Search Results (7,136)

Early plant disease detection is crucial for sustainable crop production and food security. Stem rust, caused by Puccinia graminis f. sp. tritici, poses a major threat to wheat and barley. This study evaluates the feasibility of using hyperspectral imaging and machine learning for early detection of stem rust and examines the cross-crop transferability of diagnostic models. Hyperspectral datasets of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) were collected under controlled conditions, before visible symptoms appeared. Multi-stage preprocessing, including spectral normalization and standardization, was applied to enhance data quality. Feature engineering focused on spectral curve morphology using first-order derivatives, categorical transformations, and extrema-based descriptors. Models based on Support Vector Machines, Logistic Regression, and Light Gradient Boosting Machine were optimized through Bayesian search. The best-performing feature set achieved F1-scores up to 0.962 on wheat and 0.94 on barley. Cross-crop transferability was evaluated using zero-shot cross-domain validation. High model transferability was confirmed, with F1 > 0.94 and minimal false negatives (<2%), indicating the universality of spectral patterns of stem rust. Experiments were conducted under controlled laboratory conditions; therefore, direct field transferability may be limited. These findings demonstrate that hyperspectral imaging with robust preprocessing and feature engineering enables early diagnostics of rust diseases in cereal crops. Full article

Electrospinning is a versatile technique used to manufacture nanofibers by applying an electric field to a polymer solution. This method has gained significant interest in the biomedical, pharmaceutical, and materials engineering fields due to its ability to produce porous structures with a high specific surface area, making it ideal for applications such as wound dressings, controlled drug delivery systems, and tissue engineering. The materials used in electrospinning play a crucial role in determining the final properties of the obtained nonwoven nanofibers. Among the most studied substances are chitosan, collagen, and fish-derived gelatin, which are biopolymers with high biocompatibility. These materials are especially used in the medical and pharmaceutical fields due to their bioactive properties. In combination with synthetic polymers such as polyethylene glycol (PEG) and polyvinyl alcohol (PVA), these biopolymers can form electrospun fibers with improved mechanical characteristics and enhanced structural stability. The characterization of these materials was performed using modern characterization techniques, such as one-dimensional (1D) proton NMR spectroscopy (¹H), for which the spin–spin relaxation time distributions T₂ were characterized. Additionally, two-dimensional (2D) measurements were conducted, for which EXSY T₂-T₂ and COSY T₁-T₂ exchange maps were obtained. The characterization was complemented with FT-IR spectra measurements, and the nanofiber morphology was observed using SEM. As a novelty, machine learning methods, including artificial neural networks (ANNs), were applied to characterize the local structural order of the produced nanofibers. In this study, it was shown that the nanofiber nonwoven materials made from PVA are characterized by a degree of order in the range of 0.27 to 0.61, which are more ordered than the nanofibers made from chitosan and fish gelatin, characterized by an order degree ranging from 0.051 to 0.312, where 0 represents the completely unordered network and 1 a fully ordered fabric. Full article

