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The multi-mode fatigue failure process in a plain-weave woven carboncarbon fabric reinforced polyester matrix composite has been investigated. Enhanced dye penetrant X-ray radiography, scanning electron fractography, optical microscopy, and edge replication techniques have been employed to examine the fatigue damage mechanisms. The 8 ply laminates were obtained from polyester resin-prepreg plain-weave carbon-carbon fabric layers, stacked in three different stacking sequences producing a unidirectional (0)8 and two angle-plied (0,0,45,-45)s, and (45,-45,0,0)s, fiber orientations. It is shown that the fatigue failure in the angle-plied (0,0,45,-45)s and (45,-45,0,0)s laminates, occurs through a multi-mode damage process, which involves matrix cracking, fiber-matrix debonding, fiber fracture, and delamination. Delamination of the stacked plies was noted to be the dominant damage process, which led to the eventual fatigue fracture in the angle-plied laminates. In the unidirectional (0)8 laminates, the fatigue failure occurred in a more simple, yet a rather more catastrophic manner. In these laminates, instead of delamination, the fatigue failure occurred predominantly by a fiber fracture process.

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The natural phenomenon of harmful algae bloom (HAB) has a bad impact on the quality of pure and freshwater. It increases the risk to human health, water bodies and overall aquatic ecosystem. It is necessary to continuously monitor and perform proper action against HAB. The inspection of algae blooms by using conventional methods, like algae detection under microscopes, is a difficult, expensive, and time-consuming task, however, computer vision-based deep learning models play a vital role in identifying and detecting harmful algae growth in aquatic ecosystems and water reservoirs. Many studies have been conducted to address harmful algae growth by using a CNN based model, however, the YOLO model is considered more accurate in identifying the algae. This advanced deep learning method is extensively used to detect algae and classify them according to their corresponding category. In this study, we used various versions of the convolution neural network (CNN) based on the You Only Look Once (YOLO) model. Recently YOLOv5 has been getting more attention due to its performance in real-time object detection. We performed a series of experiments on our custom microscopic images dataset by using YOLOv3, YOLOv4, and YOLOv5 to detect and classify the harmful algae bloom (HAB) of four classes. We used pre-processing techniques to enhance the quantity of data. The mean average precision (mAP) of YOLOv3, YOLOv4, and YOLO v5 is 75.3%, 83.0%, and 91.0% respectively. For the monitoring of algae bloom in freshwater, computer-aided based systems are very helpful and effective. To the best of our knowledge, this work is pioneering in the AI community for applying the YOLO models to detect algae and classify from microscopic images.

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The maize crop is used as food for humans, livestock and poultries forms, it is also used in bread making, corn flakes, corn syrup, corn starch and corn oils. The field study consisted of one experimental trial, about the incidence of that insect pest complex on maize cultivar Azam during the Kharif season 2020 at the Agricultural, Research Station, Baffa, Mansehra. The trial was laid out in the Randomized Complete Block Design (RCBD), and then it was divided into three replications. The result obtained from the trial showed that a number of the pest species were recorded during the experimental period; however, the population was noticed at a low level. The obtained insect species were corn leaf aphid (6.90 ± 5.5) per square inch, corn leafhopper (1.32 ± 0.63), maize stem borer (0.63 ± 0.29), corn flea beetle (0.43 ± 0.28), Thrips (0.38 ± 0.22), Hairy caterpillar (0.21 ± 0.22), Grasshopper (0.17 ± 0.11) and shoot fly (0.11 ± 0.08) throughout the season

Considering the economic importance of the maize Zea mays (Poales: Poaceae) crop and the losses caused by the maize stem borer, an experiment was carried out in a plot of 15 m 2 that was equally divided into three replications using randomized complete block design to find the effect of granular and liquid insecticides on Chilo partellus and crop yield. The trial was laid out at the Agricultural Research Station in Baffa, Mansehra, Pakistan during the Kharif season 2015. In this experiment, granular insecticide Carbofuran 3G and liquid insecticides Cypermethrin10 EC, Endosulfan 35 EC, Karate 2.5 EC and Chlorpyrifos 10 EC were used for treating maize stem borer. The obtained results revealed that Carbofuran 3G proved significantly to be the most effective in reducing plant damage, the number of dead heart plants and the number of pest larvae. Additionally, the treatments caused an increase in grain yield. The liquid insecticides Cypermethrin and Karate ranked second, and Endosulfan and Chlorpyrifos ranked third in efficacy against the pest. On the basis of present findings, it was recommended that the granular form of insecticides in general and Carbofuran 3G in particular may be used for effective control of C. partellus in maize crop. The granules should be applied in plant whorls, which will give better results than application through other methods. In the case of foliar applications, Cypermethrin is recommended to get effective control among other liquid formulations. Although the chemical treatments which were used in this research gave good control of C. partellus infestations and increase in yield as compared to check, some variations still occur in the amount of damage, population density of the pest and yield of crop. These differences may be due to differences in types of treatments, methods of experimentation, and environmental factors like temperature, humidity etc.

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