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Barely visible impact damage (BVID) is one of the most dangerous types of structural damage in composites, since in most practical cases the application of advanced non-destructive testing (NDT) methods is required to detect and identify it. Due to its character of propagation, there are minor signs of structural damage on a surface, while the internal damage can be broad and complex both in the point of view of fracture mechanisms and resulting geometry of damage. The most common NDT method applied e.g., in aircraft inspections is ultrasonic testing (UT), which enables effective damage detection and localization in various environments. However, the results of such inspections are usually misestimated with respect to the true damage extent, and the quantitative analysis is biased by an error. In order to determine the estimation error a comparative analysis was performed on NDT results obtained for artificially damaged carbon fiber-reinforced composite structures using two UT methods and X-ray computed tomography (CT). The latter method was considered here as the reference one, since it gives the best spatial resolution and estimation accuracy of internal damage among the available NDT methods. Fusing the NDT results for a set of pre-damaged composite structures with various energy values of impact and various types of impactor tips applied for introducing damage, the evaluation of estimation accuracy of UT was possible. The performed analysis allowed for evaluation of relations between UT and X-ray CT NDT results and for proposal of a correcting factor for UT results for BVID in the analyzed composite structures.

The occurrence of barely visible impact damage (BVID) in aircraft composite components and structures being in operation is a serious problem, which threatens structural safety of an aircraft, and should be timely detected and, if necessary, repaired according to the obligatory regulations of currently applied maintenance methodologies. Due to difficulties with a proper detection of such a type of damage even with non-destructive testing (NDT) methods as well as manual evaluation of the testing results, supporting algorithms for post-processing of these results seem to be of a high interest for aircraft maintenance community. In the following study, the authors proposed new approaches for BVID reconstruction based on results of ultrasonic and X-ray computed tomographic testing using authored advanced image processing algorithms. The studies were performed on real composite structures taking into consideration failure mechanisms occurring during impact damaging. The developed algorithms allow extracting relevant diagnostic information both from ultrasonic B-and C-Scans as well as from tomographic 3D arrays used for the validation of ultrasonic reconstructed damage locations, which allows for a significant improvement of the detectability of BVID in tested structures. The developed approach can be especially useful for NDT operators evaluating the results of structural NDT inspections.

Traditional inspection methods often fall short in detecting defects or damage in fibre-reinforced polymer (FRP) composite structures, which can compromise their performance and safety over time. A prime example is barely visible impact damage (BVID) caused by out-of-plane loadings such as indentation and low-velocity impact that can considerably reduce the residual strength. Therefore, developing advanced visual inspection techniques is essential for early detection of defects, enabling proactive maintenance and extending the lifespan of composite structures. This study explores the viability of using novel bio-inspired hybrid composite sensors for detecting BVID in laminated FRP composite structures. Drawing inspiration from the colour-changing mechanisms found in nature, hybrid composite sensors composed of thin-ply glass and carbon layers are designed and attached to the surface of laminated FRP composites exposed to transverse loading. A comprehensive experimental characterisation, including quasi-static indentation and low-velocity impact tests alongside non-destructive evaluations such as ultrasonic C-scan and visual inspection, is conducted to assess the sensors’ efficacy in detecting BVID. Moreover, a comparison between the two transverse loading types, static indentation and low-velocity impact, is presented. The results suggest that integrating sensors into composite structures has a minimal effect on mechanical properties such as structural stiffness and energy absorption, while substantially improving damage visibility. Additionally, the influence of fibre orientation of the sensing layer on sensor performance is evaluated, and correlations between internal and surface damage are demonstrated.

