Search Results (2,837)

Predicting student academic success is a significant task in the field of educational data analysis, offering insights for personalized learning interventions. However, the existing research faces challenges such as imbalanced datasets, inefficient feature transformation methods, and limited exploration data integration. This research introduces an innovative method for predicting student performance by transforming one-dimensional student online learning behavior data into two-dimensional images using four distinct text-to-image encoding methods: Pixel Representation (PR), Sine Wave Transformation (SWT), Recurrence Plot (RP), and Gramian Angular Field (GAF). We evaluated the transformed images using CNN and FCN individually as well as an ensemble network, EnCF. Additionally, traditional machine learning methods, such as Random Forest, Naive Bayes, AdaBoost, Decision Tree, SVM, Logistic Regression, Extra Trees, K-Nearest Neighbors, Gradient Boosting, and Stochastic Gradient Descent, were employed on the raw, untransformed data with the SMOTE method for comparison. The experimental results demonstrated that the Recurrence Plot (RP) method outperformed other transformation techniques when using CNN and achieved the highest classification accuracy of 0.9528 under the EnCF ensemble framework. Furthermore, the deep learning approaches consistently achieved better results than traditional machine learning, underscoring the advantages of image-based data transformation combined with advanced ensemble learning approaches. Full article

Forest aboveground biomass (AGB) is not only the basis for forest carbon stock research, but also an important parameter for assessing the forest carbon cycle and ecological functions of forests. However, there are various uncertainties in the estimation process, limiting the accuracy of AGB estimation. Therefore, we extracted the spectral features, vegetation indices and texture factors from remote sensing images based on the field data and Landsat 8 OLI remote sensing images in Southern China to quantify the uncertainties. Then, we established three AGB estimation models, including K Nearest Neighbor Regression (KNN), Gradient Boosted Regression Tree (GBRT) and Random Forest (RF). Uncertainties at the plot scale and models were measured by using error equations to analyze the influences of uncertainties at different scales on AGB estimation. Results were as follows: (1) The R² of the per-tree biomass model for Cunninghamia lanceolata was 0.970, while the uncertainty of the residual and parameters for per-tree biomass model was 4.62% and 4.81%, respectively; and the uncertainty transferred to the plot scale was 3.23%. (2) The estimation methods had the most significant effects on the remote sensing models. RF was more accurate than other two methods, and had the highest accuracy (R² = 0.867, RMSE = 19.325 t/ha) and lowest uncertainty (5.93%), which outperformed both the KNN and GBRT models (KNN: R² = 0.368, RMSE = 42.314 t/ha, uncertainty = 14.88%; GBRT: R² = 0.636, RMSE = 32.056 t/ha, uncertainty = 6.3%). Compared to KNN and GBRT, the R² of RF was enhanced by 0.499 and 0.231, while the uncertainty was decreased by 8.95% and 0.37%, respectively. The uncertainty associated with the scale of remote sensing models remains the primary source of uncertainty when compared to the plot scale. On the remote sensing scale, RF is the model with the best estimation effect. This study examines the impact of both plot-scale and remote sensing model-scale methodologies on the estimation of AGB for Cunninghamia lanceolata. The findings aim to offer valuable insights and considerations for enhancing the accuracy of AGB estimations. Full article

This study coupled precision agriculture with agroecology to improve the agricultural systems’ sustainability in a climate variability context, characterized by fewer rainy days and more extreme events. A three-year comparative analysis was carried out in a durum wheat rotation, divided into two plots of 2.5 ha each, one managed with conventional methods (CP, sunflower as intermediate crop) and another managed with an agroecological approach (AE, field bean as green manure crop), featuring prescription maps for site-specific mineral fertilization. The statistical analysis of durum wheat parameters, soil characteristics, and economic variables was conducted alongside the examination of climatic data. In AE soil, the exchangeable calcium was statistically different from CP soil (6044 mg kg⁻¹ and 5660 mg kg⁻¹, respectively). Cation exchange capacity was significantly higher in AE (32.7 meq 100 g⁻¹), compared to CP (30.9 meq 100 g⁻¹). In AE, wheat yield (2.36 t ha⁻¹) was higher than in CP (2.07 t ha⁻¹), despite extreme rainfall causing flooding in some parts of the AE plot. The economic balance was only 6% in favor of CP (EUR + 2157), confirming the AE approach’s resilience (EUR + 2027), despite the higher costs of cover cropping and site-specific fertilization. The novelty of integration between “smartish” precision agriculture and agroecology allows for sustainable management. Full article