Aero-engine ablation detection is a critical task in aircraft health management, yet existing rotation-based object detection methods often face challenges of high computational complexity and insufficient local feature extraction. This paper proposes an improved YOLOv11 algorithm incorporating Context-guided Large-kernel attention and Rotated detection head, called CLR-YOLOv11. The model achieves synergistic improvement in both detection efficiency and accuracy through dual structural optimization, with its innovations primarily embodied in the following three tightly coupled strategies: (1) Targeted Data Preprocessing Pipeline Design: To address challenges such as limited sample size, low overall image brightness, and noise interference, we designed an ordered data augmentation and normalization pipeline. This pipeline is not a mere stacking of techniques but strategically enhances sample diversity through geometric transformations (random flipping, rotation), hybrid augmentations (Mixup, Mosaic), and pixel-value transformations (histogram equalization, Gaussian filtering). All processed images subsequently undergo Z-Score normalization. This order-aware pipeline design effectively improves the quality, diversity, and consistency of the input data. (2) Context-Guided Feature Fusion Mechanism: To overcome the limitations of traditional Convolutional Neural Networks in modeling long-range contextual dependencies between ablation areas and surrounding structures, we replaced the original C3k2 layer with the C3K2CG module. This module adaptively fuses local textural details with global semantic information through a context-guided mechanism, enabling the model to more accurately understand the gradual boundaries and spatial context of ablation regions. (3) Efficiency-Oriented Large-Kernel Attention Optimization: To expand the receptive field while strictly controlling the additional computational overhead introduced by rotated detection, we replaced the C2PSA module with the C2PSLA module. By employing large-kernel decomposition and a spatial selective focusing strategy, this module significantly reduces computational load while maintaining multi-scale feature perception capability, ensuring the model meets the demands of high real-time applications. Experiments on a self-built aero-engine ablation dataset demonstrate that the improved model achieves 78.5% mAP@0.5:0.95, representing a 4.2% improvement over the YOLOv11-obb which model without the specialized data augmentation. This study provides an effective solution for high-precision real-time aviation inspection tasks. Full article

This study investigates torsional vibration characteristics in an aged coastal car ferry propulsion system using theoretical calculations based on the Matrix method alongside experimental measurements. While the measured torsional vibration at the propeller shaft remained within the limits, it was significantly higher than the calculated values, particularly at the 5th harmonic order excited by engine combustion. Negative torque peaks observed during transient clutch engagement caused gear hammering. Structural vibration analysis identified potential gearbox defects, such as wear or misalignment. Multiple torsional vibration calculation models were developed considering various degrees of degradation of the aged rubber blocks and viscous torsional damper. A model assuming that the damping capacity of damper drops to about 1%, corresponding to the specified values at 125 °C, produced results that closely reproduced the measured vibration characteristics. The finding, confirmed by an actual inspection, identifies viscous oil leakage and deterioration of the damper as the primary cause of excessive vibration. Prompt replacement of the viscous oil is recommended to improve torsional vibration behavior. Full article

This study introduces a novel control strategy aimed at achieving projective synchronization in incommensurate fractional-order chaotic systems (IFOCS). The approach integrates the mathematical framework of fractional calculus with the recursive structure of the backstepping control technique. A key feature of the proposed method is the systematic use of the Mittag–Leffler function to verify stability at every step of the control design. By carefully constructing the error dynamics and proving their asymptotic convergence, the method guarantees the overall stability of the coupled system. In particular, stabilization of the error signals around the origin ensures perfect projective synchronization between the master and slave systems, even when these systems exhibit fundamentally different fractional-order chaotic behaviors. To illustrate the applicability of the method, the proposed fractional order backstepping control (FOBC) is implemented for the synchronization of two representative systems: the fractional-order Van der Pol oscillator and the fractional-order Rayleigh oscillator. These examples were deliberately chosen due to their structural differences, highlighting the robustness and versatility of the proposed approach. Extensive simulations are carried out under diverse initial conditions, confirming that the synchronization errors converge rapidly and remain stable in the presence of parameter variations and external disturbances. The results clearly demonstrate that the proposed FOBC strategy not only ensures precise synchronization but also provides resilience against uncertainties that typically challenge nonlinear chaotic systems. Overall, the work validates the effectiveness of FOBC as a powerful tool for managing complex dynamical behaviors in chaotic systems, opening the way for broader applications in engineering and science. Full article

Wood-based materials in the form of wood veneer composites (WVCs) possess a high lightweight construction potential for load-bearing applications in mechanical engineering due to their high strength properties combined with low density. However, in order to substitute energy-intensive metallic construction materials (such as steel or aluminum), additional structural space is required to compensate for the comparatively low stiffness by means of the area moment of inertia. Under bending loads, an increase in cross-sectional height at a constant span length leads to elevated shear stresses. Owing to the low shear strength and stiffness of wood-based materials, the influence of shear stresses must be considered in both the design of wooden components and in material testing. Current standards for determining the bending properties of wood-based materials only describe methods for assessing pure bending behavior, without accounting for shear effects. The present contribution introduces a method for determining both bending and shear properties of WVC using the three-point bending test. This approach allows for the derivation of bending and shear modulus values through an analytical model based on Timoshenko beam theory by testing various span-to-height ratios. These modulus values represent material constants and enable the numerical design of wooden components for arbitrary geometric parameters. Full article

