Search Results (29,946)

Investigating the optimal planting strategies for brassica vegetables under variable climatic conditions is essential for developing sustainable production systems in northern agricultural regions. However, comprehensive knowledge about how planting timing modulates growth, physiological responses, and yield parameters across different cultivars remains limited. We investigated vegetative development, root morphology, physiological efficiency, and marketable yield in six cauliflower cultivars (‘Amazing’, ‘Cheddar’, ‘Clementine’, ‘Flame Star’, ‘Snow Crown’, and ‘Vitaverde’) subjected to four planting dates (May 1, May 15, June 1, and June 15) across two growing seasons (2023–2024), followed by detailed morphological and physiological profiling. Planting date, cultivar selection, and seasonal variation significantly influenced all measured parameters (p < 0.001), with notable interaction effects observed for fresh root weight, stomatal conductance, water use efficiency, and yield components. Early planted cultivars consistently demonstrated superior performance under variable environmental conditions, maintaining higher growth rates, enhanced root development, and improved physiological efficiency, particularly ‘Flame Star’, ‘Snow Crown’, and ‘Cheddar’, compared to late-planted treatments. Recovery of optimal plant development was most pronounced at May planting dates, with early-established crops showing better maintenance of vegetative growth patterns and enhanced yield potential, including higher curd weights (585.7 g for ‘Flame Star’) and superior marketable grades. Morphological profiling revealed distinct clustering patterns, with early-planted cultivars forming separate groups characterized by elevated root biomass, enhanced physiological parameters, and superior yield characteristics. In contrast, late-planted crops showed reduced performance, indicative of environmental stress responses. We conclude that strategic early planting significantly enhances cauliflower production resilience through comprehensive optimization of growth, physiological, and yield parameters, particularly under May establishment conditions. The differential performance responses between planting dates provide insights for timing-based management strategies, while the quantitative morphological and physiological profiles offer valuable parameters for assessing crop adaptation and commercial viability potential under variable climatic scenarios in northern agricultural systems. Full article

Innovation is central to the Zero Hunger agenda, yet its distributional links to agricultural performance and policy in Eastern Europe remain unclear. This study investigates whether national innovation performance, proxied by the Global Innovation Index, is associated with agriculture’s macroeconomic weight and with public budget orientation in Bulgaria, Czechia, Hungary, Poland, Romania, and Slovakia across the past decade and a half. Using panel quantile regression with country fixed effects and bootstrapped standard errors, we estimate effects at the lower, median, and upper parts of the outcome distributions for three indicators: agriculture value added share of gross domestic product, the agriculture orientation index for government expenditures, and the agriculture share of government expenditure. Results show a robust negative association between innovation and the agricultural share of gross domestic product that strengthens toward the upper quantiles, consistent with structural transformation that reallocates value added toward higher-productivity sectors. For the orientation index, innovation is unrelated at the lower and median parts but becomes positive in mid–upper regimes, fading again at the extreme upper tail. No systematic relationship emerges for the budget share. Land endowment is positively associated with agricultural weight, while population size is negatively associated. We conclude that economy-wide innovation aligns with structural change, whereas shifting agricultural budget shares requires targeted, sector-specific policy instruments. Full article

Over the past decade, recurring influxes of pelagic Sargassum have posed significant environmental and economic challenges in the Caribbean Sea. Effective monitoring is crucial for understanding bloom dynamics and mitigating their impacts. This study presents a comprehensive machine learning (ML) and deep learning (DL) framework for detecting Sargassum and estimating its fractional cover using imagery from key satellite sensors: the Operational Land Imager (OLI) on Landsat-8 and the Multispectral Instrument (MSI) on Sentinel-2. A spectral library was constructed from five core spectral bands (Blue, Green, Red, Near-Infrared, and Short-Wave Infrared). It was used to train an ensemble of five diverse classifiers: Random Forest (RF), K-Nearest Neighbors (KNN), XGBoost (XGB), a Multi-Layer Perceptron (MLP), and a 1D Convolutional Neural Network (1D-CNN). All models achieved high classification performance on a held-out test set, with weighted F1-scores exceeding 0.976. The probabilistic outputs from these classifiers were then leveraged as a direct proxy for the sub-pixel fractional cover of Sargassum. Critically, an inter-algorithm agreement analysis revealed that detections on real-world imagery are typically either of very high (unanimous) or very low (contentious) confidence, highlighting the diagnostic power of the ensemble approach. The resulting framework provides a robust and quantitative pathway for generating confidence-aware estimates of Sargassum distribution. This work supports efforts to manage these harmful algal blooms by providing vital information on detection certainty, while underscoring the critical need to empirically validate fractional cover proxies against in situ or UAV measurements. Full article