With the development of algorithms for autonomous decision-making in the field of structural engineering, the design of precast concrete segment (PCS) box girder bridges faces new challenges. This paper proposes using a multi-objective optimization method based on genetic algorithms for the rapid design of PCS box girder bridges with small and medium spans. By considering 20 design parameters such as the physical dimensions of the box girder cross-section, material properties, and prestressing parameters, the paper formulates and quantifies three objective functions: cost, safety, and structural performance. The multi-objective optimization was conducted using four optimization algorithms (NSGA-II, NSGA-III, GDE3, and PSO). An optimization evaluation index ($\phi \left[F\right(x\left)\right]$) was established and weights were assigned to different optimization objectives. A specific design case based on the general diagram of a 3 × 25 m-long continuous PCS box girder bridge was carried out. The results indicate that genetic algorithms performed exceptionally well on this problem, with the NSGA-III algorithm achieving the best $\phi \left[F\right(x\left)\right]$ value of 0.2789 among all algorithms. A performance analysis was conducted on various optimization models using box plots and sensitivity studies. Scatter plots and surface plots of the Pareto front of the optimized solutions were generated, and corresponding cross-sectional design drawings were created based on the two proposed solutions. Compared with the general graph, the design cases provided by the NSGA-III algorithm model have a change rate of 8.03%, −0.29%, and 75.49% in the three optimization objectives, respectively, indicating a significant improvement effect. The research content of this paper provides a reasonable direction for future studies on intelligent bridge design methodologies. Full article

Tan spot (Pyrenophora tritici-repentis) and stripe rust (Puccinia striiformis f. sp. tritici) are major foliar diseases of wheat, causing significant yield losses globally. This study evaluated the efficacy of fungicide seed treatments in managing these diseases during early growth stages under greenhouse, growth chamber, and field conditions in the Northern Great Plains. Winter and spring wheat cultivars were treated with pyraclostrobin or combinations of thiamethoxam, difenoconazole, mefenoxam, fludioxonil, and sedaxane, among others. Greenhouse and growth chamber plants were inoculated with the respective pathogens, while field trials relied on natural inoculum. Fungicide treatments significantly reduced stripe rust severity (up to 36%) (p ≤ 0.05) and moderately reduced tan spot severity during early growth stages (15–20%). Treated plants demonstrated a 30–40% improvement in plant vigor, and a 25–50% increase in winter survival. Additionally, grain yield in treated plots increased by 25–50% (p ≤ 0.05), with test weight and protein content improving by 10% and 15%, respectively. These findings demonstrate the potential of fungicide seed treatments as an integrated pest (or pathogen) management (IPM) strategy to enhance early foliar disease control and wheat productivity. Full article

Hopea chinensis is a representative tree species in evergreen monsoon forests in the northern tropics, but it is currently in a critically endangered state due to destruction by human activities and habitat loss. In this study, we measured and analyzed the number of regenerating seedlings and habitat factors in wild populations of H. chinensis by combining field surveys with laboratory analysis. The aim of this study was to clarify the spatial distribution of H. chinensis seedlings and related factors to provide a scientific basis for conserving its germplasm resources and population restoration. In six populations, most size-class seedlings had aggregated distributions at three scales, and the intensity of aggregation decreased as the sample plot scale increased for most size-class seedlings. In the northern foothills of the Shiwandashan Mountains, size class I seedlings tended to be distributed in habitats with a higher rock bareness rate, whereas size class II and III seedlings tended to be distributed in habitats with a higher canopy density, thicker humus layers, and higher soil moisture content. In the southern foothills of the Shiwandashan Mountains, size class I and II seedlings tended to be distributed in habitats with higher available nitrogen contents, and size class III seedlings tended to be distributed in habitats with higher available nitrogen and soil moisture contents. Therefore, in the southern foothills of the Shiwandashan Mountains, the survival rate of H. chinensis seedlings can be improved by artificially adding soil to increase the thickness of the soil layer in stone crevices and grooves, regularly watering the seedlings during the dry season, and appropriately reducing the coverage of the shrub layer. In the northern foothills, the survival rate of H. chinensis seedlings can be enhanced by regularly applying nitrogen fertilizer and watering to increase the available nitrogen and soil moisture contents. Full article