Despite the rapid progress in the capabilities of large language models, they still lack a reliable and efficient method of storing and retrieving new information conveyed over the course of their interaction with users upon deployment. In this paper, we use reinforcement learning methods to train a team of smaller language models, which we frame as options, on reward-respecting subtasks, to learn to use SQL commands to store and retrieve relevant information to and from an external SQL database. In particular, we train a storage language model on a subtask for distinguishing between user and assistant in the dialogue history, to learn to store any relevant facts that may be required to answer future user queries. We then train a retrieval language model on a subtask for querying a sufficient number of fields, to learn to retrieve information from the SQL database that could be useful in answering the current user query. We find that training our models on their respective subtasks results in much higher performance than training them to directly optimize the reward signal and that the resulting team of language models is able to achieve performance on memory tasks comparable to existing methods that rely on language models orders of magnitude larger in size. In particular, we were able to able to achieve a 36% gain in accuracy over a prompt engineering baseline and a 13% gain over a strong baseline that uses the much larger GPT-3.5 Turbo on the MSC-Self-Instruct dataset. Full article

The objective of this research endeavor is to present engineering data pertaining to the size and shape characteristics of strawberries, which have a wide range of applications in industry, and to obtain the data necessary for the development and design of product processing systems. In this study, standard strawberry varieties were utilized, and analyses were conducted by means of an image-processing method. The projection area (601.5–762.0 mm²), length (34.0 mm), width (28.6 mm) and surface area (28.6 cm²) of the strawberry samples were measured in the horizontal and vertical orientation, in order to ascertain their size characteristics. Furthermore, the sphericity (86.1%) and roundness (1.039–1.087) parameters were calculated for the shape characteristics, accordingly. The findings of the correlation analysis suggested that the size parameters of the fruits exerted no influence on fruit shape characteristics. In the elliptic Fourier analysis performed to reveal the shape differences in the fruit, the contour geometry of each fruit sample was extracted, the principal component (PC) scores describing the shape were obtained and the shape categories of the fruit were determined. Following the analysis of the PCs, it was determined that 90.77% of the total shape variance was explained by the first seven components. Consequently, the shape of the strawberry fruit was defined as a spherical cone. Following the implementation of a discriminant analysis in conjunction with a clustering process, which categorized the samples into seven distinct shape categories employing the k-means algorithm, an accuracy rate of 94.1% was achieved. Full article

To address the engineering challenges of powertrain excitation noise and aggravated low-frequency interior noise caused by armored structures in special-purpose vehicles, this study proposes an in-vehicle acoustic response analysis method based on vibro-acoustic coupling theory. This study presents a method for analyzing in-vehicle acoustic response under engine excitation, integrating Panel Acoustic Contribution Analysis (PACA) with a vibro-acoustic coupling model tailored for armored vehicles. The framework experimentally reveals a condition-independent resonance at 26.5 Hz and reproduces engine-order peaks at 40 Hz, 93.3 Hz, and 140 Hz. Quantitative comparison shows ΔSPL ≤ 2.5 dB and RMSE ≤ 2.2 dB between simulation and experiment, confirming model robustness. Based on these results, conceptual Dynamic Vibration Absorber (DVA) placement guidelines are proposed for dominant panels, providing practical engineering insights for NVH mitigation in armored vehicles. Full article