Objectives: This study proposes a systematic strategy for optimizing sustained-release formulations using mixture experiments. Methods: Model variables were identified and screened via LASSO regression, Smoothly Clipped Absolute Deviation (SCAD), and Minimax Concave Penalty (MCP), leading to the construction of a quadratic inference function-based objective model. Using this model, three multi-objective optimization algorithms—NSGA-III, MOGWO, and NSWOA—were employed to generate a Pareto-optimal solution set. Solutions were further evaluated through the entropy weight method combined with TOPSIS to reduce subjective bias. Results: The MCP-screened model demonstrated strong fit (AIC = 19.8028, BIC = 45.2951) and suitability for optimization. Among the Pareto-optimal formulations, formulation 45, comprising HPMC K4M (38.42%), HPMC K100LV (13.51%), MgO (6.28%), lactose (17.07%), and anhydrous CaHPO₄ (7.52%), exhibited superior performance, achieving cumulative release rates of 22.75%, 64.98%, and 100.23% at 2, 8, and 24 h, respectively. Compared with the original formulation, drug release was significantly improved across all time points. Conclusions: This integrated workflow effectively accounted for component interactions and repeated measurements, providing a robust and scientifically grounded approach for optimizing multi-component sustained-release formulations. Full article

Student course performance evaluation serves as a critical pedagogical tool for diagnosing learning gaps and enhancing educational outcomes, yet conventional assessments often suffer from rigid single-metric scoring and ambiguous causality. This study proposes an integrated analytic framework addressing these limitations by synergizing pedagogical expertise with data-driven diagnostics through four key measure: (1) Interval Analytic Hierarchy Process (I-AHP) to derive criterion weights reflecting instructional priorities via expert judgment; (2) K-means clustering to objectively stratify students into performance cohorts based on multidimensional metrics; (3) Random Forest classification and SHAP value analysis to quantitatively identify key discriminators of cluster membership and interpret decision boundaries; and (4) attribution-guided interventions targeting cohort-specific deficiencies. Leveraging a dual-channel ecosystem across pre-class, in-class, and post-class phases, we established a hierarchical evaluation system where I-AHP weighted pedagogical sub-criteria to generate comprehensive student scores. Full article

Rapid urbanization drives significant land use transformations, making the timely detection of newly constructed buildings a critical research focus. This study presents a novel unsupervised framework that integrates pixel-level change detection with object-level, mono-temporal building information to identify new constructions. Within this framework, we propose the Building Line Index (BLI) to capture structural characteristics from building edges. The BLI is then combined with spectral, textural, and the Morphological Building Index (MBI) to extract buildings. The fusion weight (φ) between the BLI and MBI was determined through experimental analysis to optimize performance. Experimental results on a case study in Wuhan, China, demonstrate the method’s effectiveness, achieving a pixel accuracy of 0.974, an average category accuracy of 0.836, and an Intersection over Union (IoU) of 0.515 for new buildings. Critically, at the object-level—which better reflects practical utility—the method achieved high precision of 0.942, recall of 0.881, and an F1-score of 0.91. Comparative experiments show that our approach performs favorably against existing unsupervised methods. While the single-case study design suggests the need for further validation across diverse regions, the proposed strategy offers a robust and promising unsupervised pathway for the automatic monitoring of urban expansion. Full article

