Search Results (3,490)

In energy production systems, the higher heating value (HHV), also known as the gross calorific value, is a key parameter for identifying the primary energy source. In this study, a novel artificial intelligence model was developed using support vector machines (SVM) combined with the Differential Evolution (DE) optimizer to predict coal gross calorific value (the dependent variable). The model incorporated the elements from coal ultimate analysis—hydrogen (H), carbon (C), oxygen (O), sulfur (S), and nitrogen (N)—as input variables. For comparison, the experimental data were also fitted to previously reported empirical correlations, as well as Ridge, Lasso, and Elastic-Net regressions. The SVM-based model was first used to assess the influence of all independent variables on coal HHV and was subsequently found to be the most accurate predictor of coal gross calorific value. Specifically, the SVM regression (SVR) achieved a correlation coefficient (r) of 0.9861 and a coefficient of determination (R²) of 0.9575 for coal HHV prediction based on the test samples. The DE/SVM approach demonstrated strong performance, as evidenced by the close agreement between observed and predicted values. Finally, a summary of the results from these analyses is presented. Full article

Background: Ambient yoghurt, also known as room-temperature yoghurt, has gained increasing attention due to its convenience in distribution and consumption without needing cold storage. To ensure extended shelf life, ambient yoghurt normally undergoes an additional heat treatment during manufacturing, the post-fermentation sterilisation process (typically at 65–85 °C), which may induce the formation of fine particle aggregates and result in undesirable textural attributes, particularly graininess. Assessing textural attributes of such products remains a challenge. Methods: By mimicking the oral behaviour of ambient yoghurt, this study uses rheological as well as tribological techniques for objective assessment of the textural sensations of slipperiness and graininess. Various experimental conditions, including the amount of saliva incorporation, sliding speed, and ball-contact and plate-contact lubrication, were examined, and results were analysed against perceived texture by panellists. Main findings: The results indicate that viscosity changes are closely associated with perceived slipperiness under the tested conditions. The friction coefficient obtained from a plate-contact tribometer shows a positive correlation with the sensation of graininess (Pearson’s r was 0.74, p < 0.05, N = 8). It was also observed that a 20% saliva incorporation showed the closest agreement with sensory perception, although this observation should be interpreted cautiously due to the limited sample size. Implications: Results obtained from this work indicate the feasibility of using rheology and tribology techniques for texture prediction in ambient yoghurt. The findings are exploratory in nature, and further studies with larger sample sets are required to validate the proposed approach. The methodology presented here may serve as a reference framework for investigating texture perception in other dairy systems. Full article

Nowadays, biowaste valorization is a key point in the circular economy. Digestate and compost from organic waste treatment can be used as nutrient-rich fertilizers. In Europe, the use of biowaste-derived fertilizers is promoted by the European Fertilizer Regulation (EU) 2019/1009, which requires verification of their biological stability through regulated indices; however, it is not clear whether the proposed indices and threshold values indicate the same level of stability and what correlations there are between them. This study compared four biological stability indices, namely Oxygen Uptake Rate (OUR), Self-Heating (SH), Residual Biogas Potential (RBP), and Dynamic Respirometric Index (DRI), which were tested on 50 samples of compost and digestate. Overall, the results revealed that most of the compost and digestate samples were quite far from European standards. On the contrary, the RBP test seemed to be less stringent than the other indices, since a much larger number of samples was closer to or in compliance with the established threshold. Data analysis using Pearson’s coefficients showed a strong linear correlation between the indices. Nevertheless, the linear regression predictive model based on experimental data demonstrated that the indices could not represent the same level of stability, providing poor consistency and variability in the predicted values and established threshold. In particular, the DRI test appeared to be more severe than the other aerobic indices. This work could provide valuable support in improving evaluation criteria and promoting a sustainable use of compost and digestate as organic fertilizers from a circular economy perspective. Full article