Cardiovascular disease (CVD) poses a significant challenge to global health, with cardiac arrhythmia representing one of its most prevalent manifestations. The timely and precise classification of arrhythmias is critical for the effective management of CVD. This study introduces an innovative approach to enhancing arrhythmia classification accuracy through advanced Electrocardiogram (ECG) signal processing. We propose a dual-channel feature fusion strategy designed to enhance the precision and objectivity of ECG analysis. Initially, we apply an Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and enhanced wavelet thresholding for robust noise reduction. Subsequently, in the primary channel, region of interest features are emphasized using a ResNet-ICBAM network model for feature extraction. In parallel, the secondary channel transforms 1D ECG signals into Gram angular difference field (GADF), Markov transition field (MTF), and recurrence plot (RP) representations, which are then subjected to two-dimensional convolutional neural network (2D-CNN) feature extraction. Post-extraction, the features from both channels are fused and classified. When evaluated on the MIT-BIH database, our method achieves a classification accuracy of 97.80%. Compared to other methods, our approach of two-channel feature fusion has a significant improvement in overall performance by adding a 2D convolutional network. This methodology represents a substantial advancement in ECG signal processing, offering significant potential for clinical applications and improving patient care efficiency and accuracy. Full article

Optimization is a highly relevant area of research due to its widespread applications. The development of new optimization algorithms or the improvement of existing ones enhances the efficiency of various fields of activity. In this paper, an improved Soft Island Model (SIM) is considered for the Tent-map-based Fish School Search algorithm with Exponential step decay (ETFSS). The proposed model is based on a probabilistic approach to realize the migration process relying on the statistics of the overall achievement of each island. In order to generate the initial population of the algorithm, a new initialization method is proposed in which all islands are formed in separate regions of the search space, thus forming clusters. For the presented SIM-ETFSS algorithm, numerical experiments with the optimization of classical test functions, as well as checks for the presence of some known defects that lead to undesirable effects in problem solving, have been carried out. Tools, such as the Mann–Whitney U test, box plots and other statistical methods of data analysis, are used to evaluate the quality of the presented algorithm, using which the superiority of SIM-ETFSS over its original version is demonstrated. The results obtained are analyzed and discussed. Full article

This study explores the effects of ultrasonic treatment on the quality of potatoes processed into fries. Ultrasonic waves generate rapid pressure changes and cavitation effects, which can enhance seed vigor and growth. Over a three-year period (2015–2017) in east-central Poland, a field experiment was conducted using a randomized block design with split-plot divisions with three replications. The study compared two cultivation technologies: (a) with ultrasonic treatment of seed potatoes before planting, and (b) traditional technology. The second-order factor consisted of eight edible potato cultivars from all earliness groups (‘Denar’, ‘Lord’, ‘Owacja’, ‘Vineta’, ‘Satina’, ‘Tajfun’, ‘Syrena’, and ‘Zagłoba’). The sonication process was carried out using an ultrasonic bath with piezoelectric transducers. The results demonstrated significant impacts of the cultivation technology, potato variety, and weather conditions on the quality of fries. This research underscores the potential of ultrasonic treatment to improve the quality and consistency of potato products in the food industry. The use of ultrasound treatment on potato tubers before planting aligns with sustainable development by enhancing agricultural efficiency, reducing the environmental impact, and supporting socio-economic aspects of sustainable farming. It also aids in developing tools and methods for monitoring and quantifying sustainability efforts in potato processing, such as in the production of French fries. Future research should focus on optimizing ultrasonic parameters and exploring the long-term effects of sonication on potato storage and processing qualities. Full article

