Nanotechnology is an interdisciplinary domain of science, technology and engineering that deals with nano-sized materials/particles. Usually, the size of nanoparticles lies between 1-100 nm. Due to their small size and large surface area-to-volume ratio, nanoparticles exhibit high reactivity, greater stability and adsorption capacity. These important physicochemical properties attract scientific community to utilize them in biomedical field. Various types of nanoparticles (inorganic and organic) have broad applications in medical field ranging from imaging to gene therapy. These are also effective drug carriers. In recent times, nanoparticles are utilized to circumvent different treatment limitations. For example, the ability of nanoparticles to cross the blood brain barrier and having a certain degree of specificity towards amyloid deposits, makes themselves important candidates for the treatment of Alzheimer’s disease. Furthermore, nanotechnology has been used extensively to overcome several pertinent issues like drug-resistance phenomenon, side effects of conventional drugs and targeted drug delivery issue in leprosy, tuberculosis and cancer. Thus, in this review, the application of different nanoparticles for the treatment of these four important diseases (Alzheimer’s disease, tuberculosis, leprosy and cancer) as well as for the effective delivery of drugs used in these diseases has been presented systematically. Although nanoformulations have many advantages over traditional therapeutics for treating these diseases, nanotoxicity is a major concern which has been discussed subsequently. Lastly, we have presented the promising future prospective of nanoparticles as alternative therapeutics. In that section, we have discussed about the futuristic approach(es) that could provide promising candidate(s) for the treatment of these four diseases.
Energy consumption is a crucial domain in energy system management. Recently, it was observed that there has been a rapid rise in the consumption of energy throughout the world. Thus, almost every nation devises its strategies and models to limit energy usage in various areas, ranging from large buildings to industrial firms and vehicles. With technological advancements, computational intelligence models have been successfully contributing to the prediction of the consumption of energy. Machine learning and deep learning-based models enhance the precision and robustness compared to traditional approaches, making it more reliable. This article performs a review analysis of the various computational intelligence approaches currently being utilized to predict energy consumption. An extensive survey procedure is conducted and presented in this study, and relevant works are discussed. Different criteria are considered during the aggregation of the relevant studies relating to the work. The author’s perspective, future trends and various novel approaches are also presented as a part of the discussion. This article thereby lays a foundation stone for further research works to be undertaken for energy prediction.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.