The early detection of damaged (partially broken) outdoor insulators in primary distribution systems is of paramount importance for continuous electricity supply and public safety. Unmanned aerial vehicles (UAVs) present a safer, autonomous, and efficient way to examine the power system components without closing the power distribution system. In this work, a novel dataset is designed by capturing real images using UAVs and manually generated images collected to overcome the data insufficiency problem. A deep Laplacian pyramid-based super-resolution network is implemented to reconstruct high-resolution training images. To improve the visibility of low-light images, a low-light image enhancement technique is used for the robust exposure correction of the training images. A different fine-tuning strategy is implemented for fine-tuning the object detection model to increase detection accuracy for the specific faulty insulators. Several flight path strategies are proposed to overcome the shuttering effect of insulators, along with providing a less complex and time- and energy-efficient approach for capturing a video stream of the power system components. The performance of different object detection models is presented for selecting the most suitable one for fine-tuning on the specific faulty insulator dataset. For the detection of damaged insulators, our proposed method achieved an F1-score of 0.81 and 0.77 on two different datasets and presents a simple and more efficient flight strategy. Our approach is based on real aerial inspection of in-service porcelain insulators by extensive evaluation of several video sequences showing robust fault recognition and diagnostic capabilities. Our approach is demonstrated on data acquired by a drone in Swat, Pakistan.
Alzheimer's disease (AD) is a brain illness that causes gradual memory loss. AD has no treatment and cannot be cured, so early detection is critical. Various AD diagnosis approaches are used in this regard, but Magnetic Resonance Imaging (MRI) provides the most helpful neuroimaging tool for detecting AD. In this paper, we employ a DenseNet-201 based transfer learning technique for diagnosing different Alzheimer's stages as Non-Demented (ND), Moderate Demented (MOD), Mild Demented (MD), Very Mild Demented (VMD), and Severe Demented (SD). The suggested method for a dataset of MRI scans for Alzheimer's disease is divided into five classes. Data augmentation methods were used to expand the size of the dataset and increase DenseNet-201's accuracy. It was found that the proposed strategy provides a very high classification accuracy. This practical and reliable model delivers a success rate of 98.24%. The findings of the experiments demonstrate that the suggested deep learning approach is more accurate and performs well compared to existing techniques and state-of-the-art methods.
Sphaeranthus indicus L. is a medicinal herb having widespread traditional uses for treating common ailments. The present research work aims to explore the in-depth phytochemical composition and in vitro reactivity of six different polarity solvents (methanol, n-hexane, benzene, chloroform, ethyl acetate, and n-butanol) extracts/fractions of S. indicus flowers. The phytochemical composition was accomplished by determining total bioactive contents, HPLC-PDA polyphenolic quantification, and UHPLC-MS secondary metabolomics. The reactivity of the phenolic compounds was tested through the following biochemical assays: antioxidant (DPPH, ABTS, FRAP, CUPRAC, phosphomolybdenum, and metal chelation) and enzyme inhibition (AChE, BChE, α-glucosidase, α-amylase, urease, and tyrosinase) assays were performed. The methanol extract showed the highest values for phenolic (94.07 mg GAE/g extract) and flavonoid (78.7 mg QE/g extract) contents and was also the most active for α-glucosidase inhibition as well as radical scavenging and reducing power potential. HPLC-PDA analysis quantified rutin, naringenin, chlorogenic acid, 3-hydroxybenzoic acid, gallic acid, and epicatechin in a significant amount. UHPLC-MS analysis of methanol and ethyl acetate extracts revealed the presence of well-known phytocompounds; most of these were phenolic, flavonoid, and glycoside derivatives. The ethyl acetate fraction exhibited the highest inhibition against tyrosinase and urease, while the n-hexane fraction was most active for α-amylase. Moreover, principal component analysis highlighted the positive correlation between bioactive compounds and the tested extracts. Overall, S. indicus flower extracts were found to contain important phytochemicals, hence could be further explored to discover novel bioactive compounds that could be a valid starting point for future pharmaceutical and nutraceuticals applications.