Tight gas reservoirs are characterized by low porosity, low permeability, and strong heterogeneity. CO₂ flooding, as an important approach for enhancing gas recovery while achieving carbon sequestration, is often restricted by gas channeling. Based on the sandstone reservoir parameters of the Shihezi Formation in the Ordos Basin, a two-dimensional fracture–matrix coupled numerical model was developed to systematically investigate the effects of fracture number, fracture inclination, fracture width, injection pressure, and permeability contrast on gas breakthrough time and sweep efficiency. A second-order regression model was further established using response surface methodology (RSM). The results show that a moderate fracture density can extend breakthrough time and improve sweep efficiency, while permeability contrast is the fundamental factor controlling gas channeling risk. When the contrast increases from 0.7 to 9.9, the breakthrough efficiency decreases from 88.5% to 68.9%. The response surface analysis reveals significant nonlinear interactions, including the coupled effects of fracture number with fracture width, injection pressure, and inclination angle. Under the optimized conditions, the breakthrough time can be extended to 46,984 h, with a corresponding sweep efficiency of 87.7%. These findings provide a quantitative evaluation method and engineering optimization guidance for controlling CO₂ channeling in tight gas reservoirs. Full article

To improve the accuracy of second-order cell-centered finite volume method in near-boundary regions for solving the two-dimensional shallow water equations, a numerical scheme with globally second-order accuracy was proposed. Having the primary objective to overcome the challenge of accuracy degradation in near-boundary regions and to develop a robust numerical framework combining high-order accuracy with strict conservation, the key research objectives had been as follows: Firstly, a physical variable reconstruction method combining a vertex-based nonlinear weighted reconstruction scheme and a monotonic upwind total variation diminishing scheme for conservation laws was proposed. While the overall computational efficiency was maintained, linear-exact reconstruction in near-boundary regions was achieved. The variable reconstruction in interior regions was integrated to achieve global second-order accuracy. Subsequently, a flux boundary condition treatment method based on uniform flow was proposed. Conservative allocation of hydraulic parameters was achieved, and flow stability in inflow regions was enhanced. Finally, a series of numerical test cases were provided to validate the performance of the proposed method in solving the shallow water equations in terms of high-order accuracy, exact conservation properties, and shock-capturing capabilities. The superiority of the method was further demonstrated under high-speed flow conditions. The high-precision numerical model developed in this study holds significant value for enhancing the predictive capability of simulations for natural disasters such as flood propagation and tsunami warning. Its robust boundary treatment methods also provide a reliable tool for simulating free-surface flows in complex environments, offering broad prospects for engineering applications. Full article

Surface finishing processes are required to produce the final shape of components made of the silicon-infiltrated silicon carbide composite Cesic^® from ECM (Engineered Ceramic Materials GmbH, 85452 Moosinning, Germany). Electrical discharge machining (EDM) is still the most effective method for manufacturing pockets and mounts in 3D-shaped ceramic satellite components for space applications. NC-grinding is not used, because it results in high grinding loads and rapid tool wear when applied to Cesic^®. In contrast to planar machining, tool wear during NC-grinding with small tools is particularly critical, as it alters the tool geometry and consequently causes deviations in the workpiece geometry. Ultrasonic-assisted grinding offers a promising alternative to overcome the low material removal rates and long processing times associated with EDM while simultaneously enhancing tool life, thus enabling more economical and reliable production. In this experimental study, both conventional grinding (CG) and ultrasonic-assisted grinding (UAG) processes are compared and used to machine Cesic^®. In order to verify the effect of the ultrasonic vibration, analyses of amplitude and frequency are performed. During machining experiments, the grinding loads are measured. The influence of different machining conditions on surface quality is evaluated concerning the roughness of the machined specimens. Compared to CG, UAG shows lower tool wear, owing to the self-cleaning effects caused by the ultrasonic oscillation of the tool. Consequently, the stability of the NC-grinding process is significantly improved. Full article