We analyzed six years (2019–2025) of Sentinel-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery to quantify how turbidity and water temperature relate to algal blooms in Utah Lake. We generated satellite-derived estimates of chlorophyll-a (chl-a), turbidity, and surface temperature at 600 randomly distributed sample points. Using generalized least squares models, we found that temperature and turbidity explain only a small fraction of the variance in chl-a (temperature coefficients 0.02–0.03; turbidity coefficients −0.18–0.42), and the strength and sign of correlations vary by location. Despite weak linear correlations, we identified a strong nonlinear pattern: 94% of intense bloom events (chl-a > 87 µg/L) occurred when turbidity was below 120 Nephelometric Turbidity Units (NTU), indicating that blooms more often form under low-turbidity conditions. We also found that the first mild blooms of the season (chl-a > 34 µg/L) typically occurred five days after the largest short-term temperature increase (3–12 °C/day) at a given location, but only when blooms first appeared in April. These results suggest that Utah Lake blooms may be light-limited, with turbidity constraining algal growth that would otherwise occur in response to high nutrient levels, while temperature spikes influence early-season bloom initiation. Our findings have direct implications for monitoring and management strategies that target algal blooms on Utah Lake. Full article

An innovative dispersive liquid–liquid microextraction technique utilizing a solid dispersion was established for the quantification of triazine herbicides in environmental water samples. Naturally derived monoterpenoids were utilized as eco-friendly extraction solvents, markedly decreasing the reliance on harmful extraction solvents. A small amount of Pop Rocks candy served as a solid dispersant; the rapid release of carbon dioxide promoted the generation of fine monoterpenoid droplets, effectively replacing conventional hazardous liquid dispersants. The solidification technique of floating organic droplets facilitated the effective phase separation of monoterpenoids from aqueous samples, thereby obviating the need for centrifugation. Triazine herbicides exhibited good linearity within the concentration range of 0.008–0.8 mg/L with correlation coefficients above 0.99 and detection limits of 0.002 mg/L. The proposed method was effectively implemented on surface and groundwater samples, attaining recoveries between 86.4% and 98.0%. Molecular docking analysis suggests a spontaneous binding between the monoterpenoid and triazine herbicides. A comprehensive green assessment utilizing two evaluation tools confirmed the excellent environmental performance of the method. This technique offers superior greenness and simplicity compared with conventional techniques, demonstrating strong potential for application in the environmental analysis of pesticide residues. Full article

NavIC and GPS are satellite-based navigation systems developed by India and the United States, respectively, and are widely used for ionospheric and space weather studies. This paper presents a comparative analysis of NavIC- and GPS-derived total electron content (TEC) during a High-Intensity Long-Duration Continuous AE Activity (HILDCAA) event that occurred from 17 to 21 August 2017. The analysis covers the five days of the event, along with three days before and after, using observations from a single low-latitude station over the Indian region. NavIC performance is evaluated by comparing vertical TEC (vTEC) derived from dual-frequency pseudorange measurements with co-located GPS-derived vTEC. The results show a strong linear correspondence between the two datasets, with Pearson correlation coefficients exceeding ∼0.97 throughout the event interval. Such high correlation is physically expected, as the dominant contribution to TEC arises from the common vertical ionospheric column sampled by both systems. Nevertheless, the close agreement observed under sustained geomagnetic disturbance conditions demonstrates that NavIC is capable of consistently capturing ionospheric TEC variability during this specific HILDCAA event. Full article

