate, their adsorption capacity was high, with Freundlich adsorption constants ranging from 772 µg g⁻¹ (S-ME on BIO-BA) to 1464 µg g⁻¹ (CYC on BIO-FO). A low hysteresis coefficient indicated a rather slow and incomplete release of the molecules from the biosulfate. Exposure of the fungi Pleurotus ostreatus and Pleurotus eryngii to 1, 2, 3, and 4% BIO-BA and BIO-FO stimulated mycelium growth, indicating that responses depended on fungal species and biosulfate dose. Finally, germination and early growth of lettuce and basil were generally unaffected by either biosulfate, as parameters such as germination percentage, root and shoot length, and fresh and dry biomass were not statistically different from the control. Some growth stimulation was observed in basil. Overall, biosulfate appears to be a promising soil fertilizer, as it can contribute to soil organic matter, retain xenobiotics, and exert biostimulatory effects under controlled conditions. Full article

The damage status of shield machine disc cutters directly impacts the safety and efficiency of tunnelling projects. Current manual inspection methods involve high risks and low efficiency, while existing detection methods suffer from low accuracy and poor real-time performance in complex environments, often lacking quantitative analysis capabilities. To address these issues, this paper proposes an intelligent identification and quantitative assessment method for disc cutter damage based on the Deep Forest (DF) model. First, an eddy current sensor calibration platform was established, and a mapping relationship between output voltage and actual wear was developed through piecewise fitting to achieve precise wear quantification. In the data preprocessing stage, signal quality was improved via filtering, and typical damage features such as edge chipping, cracks, and eccentric wear were extracted using pulse edge detection. These feature segments were then resampled to construct the model training dataset. The DF model utilizes a hierarchical ensemble structure to mine data correlations, enabling accurate identification of four states: normal, edge chipping, eccentric wear, and cracks. Simultaneously, a DF regression model was employed to provide continuous quantitative predictions of damage size. Experimental results show that the classification model achieved accuracies of 98%, 96%, and 96% on the training, validation, and test sets, respectively, with weighted average F1-scores exceeding 0.96. The regression model achieved a coefficient of determination (R²) of 0.9940 and a root mean square error (RMSE) of 0.4051 on the test set. Both models demonstrate excellent performance and generalization, achieving full coverage from “qualitative state identification” to “quantitative wear assessment,” thereby providing reliable decision support for cutter maintenance and replacement. Full article

Background: Continuous cropping severely restricts ornamental gourd productivity through yield decline, microbial dysbiosis, and rhizosphere autotoxin production. This study characterized rhizosphere–root–leaf metabolic reorganization under three-year monoculture, identifying key metabolites, pathways, and a hierarchical cascade for stress adaptation. Methods: Ornamental gourd seedlings were potted in three-year monoculture soil exhibiting replanting disorders. At the seven-leaf stage, rhizosphere soil, roots, and leaves were sampled for untargeted UHPLC-MS/MS metabolomics, followed by PCA, OPLS-DA, differential analysis (VIP > 1, p < 0.05), and KEGG pathway enrichment analysis. Results: A total of 10,792 metabolic features were detected in positive mode and 8992 in negative mode. PCA explained 83.84% of the variance, with PC1 at 56.35% and PC2 at 27.49%, clearly separating the compartments of the study. A total of 1132 shared metabolites were suppressed, with log2 fold changes exceeding −1. Roots displayed activation, with upregulated metabolites outnumbering downregulated ones, and log2 fold changes frequently exceeding +3. Leaves exhibited mean log2 fold changes of approximately +1 for phenylpropanoid intermediates, indole, and terpenoid biosynthesis. The enriched pathways included amino acid metabolism, phenylpropanoid and flavonoid biosynthesis, lipid metabolism, and hormone signaling. Conclusions: Continuous cropping induces a hierarchical rhizosphere–root–leaf metabolic cascade, linking suppressed soil activity with reinforced root defense and coordinated leaf signaling, centered on the phenylpropanoid and flavonoid pathways as key drivers of adaptation. Full article