Explore the vast range of titles available.

Poor shelling quality degrades the performance and profit of the peanut industry. The Hertz theory and the wear resistance experimental method were applied to identify a highly wear-resistant material guaranteeing a low mechanical damage rate (MDR) of peanut kernels. The Box–Behnken design method was applied in the experiment to illustrate the influence of the material’s elastic modulus (X1), radius of curvature of the key parts (X2), rotating speed of the shelling drum (X3), and clearance between the shelling drum and concave screen (X4) on MDR and shelling efficiency (SE). Depending on the analysis of variance, the weights of the influential factors were observed as X1 > X3 > X4 > X2. The mathematical models of MDR and SE were derived from the least squares’ method, and four-dimensional slice diagrams of the three most significant factors were used to illustrate the trends of MDR and SE. A multi-objective analysis provided the optimal combination of parameters as: X1 = 10 MPa, X2 = 12.77 mm, X3 = 277.48 r/min, and X4 = 24.24 mm, yielding MDR = 4.89% and SE = 97.91%. The results were further verified by a production trial test, proving that the proposed solution with the selected material, machine design, and working parameters were effective in improving peanut shelling quality.

Tree leaves emit biogenic volatile organic compounds (BVOCs) in response to mechanical wounding by insect folivores. However, BVOCs are also released from leaves in response to damage to other tree organs. In this study, we hypothesized that if trees utilize BVOCs to defend against leaf herbivory, BVOCs emitted in response to leaf wounding would be different from those emitted in response to other types of mechanical damage. To test this hypothesis, we measured BVOCs emitted from the leaves of four Ficus species in response to leaf-cutting, branch-cutting, leaf-branch-cutting, and control (constitutive BVOCs). We found that leaf-cutting triggered the emission of BVOCs, but their emission patterns were species-specific, and the overall BVOC composition did not significantly differ from that of constitutive BVOCs. In contrast, branch-cutting triggered the emission of many BVOCs, some known as parasitoid attractants and herbivore deterrents. Our study suggests that plant defense mediated by BVOCs is highly species-specific and not effective for attracting herbivore enemies when unrelated disturbances such as tree falls and windstorms occur. Additionally, we recommend avoiding ex situ BVOC sampling of cut plants, as this method alters BVOC emission patterns from both intact and damaged leaves.

Plants respond to stress by releasing biogenic volatile organic compounds (BVOCs). Green leaf volatiles (GLVs), which are abundantly produced across the plant kingdom, comprise an important group within the BVOCs. They can repel or attract herbivores and their natural enemies; and they can induce plant defences or prime plants for enhanced defence against herbivores and pathogens and can have direct toxic effects on bacteria and fungi. Unlike other volatiles, GLVs are released almost instantly upon mechanical damage and (a)biotic stress and could thus function as an immediate and informative signal for many organisms in the plant’s environment. We used a meta-analysis approach in which data from the literature on GLV production during biotic stress responses were compiled and interpreted.We identified that different types of attackers and feeding styles add a degree of complexity to the amount of emitted GLVs, compared with wounding alone. This meta-analysis illustrates that there is less variation in the GLV profile than we presumed, that pathogens induce more GLVs than insects and wounding, and that there are clear differences in GLV emission between monocots and dicots. Besides the meta-analysis, this review provides an update on recent insights into the perception and signalling of GLVs in plants.

Polymer composite materials are being increasingly used in primary load-bearing structures in several advanced industrial fields such as aerospace vessels, railway wagons and mega-scaled wind turbines where detection of subcritical damage initiation can significantly reduce safety issues and maintenance costs. It is therefore crucial to inspect these composite structures in order to assess their structural health and to ensure their integrity. Non-destructive testing techniques (NDT) are used for this purpose, making it possible to monitor mechanical damage of composite materials under in situ or ex situ service conditions. This paper reviews the capabilities of the most common NDT techniques used to inspect the integrity of composite materials. Each technique has a detection potential and cannot allow a full diagnosis of the mechanical damage state of the material. Thus, depending on the occurring damage mechanism and the conditions of use, one technique will be preferred over another, or several techniques should be combined to improve the diagnosis of the damage state of the structures.

Deep learning has become a widely used powerful tool in many research fields, although not much so yet in agriculture technologies. In this work, two deep convolutional neural networks (CNN), viz. Residual Network (ResNet) and its improved version named ResNeXt, are used to detect internal mechanical damage of blueberries using hyperspectral transmittance data. The original structure and size of hypercubes are adapted for the deep CNN training. To ensure that the models are applicable to hypercube, we adjust the number of filters in the convolutional layers. Moreover, a total of 5 traditional machine learning algorithms, viz. Sequential Minimal Optimization (SMO), Linear Regression (LR), Random Forest (RF), Bagging and Multilayer Perceptron (MLP), are performed as the comparison experiments. In terms of model assessment, k-fold cross validation is used to indicate that the model performance does not vary with the different combination of dataset. In real-world application, selling damaged berries will lead to greater interest loss than discarding the sound ones. Thus, precision, recall, and F1-score are also used as the evaluation indicators alongside accuracy to quantify the false positive rate. The first three indicators are seldom used by investigators in the agricultural engineering domain. Furthermore, ROC curves and Precision-Recall curves are plotted to visualize the performance of classifiers. The fine-tuned ResNet/ResNeXt achieve average accuracy and F1-score of 0.8844/0.8784 and 0.8952/0.8905, respectively. Classifiers SMO/ LR/RF/Bagging/MLP obtain average accuracy and F1-score of 0.8082/0.7606/0.7314/0.7113/0.7827 and 0.8268/0.7796/0.7529/0.7339/0.7971, respectively. Two deep learning models achieve better classification performance than the traditional machine learning methods. Classification for each testing sample only takes 5.2 ms and 6.5 ms respectively for ResNet and ResNeXt, indicating that the deep learning framework has great potential for online fruit sorting. The results of this study demonstrate the potential of deep CNN application on analyzing the internal mechanical damage of fruit.