Accurate determination of Soil Organic Carbon (SOC), which is the foundation of soil health and safeguards ecological and food security, is crucial in local agricultural production. We aimed to investigate the influence of soil texture on hyperspectral models for predicting SOC content and to evaluate the role of different preprocessing methods and feature band selection algorithms in improving modeling efficiency. Laboratory-determined SOC content and hyperspectral reflectance data were obtained using soil samples from daylily cultivation areas in Yunzhou District, Datong City. Mathematical transformations, including Savitzky–Golay smoothing (SG), First Derivative (FD), Second Derivative (SD), Multiplicative Scatter Correction (MSC), and Standard Normal Variate (SNV), were applied to the spectral reflectance data. Feature bands extracted based on the successive projection algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) were used to establish SOC content inversion models employing four algorithms: partial least-squares regression (PLSR), Random Forest (RF), Backpropagation Neural Network (BP), and Convolutional Neural Network (CNN). The results indicate the following: (1) Preprocessing can effectively increase the correlation between the soil spectral reflectance process and SOC content. (2) SPA and CARS effectively screened the characteristic bands of SOC in daylily cultivated soil from the spectral curves. The SPA algorithm and CARS selected 4–11 and 9–122 bands, respectively, and both algorithms facilitated model construction. (3) Among all the constructed models, the FD-CARS-PLSR performed most prominently, with coefficients of determination (R²) for the training and validation sets reaching 0.93 and 0.83, respectively, demonstrating high model stability and reliability. (4) Incorporating soil texture as an auxiliary variable into the PLSR inversion model improved the inversion accuracy, with accuracy gains ranging between 0.01 and 0.05. Full article

The correct estimation of the population variance plays a vital role in the sampling procedure in surveys, especially when simple random sampling techniques are used. In this work, we propose a new generalized statistical inference in order to estimate the population variance using auxiliary information. We can use the relationship between the study variable and the auxiliary variable to construct a novel generalized class of estimators that is better performing in terms of minimum mean squared error (MSE) and has a higher percentage of relative efficiency than the traditional estimators. The proposed methodology is based on the existing methods of inference with the introduction of modifications to cover the known population parameters of additional auxiliary variables, like the mean, the coefficient of variation, skewness, or kurtosis. Theoretical properties such as bias and mean squared error are obtained with regard to the first-order approximation. The performance of the proposed class of estimators is checked by comparing with that of the classical variance estimators in different population conditions based on real-life data sets and a simulation study. The numerical findings have indicated that the suggested class of estimators is more effective compared to classical methods, especially in cases where there is a very high linear correlation between the auxiliary and the study variables. Also, the estimators are robust, as confirmed using various sample sizes and population structures. The research has made a significant contribution to the development of statistical procedures in survey sampling because the practical and efficient tools provided in the study were useful in estimating the variance. The results have been of great importance when applied by researchers and practitioners active in large-scale surveys. Subsequently, in the case of efficient utilization of auxiliary information, it is feasible to have more accurate and cost-effective statistical inference. Full article

Electronic nose systems are widely used in environmental monitoring and other related fields. In recent years, systems based on gas sensor arrays have attracted considerable attention. However, relying solely on improvements in gas-sensitive materials has struggled to break through the bottleneck in recognition accuracy. To address this challenge, this study designs and validates an integrated machine learning framework for enhanced gas identification in electronic nose systems. Specifically, (1) a Butterworth low-pass filter is combined with principal component analysis (PCA) to suppress sensor noise; (2) the synthetic minority over-sampling technique (SMOTE) is utilized for training set data augmentation to further enhance the classification accuracy of the support vector machine (SVM); and (3) the relationship between single-component and mixed-gas responses is analyzed to construct an artificial neural network (ANN) regression model. Experimental results demonstrate that the SMOTE-augmented, PCA-optimized SVM model achieves a recognition accuracy of 0.93 ± 0.08 for most target gases, representing improvements of 19% and 7% over decision tree and ANN classifiers, respectively, and that the ANN regression model attains a correlation coefficient of 99.55% between predicted and measured values in mixed-gas experiments. Overall, the construction and optimization of this system demonstrate significant practical value for intelligent gas identification and the development of advanced e-nose devices. Full article

