Marine-derived natural products are rich source of secondary metabolites with huge potentials including novel therapeutic agents. Marine algae are considered to be a good source of secondary metabolites with versatile bioactivities. During the last few decades, researches related to natural products obtained from brown algae have remarkably escalated as they contain active compounds with varied biologically activities like antimicrobial, anticancer, antioxidant, anti-inflammatory, antidiabetic, and antiparasitic properties. The main bioactive components such as phlorotannin, fucoxanthin, alginic acid, fucoidan, and laminarin have been briefly discussed here, together with their composition and biological activities. In this review, the biological function of extracts and the metabolites of brown algae as well as their pharmacological impacts with the description of the possible mechanism of their action are described and discussed. Also, this study is expected to examine the multifunctional properties of brown algae that facilitate natural algal products, including the ability to integrate these functional properties in a variety of applications.

SARS-CoV-2 is a novel virus, responsible for causing the COVID-19 pandemic that has emerged as a pandemic in recent years. Humans are becoming infected with the virus. In 2019, the city of Wuhan reported the first-ever incidence of COVID-19. COVID-19 infected people have symptoms that are related to pneumonia, and the virus affects the body’s respiratory organs, making breathing difficult. A real-time reverse transcriptase-polymerase chain reaction (RT-PCR) kit is used to diagnose the disease. Due to a shortage of kits, suspected patients cannot be treated promptly, resulting in disease spread. To develop an alternative, radiologists looked at the changes in radiological imaging, like CT scans, that produce comprehensive pictures of the body of excellent quality. The suspected patient’s computed tomography (CT) scan is used to distinguish between a healthy individual and a COVID-19 patient using deep learning algorithms. A lot of deep learning methods have been proposed for COVID-19. The proposed work utilizes CNN architectures like VGG16, DeseNet121, MobileNet, NASNet, Xception, and EfficientNet. The dataset contains 3873 total CT scan images with “COVID” and “Non-COVID.” The dataset is divided into train, test, and validation. Accuracies obtained for VGG16 are 97.68%, DenseNet121 is 97.53%, MobileNet is 96.38%, NASNet is 89.51%, Xception is 92.47%, and EfficientNet is 80.19%, respectively. From the obtained analysis, the results show that the VGG16 architecture gives better accuracy compared to other architectures.

Image fusion can be performed on images either in spatial domain or frequency domain methods. Frequency domain methods will be most preferred because these methods can improve the quality of edges in an image. In image fusion, the resultant fused images will be more informative than individual input images, thus more suitable for classification problems. Artificial intelligence (AI) algorithms play a significant role in improving patient’s treatment in the health care industry and thus improving personalized medicine. This research work analyses the role of image fusion in an improved brain tumour classification model, and this novel fusion-based cancer classification model can be used for personalized medicine more effectively. Image fusion can improve the quality of resultant images and thus improve the result of classifiers. Instead of using individual input images, the high-quality fused images will provide better classification results. Initially, the contrast limited adaptive histogram equalization technique preprocess input images such as MRI and SPECT images. Benign and malignant class brain tumor images are applied with discrete cosine transform-based fusion method to obtain fused images. AI algorithms such as support vector machine classifier, KNN classifier, and decision tree classifiers are tested with features obtained from fused images and compared with the result obtained from individual input images. Performances of classifiers are measured using the parameters accuracy, precision, recall, specificity, and 1 score. SVM classifier provided the maximum accuracy of 96.8%, precision of 95%, recall of 94%, specificity of 93%, 1 score of 91%, and performed better than KNN and decision tree classifiers when extracted features from fused images are used. The proposed method results are compared with existing methods and provide satisfactory results.

Increased environmental concerns and global warming have diverted effort all over the world to focus on renewable and sustainable resources for the next generation of composite products due to their recyclability, renewability, cost effectiveness, and satisfactory mechanical performance. Bio/natural fibers which are environment friendly materials employed as reinforcement have led to developing a biocomposite for reduction in greenhouse emission and carbon footprints. However, biofibers are also having some limitations that need to be addressed including poor compatibility between the reinforcing fiber matrices, high moisture absorption, swelling, poor chemical and fire resistance, and high dispersion of mechanical properties. A lot of research has been performed on physical and mechanical properties of natural fiber composite. Properties of such novel composite mainly depend on adhesion between fiber and matrices. Consequently, poor adhesion, high moisture absorption, and swelling lead to formation of crack in both the matrix and fiber. Therefore, numerous techniques have been tried till date to modify both fiber surfaces to enhance their adhesion and reduce their water absorption. This review article provides comprehensive information about effect of various surface modification techniques that include alkaline, silane, acetylation, permanganate, peroxide, benzoylation, acrylonitrile grafting, maleic anhydride grafted, acrylation, and isocyanate. In addition, the effects of cellulose, hemicellulose, lignin, and pectin of biofibers are also reported. This review concluded that chemical treatment of biofibers with 5% NaOH concentration improves the physical, mechanical, and thermal properties of the resulting composites compared to untreated fiber composites.

In recent decades, research in nanomaterial specific to biodegradation has gained attraction owing to its physicochemical characteristics. Biodegradation is the main strategy used in the management of wastes. The role of nanoparticles helps in controlling the biodegradation rate. The biodegradation might not be quite effective in certain cases like high chemical deposition, due to its toxic nature towards microorganisms. The conventional strategies demonstrated a limited use of nature source, and this could be overcome through microbes and enzyme‐based biodegradation. Nanoparticles have improved the biodegradation rate through low‐density polyethylene development, thereby corrupting microbes. Thus, a major problem is confronted. Though innovation in science had a great impact in everyone’s life, it also has a negative impact through the increased use of toxic materials. Recent development and use of biodegradable nano‐based compounds have led to many secured forms of nanomedicines. In this review, we would discuss the more recent findings of nanoparticles related to biodegradation applications and elaborate how their characteristics could influence in various biomedical applications.

For comparison, the drilling behaviour of abaca fiber‐reinforced polymer (AFRP) composites and Kevlar‐reinforced epoxy polymer (KFRP) composites has been studied in the specified experimental condition. The different geometrical drilling tools have been used for the investigation, namely, candlestick (T1), core (T2), standard twist drill (T3), and step cone (T4). The tool feed of 30, 45, and 60 m/min and rotational speed of 1000, 1500, and 2000 rpm have been used for the investigation. The thrust force is chosen as a response parameter for this study. The results revealed that, at lesser rotational speed and tool feed, the thrust force has declined. The result obtained correlates with the abaca fiber‐based systems. However, the thrust force of KFRP is higher compared to AFRP composite systems. The axial force generated by candlestick drill is minimal compared to the other drill bits. The following may be responsible for lower thrust force: (1) the axial force distributes circumferential of the cutting tool instead of focusing at the center and (2) the interfacial adhesiveness between the matrix and the fiber is higher. The optimization of drilling process parameters, namely, tool feed and rotational speed on thrust force, has been studied. The results reveal that the tool feed contributed more to axial force compared to rotational speed.