This paper introduces a novel three-parameter probability model, the unit-Gompertz–Makeham (UGM) distribution, designed for modeling bounded data on the unit interval (0,1). By transforming the classical Gompertz–Makeham distribution, we derive a unit-support distribution that flexibly accommodates a wide range of shapes in both the density and hazard rate functions, including increasing, decreasing, bathtub, and inverted-bathtub forms. The UGM density exhibits rich patterns such as symmetric, unimodal, U-shaped, J-shaped, and uniform-like forms, enhancing its ability to fit real-world bounded data more effectively than many existing models. We provide a thorough mathematical treatment of the UGM distribution, deriving explicit expressions for its quantile function, mode, central and non-central moments, mean residual life, moment-generating function, and order statistics. To facilitate parameter estimation, eight classical techniques, including maximum likelihood, least squares, and Cramér–von Mises methods, are developed and compared via a detailed simulation study assessing their accuracy and robustness under varying sample sizes and parameter settings. The practical relevance and superior performance of the UGM distribution are demonstrated using two real-world engineering datasets, where it outperforms existing bounded models, such as beta, Kumaraswamy, unit-Weibull, unit-gamma, and unit-Birnbaum–Saunders. These results highlight the UGM distribution’s potential as a versatile and powerful tool for modeling bounded data in reliability engineering, quality control, and related fields. Full article

Chloroplast codon usage bias (CUB) records both maternal phylogeny and selection intensity. Characterizing CUB in the synthetic cereal × Triticosecale and its Triticum and Secale parents is therefore a prerequisite for plastid-based engineering and for tracing the evolutionary consequences of recent allopolyploidy. Complete plastome sequences of five taxa—Triticum monococcum, T. turgidum, T. aestivum, Secale cereale and × Triticosecale sp.—were downloaded. Protein-coding genes were extracted to calculate overall GC, GC1–GC3, SCUO, RSCU, ENC-GC3s, neutrality, and PR2 plots. Optimal codons were defined as RSCU ≥ 1 and △RSCU ≥ 0.8. The results showed that the chloroplast genomes of these five species are low in GC content for the third base of codons, suggesting an end preference for A or U bases. The SCUO values ranged from 0.22 to 0.23, suggesting no significant codon usage bias. GC content was relatively low (38.78–39.16%), with the order GC1 > GC2 > GC3. RSCU analysis indicated that codons ending with A/T are more commonly used. Neutral mapping, ENC-GC3s, and the PR2 plot all showed that the preference of codon usage for the majority of functional genes was influenced by a combination of mutation and natural selection pressure, and the influence of natural selection was predominant. RSCU clustering recovers the expected maternal tree (Triticum clade + triticale). All optimal codons terminate with A or U, yielding identical plastid translation tables for the five species. Despite its recent hybrid origin, triticale plastid CUB is indistinguishable from its wheat maternal ancestor and is governed mainly by selection. The compiled optimal codon set provides an immediate reference for chloroplast transformation and for dissecting selection relaxation in newly synthesized triticale combinations. Full article

Edible mushrooms have gained global popularity due to their nutritional value, medicinal properties, bioactive compounds and industrial applications. Despite their long-standing roles in ecology, nutrition, and traditional medicine, their additional functions in cultivation, breeding, and classification processes are still in their infancy due to technological constraints. The advent of Artificial Intelligence (AI) technologies has transformed the cultivation process of mushrooms, genetic breeding, and classification methods. However, the analysis of the application of AI in the mushroom production cycle is currently scattered and unorganized. This comprehensive review explores the application of AI technologies in mushroom cultivation, breeding, and classification. Four databases (Scopus, IEEE Xplore, Web of Science, and PubMed) and one search engine (Google Scholar) were used to perform a thorough review of the literature on the utility of AI in various aspects of the mushroom production cycle, including intelligent environmental control, disease detection, yield prediction, germplasm characterization, genotype–phenotype integration, genome editing, gene mining, multi-omics, automatic species identification and grading. In order to fully realize the potential of these edge-cutting AI technologies in transforming mushroom breeding, classification, and cultivation, this review addresses challenges and future perspectives while calling for interdisciplinary approaches and multimodal fusion. Full article