(1) Background: Research and development in remote sensing have been used to determine and monitor crop phenology. This approach assesses the internal and external changes of the plant. Therefore, the objective of this study was to determine the potential of using a multispectral sensor to predict phenology in wild blueberry fields. (2) Method: A UAV equipped with a five-banded multispectral camera was used to collect aerial imagery. Sites consisted of two commercial fields, Lemmon Hill and Kemptown. An RCBD with six replications, four treatments, and a plot size of 6 × 8 m with a 2 m buffer between plots was used. Orthomosaic maps and vegetative indices were generated. (3) Results: There were significant correlations between VIs and growth parameters at different stages. The F4/F5 and F6/F7 stages showed significantly high correlation values among all growth stages. LAI, floral, and vegetative bud stages could be estimated at the tight cluster (F4/F5) and bloom (F6/F7) stages with R²/CCC = 0.90/0.84. Variable importance showed that NDVI, ENDVI, GLI, VARI, and GRVI contributed significantly to achieving these predicted values, with NDRE showing low effects. (4) Conclusion: This implies that the F4/F5 and F6/F7 stages are good stages for making phenological predictions and estimations about wild blueberry plants. Full article

To overcome the challenges during the crop growth process, e.g., pest infestation, inadequate environmental monitoring, and poor intelligence, this study proposes a crop growth information collection system based on a solar insecticidal lamp. The system comprises two primary modules: (1) an environmental information collection module, and (2) a multi-view image collection module. The environmental information collection module acquires crucial parameters, e.g., temperature, relative humidity, light intensity, soil conductivity, nitrogen, phosphorus, potassium content, and pH, by means of various sensors. Simultaneously, the multi-view image collection module employs three industrial cameras to capture images of the crop from the top, left, and right perspectives. The system is developed on the ESP32-S3 platform. WiFi-Mesh wireless communication technology is adopted to achieve high-frequency, real-time data transmission. Additionally, visualization software has been developed for real-time data display, data storage, and dynamic curve plotting. Field verification indicates that the proposed system effectively meets the requirements of pest control and crop growth information collection, which provides substantial support for the advancement of smart agriculture. Full article

A new compound [Y₂(sq)₃(H₂O)₄] (Y-sq; sq = squarate (C₄O₄^2–)) was prepared and structurally characterized. Since the RE-sq family (RE = Y, Dy, Yb, Lu) gave isostructural crystals, the objective of this study is to explore the effects of diamagnetic dilution on the SIM behavior through systematic investigation and comparison of diamagnetically diluted and undiluted forms. The 1%-Diluted Dy compounds, Dy@Y-sq and Dy@Lu-sq, showed AC magnetic susceptibility peaks without any DC bias field (H_DC), whereas undiluted Dy-sq showed no AC out-of-phase response under the same conditions. The Orbach and Raman mechanisms are assumed in the Arrhenius plots, giving U_eff/k_B = 139(5) and 135(8) K for Dy@Y-sq and Dy@Lu-sq, respectively, at H_DC = 0 Oe. In contrast, Yb@Y-sq and Yb@Lu-sq behaved different; Yb@Y-sq can be regarded as a field-induced SIM because AC out-of-phase response was recorded only when H_DC was present. On the other hand, Yb@Lu-sq showed a relaxation independent from temperature around 2 K at H_DC = 0 Oe, possibly ascribed to a quantum-tunneling-magnetization mechanism. Applying H_DC = 400 Oe afforded U_eff = 61.2(14) and 62.5(16) K for Yb@Y-sq and Yb@Lu-sq, respectively. The Y/Lu matrix dependence may be related to spin–phonon coupling. The dilution technique is a facile and versatile tool for modification of SIM characteristics. Full article