Driver fatigue is a primary factor in traffic accidents and poses a serious threat to road safety. To address this issue, this paper proposes a multi-feature fusion fatigue detection method based on an improved YOLOv8 model. First, the method uses an enhanced YOLOv8 model to achieve high-precision face detection. Then, it crops the detected face regions. Next, the lightweight PFLD (Practical Facial Landmark Detector) model performs keypoint detection on the cropped images, extracting 68 facial feature points and calculating key indicators related to fatigue status. These indicators include the eye aspect ratio (EAR), eyelid closure percentage (PERCLOS), mouth aspect ratio (MAR), and head posture ratio (HPR). To mitigate the impact of individual differences on detection accuracy, the paper introduces a novel sliding window model that combines a dynamic threshold adjustment strategy with an exponential weighted moving average (EWMA) algorithm. Based on this framework, blink frequency (BF), yawn frequency (YF), and nod frequency (NF) are calculated to extract time-series behavioral features related to fatigue. Finally, the driver’s fatigue state is determined using a comprehensive fatigue assessment algorithm. Experimental results on the WIDER FACE and YAWDD datasets demonstrate this method’s significant advantages in improving detection accuracy and computational efficiency. By striking a better balance between real-time performance and accuracy, the proposed method shows promise for real-world driving applications. Full article

Direct seeding of winter rapeseed in the Yangtze River Basin often coincides with low temperatures during establishment. The aim of this study was to test whether low-temperature (LT) germination performance predicts overwintering survival and yield under delayed sowing. Thirty accessions were evaluated in controlled germination at 20/14 °C (CK) and 12/6 °C (LT) and in two field seasons (2020–2021 and 2021–2022), with six sowing dates from 15 October to 4 December. Mean germination rate was 97.6% in CK and 88.0% in LT. Germination potential (GP) averaged 95.7% in CK and 41.9% in LT. Root and shoot length decreased from 7.63 and 5.02 cm in CK to 1.47 and 0.48 cm in LT. Overwintering survival declined with later sowing. In the colder season (2020–2021), survival for sowings after November fell below 20% for most accessions, whereas S1–S2 averaged above 80%. Yield decreased with delay. In 2021–2022, yield under S1 exceeded S2–S6 by 5.5%, 8.5%, 13.9%, 14.0%, and 23.3%. In 2020–2021, S1 was similar to S2, but 6.3–22.8% higher than S3–S6. Thousand-seed weight followed the same trend. LT GP and LT root length were positively correlated with yield at several sowing dates in the colder season, indicating that LT germination traits are predictive of late-sown performance under harsher winters. Seven accessions (3409, M417, Zheza0903, 86155, 3445, Zheyou50, and 3462) showed superior LT germination and comparatively better field performance. For the lower Yangtze site, a practical latest safe sowing window is late October, based on two seasons; November sowing substantially increases winter mortality and yield risk. Selecting genotypes with strong LT germination and managing for rapid autumn establishment can stabilize 1000-seed weight and yield when sowing is delayed. Full article

This study investigated the impacts of dietary thymol–carvacrol cocrystal (CEO) supplementation on broiler production performance, antioxidant status, intestinal health, and cecal microbiota. Eight hundred one-day-old chicks were randomly divided into four groups, receiving basal diets supplemented with 0, 40, 60, or 80 mg/kg CEO. The results showed that CEO addition increased average daily gain, superoxide dismutase activity in the serum, liver, and jejunum, jejunal villus height/crypt depth ratio, cecal butyric acid concentration, and Lactobacillus abundance, while reducing serum alanine transaminase activity and malondialdehyde content in the serum, liver, and jejunum. Furthermore, 60 mg/kg CEO enhanced the final body weight, dressing percentage, serum total protein and glucose levels, and jejunal trypsin and amylase activities, while lowering the feed-to-gain ratio and serum cholesterol, urea nitrogen, and aspartate transaminase concentrations; it also increased the activities of superoxide dismutase, catalase, and glutathione and mRNA expressions of related genes in the liver and jejunum. It also increased cecal concentrations of acetic acid and isovalerate acid, while decreasing serum diamine oxidase and D-lactate concentrations, as well as malondialdehyde concentrations in the serum, liver, and jejunum. Therefore, dietary CEO supplementation improved the production performance, antioxidant status, and liver and gut health and function in broilers, with 60 mg/kg CEO demonstrating the most pronounced effects. Full article

