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.

The hunt for novel antibiotics has become a global public health imperative due to the rise in multidrug-resistant microorganisms, untreatable infection cases, overuse, and inefficacy of modern antibiotics. Polyphenols are getting much attention in research due to their multiple biological effects; their use as antimicrobial agents is attributed to their activity and that microbes have a hard time developing resistance to these natural compounds. Polyphenols are secondary metabolites produced in higher plants. They are known to possess various functional properties in the human body. Polyphenols also exhibit antibacterial activities against foodborne pathogens. Their antibacterial mechanism is based on inhibiting bacterial biofilm formation or inactivating enzymes. This review focused on polyphenol-protein interactions and the creation of this complex as a possible antibacterial agent. Also, different phenolic interactions on bacterial proteins, efflux pump, cell membrane, bacterial adhesion, toxins, and other bacterial proteins will be explored; these interactions can work in a synergic combination with antibiotics or act alone to assure bacterial inhibition. Additionally, our review will focus on polyphenol-protein interaction as a possible strategy to eradicate bacteria because polyphenols have shown a robust enzyme-inhibitory characteristic and a high tendency to complex with proteins, a response that neutralizes any bactericidal potential.

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.

Several economies have acknowledged that environmental degradation poses a serious danger to worldwide sustainable production and consumption. Policy makers concur that the increased use and production of carbon-intensive technologies has intensified the detrimental consequences of carbon dioxide emissions. In response, a number of nations have reacted by enacting stringent regulations and encouraging green technology innovations across corporate and governmental organizations. Evidence that already exists suggests that research and development is a cyclical process; nevertheless, the non-linear influence of shocks in research and development and innovation in green technologies on CO₂ emissions in the Nordic nations has not been well investigated. Using panel data from 1995 to 2019, this research explores the asymmetric link between innovation in green technologies and CO₂ emissions. The cointegration link between the chosen variables was validated using the Westerlund cointegration test and the Johansen–Fisher panel cointegration test. The findings of both tests confirm the presence of cointegration association between dependent and independent variables. The outcomes of CS-ARDL revealed that negative shocks in creating green technologies contribute to carbon dioxide emissions during recessions. Second, the findings supported the notion that innovation in green technology may reduce carbon dioxide emissions during times of economic expansion. Thirdly, the GDP increases the CO₂ emissions, but the usage of renewable energy decreases CO₂ emissions. In addition, the robustness analysis validated the consistency and precision of the existing findings. In summary, the findings suggest that the link between advances in environmentally friendly technologies and levels of carbon dioxide emissions were inversely proportional.

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.

Turkey is increasingly concerned about the effects of climate change, weather unpredictability, and severe events on agricultural production, food loss, and livelihoods. Turkey has long struggled against climate variability and catastrophic climatic events to prevent further declines in agricultural output. This study assessed the risk of climate change in Turkey from the perspective of loss in food grains and food security domain considering exposure to extreme climate events using the data from 1991 to 2019. This paper makes a theoretical contribution to the literature by identifying the relationship between food waste and food import, food prices and economic growth. It also makes an empirical contribution by administering and econometrically analyzing the impact of the loss of food grains on the aforementioned independent variables. Policy implications for the current national agriculture policy were provided using the vector auto-regression (VAR) model and derivative analysis. Food grain loss negatively correlates with food security since it increases reliance on food imports from outside. Moreover, the losses in food supplies contributes greatly to price increases. The GDP growth rate, however, was shown to be a feeble instigator. Climate change threatens food security, and the country’s progress toward sustainable development objectives is hampered in general, particularly concerning no poverty and zero hunger goals. In conclusion, climate change and its associated factors harm Turkey’s food security and economy.