Shikonin and its derivatives, isolated from traditional medicinal plant species of the genus Lithospermum, Alkanna, Arnebia, Anchusa, Onosma, and Echium belonging to the Boraginaceae family, have numerous applications in foods, cosmetics, and textiles. Shikonin, a potent bioactive red pigment, has been used in traditional medicinal systems to cure various ailments and is well known for its diverse pharmacological potential such as anticancer, antithrombotic, neuroprotective, antidiabetic, antiviral, anti-inflammatory, anti-gonadotropic, antioxidants, antimicrobial and insecticidal. Herein, updated research on the natural sources, pharmacology, toxicity studies, and various patents filed worldwide related to shikonin and approaches to shikonin’s biogenic and chemical synthesis are reviewed. Furthermore, recent studies to establish reliable production systems to meet market demand, functional identification, and future clinical development of shikonin and its derivatives against various diseases are presented.
Green energy is a crucial component in addressing expanding energy demands and combating climate change, but the possible negative repercussions of these technologies are frequently disregarded. Green energy’s deployment is tied to environmentally sustainable development goals (SDGs). It can only be achieved by scaling up the finance of investment that provides environmental benefits through new financial instruments and new policies, such as green banks, green bonds, community-based green funds, green central banking, etc. In an effort to address the issues with IPAT and ImPACT, this study employed the STIRPAT model approach, which is a proven framework for energy economics analysis. The author gathers yearly data spanning 2002–2018 for six ASEAN member countries with the aim of investigating the relationship between CO2 emissions, green finance, energy efficiency, and the green energy index (GEX). After preliminary tests, the study employed the Westerlund test and Johansen Fisher test for long-term equilibrium and estimated the Granger causal links between variables using the generalized method of moments (GMM). The results indicate that green bonds are an effective technique for promoting green energy projects and considerably reducing CO2 emissions. Therefore, governments should establish supporting policies with a long-term perspective to increase the investment of green energy projects related investment from private participants to ensure sustainable growth and address environmental challenges. This strategy may be appropriate during and after the COVID-19 period.
To combat cyber threats in the smart grid, an intrusion detection system can be integrated into the advanced metering infrastructure. Anomaly-based intrusion detection can detect even the tiniest changes in the parameter under investigation, whereas signature-based intrusion detection only recognises known attacks. The growing usage of smart grids necessitates the classification, identification, and implementation of countermeasures to threats. At the absolute least, smart grids must be protected against cyberattacks; thus, the highest level of information security must be offered. As a result of digitisation and the usage of more smart applications, the research looked at a variety of attack types, smart grid assaults, and major cyber threats on the voltage regulation. Machine learning techniques that analyse data in real time and formulate patterns to recognise an attack and scan through huge data for anomalies can be implemented into the advanced metering infrastructure (AMI) for intrusion detection for anomaly-based intrusion detection. The comparative test study done for the research found that the proposed method, median absolute deviation for anomaly identification in smart metering datasets, produced the most accurate and precise differentiations with the highest accuracy and precision. The median absolute deviation (MAD) algorithm model is trained using test data, and raw predictions are made, before true data are used to derive final test result parameters, precision, recall, and F1 scores. The methodology of the entire study is discussed in this paper, as well as how the MAD algorithm is best suited for anomaly-based intrusion detection, as well as result comparisons of other machine learning algorithms.
The influence of medicinal plants on humanity spans time immemorial. These plants are also used at present with local and tribal peoples for the cures of various illnesses. Nature has produced an immense number of medicinal plants, which directly or indirectly help to treat various ailments and have numerous applications in the fields of pharmaceuticals, agriculture, food flavors and preservatives, aromas, and cosmetics. Bergenia pacumbis (Buch.-Ham. ex D.Don) C.Y.Wu & J.T.Pan (synonym: Bergenia ligulate Engl.), is an important medicinal plant belonging to the Saxifragaceae family, and not to be confused with Bergenia ciliata (Haw.) Sternb., and is popularly known as Pashanbheda (meaning to dissolve the kidney stone). This plant is a rich source of secondary metabolites (SMs) such as coumarins, flavonoids, benzenoids, lactones, tannins, phenols, and sterols, which make this plant a highly valued medicinal herb with a broad spectrum of pharmacological activities such as anti-urolithic, antioxidant, anti-viral, free radical scavenging, antidiabetic, anti-hepatotoxic, diuretic, antipyretic, anti-oxaluria, anti-tumour, antibacterial, antifungal, anti-inflammatory, antimicrobial, and cardioprotective. This review summarizes traditional uses and offers up to date data for future research on B. pacumbis.
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