Shape memory polymer composites (SMPCs) are promising materials in aerospace thanks to their light weight and ability to provide an actuation load during shape recovery, the magnitude of which depends on the laminates design. In this work, SMPCs were manufactured by alternating carbon fiber prepregs with a SM interlayer of epoxy resin. The number of composite plies ranged from 2 to 8 and two interlayer thicknesses were selected (100 μm and 200 μm in the lamination stage). Compression molding was performed for consolidation, and the interlayer’s thickness was reduced by edge bleeding. A thermo-mechanical cycle was applied for memorization. The shape fixity and the shape recovery of the vast majority of the SMPCs were above 90%, with the 200 μm/six-ply laminate recording the highest combination of values (94.8% and 95.7%, respectively). A significant effect due to the presence of a thicker interlayer was not evident, underlying the need to determine specific manufacturing procedures. Starting from these results, a lab-scale procedure was implemented to manufacture a smart device by embedding a microheater in the 200 μm/two-ply architecture. The device was memorized into a L-shape (90° bending angle), and a voltage of 24 V allowed it to recover 86.2° in 90 s, with a maximum angular velocity of 1.55 deg/s. Full article

In this study, supercritical carbon dioxide (scCO₂) was investigated as a sustainable medium for cotton yarn sizing and desizing, eliminating the need for water and conventional organic solvents. Cellulose acetate was employed as the sizing agent with acetone as a co-solvent, achieving a 10% add-on comparable to conventional starch-sized yarns. Since starch sizing is typically reported in the range of 3–10% add-on, a 3% starch level was selected as the industrially relevant benchmark for 20/1 cotton yarn. Trials conducted at 15–20 MPa and 40–60 °C demonstrated uniform size deposition and efficient removal during desizing, as confirmed by weight gain distribution and friction testing. Mechanical characterization further revealed that scCO₂-sized yarns exhibited tensile strength and break elongation within the range of industry benchmarks. Overall, these findings establish scCO₂-based sizing as a viable and eco-friendly alternative, with encouraging preliminary performance that suggests potential alignment with textile industry standards. The process also shows promise for solvent recovery and effluent reduction; however, full quantification of recovery yields, energy requirements, and wastewater impacts remains an important direction for future investigation. Full article

Recently developed quantitative structure–activity relationship (QSAR) prediction uses machine learning techniques with analytical signals from the full scan of mass spectra as input, and does not need exhaustive structural determination to assess unknown compounds. The QSAR approach assumes that a mass spectral pattern reflects the structure of a target chemical. However, the relationship between the spectrum and structure is complex, and requirement of its interpretation could restrict further development of QSAR prediction methods based on analytical signals. In this study, whether gas chromatography-electron-impact ionization-mass spectrometry (GC-EI-MS) data contain meaningful structural information that assists QSAR prediction was determined by comparing it with the traditional molecular descriptor used in QSAR prediction. Four molecular descriptors were used: ECFP6, topological descriptor in CDK, MACCS key, and PubChem fingerprint. The predictive performance of QSAR based on analytical and molecular descriptors was evaluated in terms of molecular weight, log K_o-w, boiling point, melting point, water solubility, and two oral toxicities in rats and mice. The influential variables were further investigated by comparing analytical-descriptor-based and linear regression models using simple indicators of the mass spectrum. The investigation indicated that the analytical and molecular descriptors preserved structural information differently. However, their performance was comparable. The analytical-descriptor-based approach predicted the physicochemical properties and toxicities of structurally unknown chemicals, which was beyond the scope of the molecular-descriptor-based approach. The QSAR approach based on analytical signals is valuable for evaluating unknown chemicals in many scenarios. Full article