Accurate estimation of soil moisture (SM) and vegetation optical depth (VOD) is essential for understanding land–atmosphere interactions, climate dynamics, and ecosystem processes. While passive microwave missions such as SMAP and SMOS provide reliable global SM and VOD products, they are limited by coarse spatial resolution and infrequent revisit times. Global Navigation Satellite System Reflectometry (GNSS-R) observations, particularly from the Cyclone GNSS (CYGNSS) mission, offer an improved spatiotemporal sampling rate. This study presents a deep learning framework based on an artificial neural network (ANN) for the simultaneous retrieval of SM and VOD from CYGNSS observations across the contiguous United States (CONUS). Ancillary input features, including specular point latitude and longitude (for spatial context), CYGNSS reflectivity and incidence angle (for surface signal characterization), total precipitation and soil temperature (for hydrological context), and soil clay content and surface roughness (for soil properties), are used to improve the estimates. Results demonstrate strong agreement between the predicted and reference values (SMAP SM and SMOS VOD), achieving correlation coefficients of R = 0.83 and 0.89 and RMSE values of 0.063 m³/m³ and 0.088 for SM and VOD, respectively. Temporal analyses show that the ANN accurately reproduces both seasonal and daily variations in SMAP SM and SMOS VOD (R ≈ 0.89). Moreover, the predicted SM and VOD maps show strong agreement with the reference SM and VOD maps (R ≈ 0.93). Additionally, ANN-derived VOD demonstrates strong consistency with above-ground biomass (R ≈ 0.77), canopy height (R ≈ 0.95), leaf area index (R = 96), and vegetation water content (R ≈ 0.90). These results demonstrate the generalizability of the approach and its applicability to broader environmental sensing tasks. Full article

Promoting the synergistic development of green finance (GF) and green technology innovation (GTI) is crucial for achieving sustainable economic development. Based on the sample data of 30 provinces in China from 2010 to 2023, this study first investigates the theoretical mechanism of interactive coupling and then employs methods including Dagum Gini coefficient, spatial kernel density estimation, spatial correlation analysis, and a GTWR model to explore the spatiotemporal pattern, evolution trend, and driving factors of the coupling coordination between GF and GTI. The findings are as follows: (1) The coupling coordination degree (CCD) is about to transition from the moderate imbalance stage to the near imbalance stage, presenting a distinct spatial pattern of “higher levels and faster development in the east, and lower levels and slower development in the west”. (2) The Gini coefficient of the CCD shows an upward trend, with the degree of imbalance increasing year by year; the main sources of the overall differences follow this order: intra-regional disparity (G_w) > inter-regional disparity (G_b) > transvariation density (G_t). (3) The CCD between GF and GTI exhibits a positive spatial correlation, and the agglomeration degree is constantly increasing; the High-High Cluster areas are mainly concentrated in northern China. (4) Economic development level, financial development level, population scale, and urbanization level drive the coupling coordination between GF and GTI. This study provides new theoretical and empirical evidence for the complex coupling relationship and driving factors of GF and GTI and offers a key scientific basis for the Chinese government to formulate differentiated regional policies, thereby promoting the effective implementation of the green and low-carbon development strategy. Full article

Background: Narrative Medicine (NM) has emerged as a strategy to support reflective clinical practice and emotional resilience among nurses. This study examined relationships between NM practices and well-being among registered nurses (RNs) and nurse practitioners (NPs). Methods: A national sample of RNs and NPs was recruited using snowball sampling. Participants completed a NM practice survey and the Mayo Clinic Well-Being Index (WBI) survey. Data were analyzed using descriptive statistics and Pearson correlation coefficients. Results: A total of 3167 responses were analyzed (1934 RNs and 1233 NPs). Among RNs, strong statistically significant correlations were found between NM practices and well-being scores (p < 0.001). Among NPs, moderate correlations appeared in select NM practice dimensions (p < 0.05). Conclusions: Engagement in narrative Medicine practices is associated with improved well-being among nurses and nurse practitioners. NM may present a promising strategy to reduce burnout and strengthen professional resilience. Full article