Cereal cyst nematodes (CCNs) are among the most important nematode pests that limit production of small grain cereals like wheat and barley. These nematodes alone are estimated to reduce production of crops by 10% globally. This necessitates a huge enhancement of nematode resistance in cereal crops against CCNs. Nematode resistance in wheat and barley in combination with higher grain yields has been a preferential research area for cereal nematologists. This usually involved the targeted genetic exploitations through natural means of classical selection breeding of resistant genotypes and finding quantitative trait luci (QTLs) associated with resistance genes. These improvements were based on available genetic diversity among the crop plants. Recently, genome-wide association studies have widely been exploited to associate nematode resistance or susceptibility with particular regions of the genome. Use of biotechnological tools through the application of various transgenic strategies for enhancement of nematode resistance in various crop plants including wheat and barley had also been an important area of research. These modern approaches primarily include the use of gene silencing, exploitation of nematode effector genes, proteinase inhibitors, chemodisruptive peptides and a combination of one or more of these approaches. Furthermore, the perspective genome editing technologies including CRISPR-Cas9 could also be helpful for improving CCN resistance in wheat and barley. The information provided in this review will be helpful to enhance resistance against CCNs and will attract the attention of the scientific community towards this neglected area.

Two different experimental techniques are employed to visualize the impact damage generated by a low-velocity impact on a carbon-fibre reinforced-polymer (CFRP) composite laminate. At the relatively low impact-velocity of 1.69 m.s−1, and a corresponding impact energy of 7.5 J, used in the present work the damage induced in the CFRP panel is barely visible to the naked eye but the techniques of ultrasonic C-scan and X-ray computed tomography (CT) can detect the damage that has occurred. This damage is mostly interlaminar damage, i.e. delaminations, between the plies due to a change in modulus from one ply to the next in the laminate. This interlaminar damage is usually accompanied by intralaminar damage, e.g. matrix cracking, in the ply itself. The type and extent of damage detected from using these two techniques is discussed and the relative merits of these techniques are compared. In general, the CT gave the better resolved picture of damage but the lateral extent of the damage was underestimated relative to C-scan which was more sensitive to very fine delamination cracks. In addition, a numerical approach, based on a finite-element analysis model, is employed to predict the type, location and extent of damage generated by the impact event and the modelling predictions are compared to the experimental results.

Trogoderma granarium Everts (Khapra beetle) is a serious pest of stored barley and wheat worldwide. The current research evaluates the efficacy of local isolates of Meterhizium anisopliae and Beauvaria bassiana compared to an imported isolates when khapra larvae sprayed with concentration of later isolates under laboratory condition, mortalities were recorded for more than two weeks after treatment. Results demonstrated that the larvae were more susceptible to the local isolates of the two fungi than the imported isolates. Cumulative mortalities were 86.67 and 50 when larvae sprayed with 10 9 conidia/ml of local and imported isolates of M. anisopliae respectively, and 86.67 and 66.67 when larvae sprayed with 10 9 conidia/ml of laocal and imported isolates of B. bassiana respectively after two weeks. The findings of the current study clearly showed that the local isolates caused higher mortalities than the imported one so they may be used as a good candidates for pragmas of integrated pest management of stored grain pests.

This study was aimed to improve the rheological and qualitative properties of the most common bread, wheat bread, by barley pentosanes, which is not optimally utilized although it's many benefits and its huge production. Therefore, water-soluble pentosanes (WSP) and water-insoluble pentosanes (WIP) were extracted from barley  (Hordeum vulgare) and added to wheat flour at different percentages. Five treatments were prepared: (T1) from flour only, (T2 and T3) adding WSP at levels(1 and 2%), (T4 and T5) adding WIP at the same levels respectively. The results showed significant improvement in rheological and qualitative properties of the dough and bread made from this dough with increasing the level of addition. The water absorption, the mixing time and the stability time of the dough for  T3 and T5, increased from (61.9%) to (64.8 and 65.3%) and from (2min) to ( 2.4 and 3 min) and from (11 min) to (17 and 18 min)respectively,  as compared with T1 . Additionally, total scores of sensory evaluation increased from (89.1) to (95.7 and 89.3), the specific volume increased from (4.37 cm3/gm) to (4.79 and 4.54 cm3/gm). The chemical composition analysis  showed an increase in the percentages of  protein and fat for the same treatments ( T3 & T5 ) as the values  increased from (9.34%) to (10.55 and 9.98%) and from (0.85%) to (1.03 and 0.94%) respectively. The percentages of fibre and ash were  also increased from (1.71%) to (1.89 and 2.10%) and from (0.74%) to (1.68 and 1.65%) respectively. While the total calorie decreased from (2.73.63 cal) to (261.98 and 253.18 cal).                                                       