Patterned fabrics are characterized by strong periodic and symmetric structures, and defect detection in such materials is essentially the task of identifying local disruptions of global texture symmetry. Conventional low-rank decomposition methods separate defect-free regions as low-rank and defects as sparse components, yet singular value decomposition (SVD)-based formulations inevitably lose structural details, hindering faithful recovery of symmetric background patterns. Autoencoder (AE)-based reconstruction provides nonlinear modeling capacity but tends to over-reconstruct defective areas, thereby reducing the separability between anomalies and symmetric textures. To address these challenges, this study proposes WOT-AE (Weighted Optimal Transport Autoencoder), a unified framework that exploits the inherent symmetry of patterned fabrics for robust defect detection. The framework integrates three key components: (1) AE-based low-rank modeling, which replaces SVD to preserve fine-grained repetitive patterns; (2) weighted sparse isolation guided by pixel-level priors, which suppresses false positives in symmetric but defect-free regions; and (3) optimal transport alignment in the encoder feature space, which enforces distributional consistency of symmetric textures while allowing deviations caused by asymmetric defects. Through extensive experiments on benchmark patterned fabric datasets, WOT-AE demonstrates superior performance over six state-of-the-art methods, achieving more accurate detection of symmetry-breaking defects with improved robustness. Full article

A parametric machine learning tool with many applications is the radial basis function (RBF) network, which has been incorporated into various classification and regression problems. A key component of these networks is their radial functions. These networks acquire adaptive capabilities through a technique that consists of two stages. The centers and variances are computed in the first stage, and in the second stage, which involves solving a linear system of equations, the external weights for the radial functions are adjusted. Nevertheless, in numerous instances, this training approach has led to decreased performance, either because of instability in arithmetic computations or due to the method’s difficulty in escaping local minima of the error function. In this manuscript, a three-stage method is suggested to address the above problems. In the first phase, an initial estimation of the value ranges for the machine learning model parameters is performed. During the second phase, the network parameters are fine-tuned within the intervals determined in the first phase. Finally, in the third phase of the proposed method, a local optimization technique is applied to achieve the final adjustment of the network parameters. The proposed method was evaluated on several machine learning models from the related literature, as well as compared with the original RBF training approach. This methodhas been successfully applied to a wide range of related problems reported in recent studies. Also, a comparison was made in terms of classification and regression error. It should be noted that although the proposed methodology had very good results in the above measurements, it requires significant computational execution time due to the use of three phases of processing and adaptation of the network parameters. Full article

Weeds continue to pose a serious challenge to agriculture, reducing both the productivity and quality of crops. In this paper, we explore how modern deep learning, specifically Vision Transformers (ViTs), can help address this issue through fast and accurate weed classification. We developed a transformer-based model trained on the DeepWeeds dataset, which contains images of nine different weed species collected under various environmental conditions, such as changes in lighting and weather. By leveraging the ViT architecture, the model is able to capture complex patterns and spatial details in high-resolution images, leading to improved prediction accuracy. We also examined the effects of model optimization techniques, including fine-tuning and the use of pre-trained weights, along with different strategies for handling class imbalance. While traditional oversampling actually hurt performance, dropping accuracy to 94%, using class weights alongside strong data augmentation boosted accuracy to 96.9%. Overall, our ViT model outperformed standard Convolutional Neural Networks, achieving 96.9% accuracy on the held-out test set. Attention-based saliency maps were inspected to confirm that predictions were driven by weed regions, and model consistency under location shift and capture perturbations was assessed using the diverse acquisition sites in DeepWeeds. These findings show that with the right combination of model architecture and training strategies, Vision Transformers can offer a powerful solution for smarter weed detection and more efficient farming practices. Full article