Innovative methods for estimating commercial-scale switchgrass yields and feedstock quality are essential to optimize harvest logistics and biorefinery efficiency for sustainable aviation fuel production. This study utilized vegetation indices (VIs) derived from multispectral images to predict biomass yield and lignocellulose concentrations of advanced bioenergy-type switchgrass cultivars (“Liberty” and “Independence”) under two N rates (28 and 56 kg N ha⁻¹). Field-scale plots were arranged in a randomized complete block design (RCBD) and replicated three times at Urbana, IL. Multispectral images captured during the 2021–2023 growing seasons were used to extract VIs. The results show that linear and exponential models outperformed partial least square and random forest models, with mid-August imagery providing the best predictions for biomass, cellulose, and hemicellulose. The green normalized difference vegetation index (GNDVI) was the best univariate predictor for biomass yield (R² = 0.86), while a multivariate combination of the GNDVI and normalized difference red-edge index (NDRE) enhanced prediction accuracy (R² = 0.88). Cellulose was best predicted using the NDRE (R² = 0.53), whereas hemicellulose prediction was most effective with a multivariate model combining the GNDVI, NDRE, NDVI, and green ratio vegetation index (GRVI) (R² = 0.44). These findings demonstrate the potential of UAV-based VIs for the in-season estimation of biomass yield and cellulose concentration. Full article

The correlation analysis between current surface cracks of structures and external loads can provide important insights into determining the structural residual bearing capacity. The classical regression assessment method based on experimental data not only relies on costly structure experiments; it also lacks interpretability. Therefore, a novel load estimation method for RC beams, based on correlation analysis between detected crack images and strain contour plots calculated by FEM, is proposed. The distinct discrepancies between crack images and strain contour figures, coupled with the stochastic nature of actual crack distributions, pose considerable challenges for load estimation tasks. Therefore, a new correlation index model is initially introduced to quantify the correlation between the two types of images in the proposed method. Subsequently, a deep neural network (DNN) is trained as a FEM surrogate model to quickly predict the structural strain response by considering material uncertainties. Ultimately, the range of the optimal load level and its confidence interval are determined via statistical analysis of the load estimations under different random fields. The validation results of RC beams under four-point bending loads show that the proposed algorithm can quickly estimate load levels based on numerical simulation results, and the mean absolute percentage error (MAPE) for load estimation based solely on a single measured structural crack image is 20.68%. Full article

Traditional field vegetation plot surveys are critical for monitoring ecosystem restoration performance and include visual observations to quantitatively measure plants (e.g., species composition and abundance). However, surveys can be costly, time-consuming, and only provide data at discrete locations, leaving potential data gaps across a site. Uncrewed aircraft system (UAS) technology can help fill data gaps between high-to-moderate spatial resolution (e.g., 1–30 m) satellite imagery, manned airborne data, and traditional field surveys, yet it has not been thoroughly evaluated in a virtual capacity as an alternative to traditional field vegetation plot surveys. This study assessed the utility of UAS red-green-blue (RGB) and low-altitude imagery for virtually surveying vegetation plots in a web application and compared to traditional field surveys at two coastal marsh restoration sites in southeast Louisiana, USA. Separate expert botanists independently observed vegetation plots in the field vs. using UAS imagery in a web application to identify growth form, species, and coverages. Taxa richness and assemblages were compared between field and virtual vegetation plot survey results using taxa resolution (growth-form and species-level) and data collection type (RGB imagery, Anafi [low-altitude] imagery, or field data) to assess accuracy. Virtual survey results obtained using Anafi low-altitude imagery compared better to field data than those from RGB imagery, but they were dependent on growth-form or species-level resolution. There were no significant differences in taxa richness between all survey types for a growth-form level analysis. However, there were significant differences between each survey type for species-level identification. The number of species identified increased by approximately two-fold going from RGB to Anafi low-altitude imagery and another two-fold from Anafi low-altitude imagery to field data. Vegetation community assemblages were distinct between the two marsh sites, and similarity percentages were higher between Anafi low-altitude imagery and field data compared to RGB imagery. Graminoid identification mismatches explained a high amount of variance between virtual and field similarity percentages due to the challenge of discriminating between them in a virtual setting. The higher level of detail in Anafi low-altitude imagery proved advantageous for properly identifying lower abundance species. These identifications included important taxa, such as invasive species, that were overlooked when using RGB imagery. This study demonstrates the potential utility of high-resolution UAS imagery for increasing marsh vegetation monitoring efficiencies to improve ecosystem management actions and outcomes. Restoration practitioners can use these results to better understand the level of accuracy for identifying vegetation growth form, species, and coverages from UAS imagery compared to field data to effectively monitor restored marsh ecosystems. Full article