With the increasingly comprehensive utilisation of Carbon Fibre-Reinforced Polymers (CFRP) in modern industry, defects detection and characterisation of these materials have become very important and draw significant research attention. During the past 10 years, Artificial Intelligence (AI) technologies have been attractive in this area due to their outstanding ability in complex data analysis tasks. Most current AI-based studies on damage characterisation in this field focus on damage segmentation and depth measurement, which also faces the bottleneck of lacking adequate experimental data for model training. This paper proposes a new framework to understand the relationship between Barely Visible Impact Damage features occurring in typical CFRP laminates to their corresponding controlled drop-test impact energy using a Deep Learning approach. A parametric study consisting of one hundred CFRP laminates with known material specification and identical geometric dimensions were subjected to drop-impact tests using five different impact energy levels. Then Pulsed Thermography was adopted to reveal the subsurface impact damage in these specimens and recorded damage patterns in temporal sequences of thermal images. A convolutional neural network was then employed to train models that aim to classify captured thermal photos into different groups according to their corresponding impact energy levels. Testing results of models trained from different time windows and lengths were evaluated, and the best classification accuracy of 99.75% was achieved. Finally, to increase the transparency of the proposed solution, a salience map is introduced to understand the learning source of the produced models.

Fusarium is a common fungal disease in grains that reduces the yield of barley and wheat. In this study, a near infrared reflectance spectroscopic technique was used with a statistical prediction model to rapidly and non-destructively discriminate grain samples contaminated with Fusarium. Reflectance spectra were acquired from hulled barley, naked barley, and wheat samples contaminated with Fusarium using near infrared reflectance (NIR) spectroscopy with a wavelength range of 1175–2170 nm. After measurement, the samples were cultured in a medium to discriminate contaminated samples. A partial least square discrimination analysis (PLS-DA) prediction model was developed using the acquired reflectance spectra and the culture results. The correct classification rate (CCR) of Fusarium for the hulled barley, naked barley, and wheat samples developed using raw spectra was 98% or higher. The accuracy of discrimination prediction improved when second and third-order derivative pretreatments were applied. The grains contaminated with Fusarium could be rapidly discriminated using spectroscopy technology and a PLS-DA discrimination model, and the potential of the non-destructive discrimination method could be verified.

The problem of characterizing the structural residual life is one of the most challenging issues of the damage tolerance concept currently applied in modern aviation. Considering the complexity of the internal architecture of composite structures widely applied for aircraft components nowadays, as well as the additional complexity related to the appearance of barely visible impact damage, prediction of the structural residual life is a demanding task. In this paper, the authors proposed a method based on detection of structural damage after low-velocity impact loading and its classification with respect to types of acting stress on constituents of composite structures using the developed processing algorithm based on segmentation of 3D X-ray computed tomograms using the rebmix package, real-oriented dual-tree wavelet transform and supporting image processing procedures. The presented algorithm allowed for accurate distinguishing of defined types of damage from X-ray computed tomograms with strong robustness to noise and measurement artifacts. The processing was performed on experimental data obtained from X-ray computed tomography of a composite structure with barely visible impact damage, which allowed better understanding of fracture mechanisms in such conditions. The gained knowledge will allow for a more accurate simulation of structural damage in composite structures, which will provide higher accuracy in predicting structural residual life.