Traditional chemical analysis of plant tissue is time-consuming, costly, and poses risks due to exposure to toxic gases, highlighting the need for faster, low-cost, and safer alternatives. Vis-NIR spectroscopy, combined with machine learning, offers a promising method for estimating leaf nutrient levels without chemical reagents. This study evaluated the potential of Vis-NIR spectroscopy for nutrient estimation in leaf samples of banana (n = 363), mango (n = 239), and grapevine (n = 336) by applying spectral pre-processing techniques—smoothing (SMO) and first derivative Savitzky–Golay (SGD1d) alongside two machine learning methods: Partial Least Squares Regression (PLSR) and Random Forest (RF). Plant tissue samples were analyzed using sulfuric and nitroperchloric wet digestion and hyperspectral sensors. The prediction models were assessed using concordance correlation coefficient (CCC) and mean squared error (MSE). The highest accuracy (CCC > 0.80 and MSE < 2 g kg⁻¹) was achieved for Ca in banana, P in mango, and N and Ca in grapevine across both machine learning methods and pre-processing techniques. The predictive models calibrated for ‘Grapevine’ exhibited the highest accuracy—characterized by higher CCC values and lower MSE values—when compared with the models developed for ‘Mango’ and ‘Banana’. Models using SMO and SGD1d showed better performance than those using raw spectra (RAW). The high amplitudes and variations in nutrient levels, combined with large standard deviations, negatively affected the predictive performance of the models. Full article

Accurate lightning data are critical for disaster warning and climate research. This study systematically compares the Fengyun-4A Lightning Mapping Imager (FY-4A LMI) satellite and the Advanced Time-of-arrival and Direction (ADTD) lightning location network in the Beijing-Tianjin-Hebei (BTH) region (April–August, 2020–2023) using coefficient of variation (CV) analysis, Welch’s independent samples t-test, Pearson correlation analysis, and inverse distance weighting (IDW) interpolation. Key results: (1) A significant systematic discrepancy exists between the two datasets, with an annual mean ratio of 0.0636 (t = −5.1758, p < 0.01); FY-4A LMI shows higher observational stability (CV = 5.46%), while ADTD excels in capturing intense lightning events (CV = 28.01%). (2) Both datasets exhibit a consistent unimodal monthly pattern peaking in July (moderately strong positive correlation, r = 0.7354, p < 0.01) but differ distinctly in diurnal distribution. (3) High-density lightning areas of both datasets concentrate south of the Yanshan Mountains and east of the Taihang Mountains, shaped by topography and water vapor transport. This study reveals the three-factor (climatic background, topographic forcing, technical characteristics) coupled regulatory mechanism of data discrepancies and highlights the complementarity of the two datasets, providing a solid scientific basis for satellite-ground data fusion and regional lightning disaster defense. Full article

Salmonella, a major global foodborne pathogen, is a leading cause of salmonellosis. Quantitative detection of Salmonella provides a scientific basis for establishing microbiological criteria and conducting risk assessments. The plate count method remains the primary approach for bacterial quantification, whereas the most probable number (MPN) method is commonly used for detecting low levels of bacterial contamination. However, both methods are time-consuming and labor-intensive. Validated digital polymerase chain reaction (dPCR) techniques are emerging as promising alternatives because they enable rapid, absolute quantification with high specificity and sensitivity. Herein, we developed a novel droplet dPCR (ddPCR) assay for identifying and quantifying Salmonella using invA as the target. The assay demonstrated high specificity and sensitivity, with a limit of quantification of 1.1 × 10² colony-forming units/mL in meat samples. Furthermore, the log₁₀ values obtained via ddPCR and plate counting exhibited a strong linear relationship (R² > 0.99). Mathematical modeling of growth kinetics further confirmed a high correlation between plate count and ddPCR measurements (Pearson correlation coefficient: 0.996; calculated bias factor: 0.88). Collectively, these results indicate that ddPCR is a viable alternative to the MPN method and represents a powerful tool for the quantitative risk assessment of food safety. Full article