Garlic is commonly known as Allium sativum L. belongs to “Alliaceae” family. In India, garlic is planted manually or by seed drill, and due to uneven seed dropping, seed losses increase. The precision planting implies accurate spacing of single seeds in the row with proper planting depth and creating a uniform germination environment for each seed. The cup-feed-type metering mechanism were designed and developed considering the physical properties of cloves of GG-4 garlic variety and evaluated the performance in soil bin. The developed mechanism was evaluated at three size of metering cups (10, 20, and 30% more than maximum clove dimension) with three forward speeds (2.5 km h−1, 3.0 km h−1, and 3.5 km h−1). The final operational parameters of metering mechanism was decided on the basis of average clove spacing, miss index, multiple index, quality of feed index, precision, and mechanical clove damage. The cup size 10% more than maximum clove dimension with 2.5 km h−1 forward speed showed better results of all performance parameters. The quality of feed index, precision and visible mechanical damage in garlic cloves were found to be 86.82, 15.80 and 5.51%, respectively.

Insects are the most abundant and diverse class of animals on the planet. One explanation for their success is their extraordinary ability to successfully consume a wide range of foods. Like all heterotrophic organisms, insects need to acquire vital nutrients from their diet. The central organ for food digestion and absorption of nutrients is the gastrointestinal tract. This organ’s principal functions are mediating the efficient digestion of the diet and protecting the organism against harmful chemicals, microorganisms, and mechanical damage from the food. These functions are achieved through regional differentiation of the alimentary canal as well as highly flexible adaptations to the consumed diets, both at anatomical and molecular levels. Numerous studies describing the general gut morphology and associated digestive mechanisms of various insects exist. Nevertheless, the molecular patterns underlying digestion and nutrient uptake in insects are still poorly characterized. This review aims to provide an overview of the general strategies of extracellular macronutrient digestion and consequent nutrient absorption found among different orders of insects.

As the aging population increases, the global incidence of cardiovascular diseases is rapidly growing, and vascular intervention has become a mainstream treatment method for vascular diseases. However, due to the complexity of the in vivo environment, mechanical interactions such as compression, adhesion, and friction occur between the catheter and vascular tissue during the intervention process, which can cause varying degrees of tissue damage. This review examined the interaction mechanism between vascular tissue and interventional catheters in response to mechanical damage in interventional surgery. It also provided an overview of the work on surface modification of interventional catheters using hydrophilic lubricated and anti-adhesion coatings. The current paper aims to reduce postoperative complications for patients with cardiovascular disease and improve the safety and accuracy of cardiovascular surgery.

Microplastics pose great risks to terrestrial systems owing to their large quantity and strong persistence. Higher plants, an irreplaceable part of the terrestrial ecosystem, are inevitably exposed to microplastics. This review highlights the effects of microplastics on higher plant growth and performance. The tested microplastics, plant species, and cultural methods used in existing studies were summarized. We discussed the reasons why these microplastics, plants, and methods were selected. The various responses of higher plants to microplastics in both soils and waters were critically reviewed. We also highlighted the influencing mechanisms of microplastics on higher plants. Conclusively, more than 13 types of common microplastics and more than 30 species of higher plants have been selected and studied by the published literatures. Soil culture tests and hydroponic experiments are almost equally divided. The effects of microplastics on higher plants varied among microplastic properties, plant species, and environmental factors. Microplastics had no or positive effects on higher plants under certain experimental conditions. However, more studies showed that microplastics can inhibit higher plant growth and performance. We reduced the inhibitory mechanisms into direct and indirect mechanisms. The direct mechanisms include blocking pores or light, causing mechanical damage to roots, hindering genes expression, and releasing additives. The indirect mechanisms contain changing soil properties, affecting soil microbes or soil animals, and affecting bioavailability of other pollutants. This review improves the understanding of effects and influencing mechanisms of microplastics on higher plants.

The technology of liquid carbon dioxide phase change fracturing (LCPCF) was used to enhance the permeability of coal seams. The combination of mechanical tests, scanning electron microscopy (SEM) and high-pressure mercury intrusion porosimetry was adopted to study the damage characteristics of coal micro-structures. LCPCF had mechanical damage effects on coal micro-structures to varying degrees, and the maximum reduction in compressive strength reached approximately 25%. SEM results confirmed that surface morphology of coal was remarkably altered after conducting LCPCF. The fractal dimension ( D ) of coal subjected to LCPCF ranged from 1.5186 to 1.8794, demonstrating the three-stage changing trends. HP-MIP results showed that LCPCF mainly affected pores of > 100 nm within coal, and pores < 100 nm were hardly influenced. When 1.26 L of liquid CO 2 was used to conduct physical blasting, at distance of < 1.5 m, the influence of LCPCF was strengthened. Affected by the high-energy gas and shock wave generated by LCPCF, meso-pores within coal were damaged and shifted to the larger pores, resulting in the increase in the number of macro-pores and micro-fractures. When distance was > 1.5 m, the obvious reduction in macro-pore and micro-fracture volumes implied that the fracturing effect was attenuated with the increase in distance. Once distance was > 6.0 m, pore and fracture structures within coal tended to be stable. Thus, in this study, the influence scope of LCPCF was around 6.0 m for a single fracturing borehole.