Exploring the synergistic effect of carboxymethyl cellulose and chitosan in enhancing thermal stability of polyurethanes through statistical mixture design approach.

The thermal stability of polyurethanes, known for its limitations, was addressed in this research by seeking improvement through the introduction of carbohydrate-based chain extenders. In this research paper, we systematically sought to improve the thermal resistance of polyurethanes by incorporating carboxymethyl cellulose and chitosan, representing a pioneering application of the mixture design approach in their preparation. In this synthesis, hydroxyl-terminated polybutadiene and isophorone diisocyanate (IPDI) were reacted to prepare -NCO terminated prepolymer, which was subsequently reacted with varying mole ratios of CMC and CSN to develop a series of five PU samples. The prepared PU samples were characterized using the Fourier-transformed infrared spectroscopic technique. Thermal pyrolysis of PU samples was examined using thermal gravimetric analysis (TGA). It was observed that, among all the samples, PUS-3 showed remarkable thermal stability over a wide temperature range. A comprehensive statistical analysis was conducted to substantiate the experimental findings. It was estimated that CMC and CSN significantly enhance the thermal stability of the samples when involved in an interaction fashion. The ANOVA Table for the mixture design demonstrates that over 90 % of the total variation in thermal stability is explained by the mixture model across a wide temperature range. Moreover, PSU-3 exhibited 4 % more thermal stability over a wide range of temperatures on average, as compared to contemporary samples.

Time series analysis and short-term forecasting of monkeypox outbreak trends in the 10 major affected countries.

BACKGROUND: Considering the rapidly spreading monkeypox outbreak, WHO has declared a global health emergency. Still in the category of being endemic, the monkeypox disease shares numerous clinical characters with smallpox. This study focuses on determining the most effective combination of autoregressive integrated moving average model to encapsulate time dependent flow behaviour of the virus with short run prediction. METHODS: This study includes the data of confirmed reported cases and cumulative cases from eight most burdened countries across the globe, over the span of May 18, 2022, to December 31, 2022. The data was assembled from the website of Our World in Data and it involves countries such as United States, Brazil, Spain, France, Colombia, Mexico, Peru, United Kingdom, Germany and Canada. The job of modelling and short-term forecasting is facilitated by the employment of autoregressive integrated moving average. The legitimacy of the estimated models is argued by offering numerous model performance indices such as, root mean square error, mean absolute error and mean absolute prediction error. RESULTS: The best fit models were deduced for each country by using the data of confirmed reported cases of monkeypox infections. Based on diverse set of performance evaluation criteria, the best fit models were then employed to provide forecasting of next twenty days. Our results indicate that the USA is expected to be the hardest-hit country, with an average of 58 cases per day with 95% confidence interval of (00-400). The second most burdened country remained Brazil with expected average cases of 23 (00-130). The outlook is not much better for Spain and France, with average forecasts of 52 (00-241) and 24 (00-121), respectively. CONCLUSION: This research provides profile of ten most severely hit countries by monkeypox transmission around the world and thus assists in epidemiological management. The prediction trends indicate that the confirmed cases in the USA may exceed than other contemporaries. Based on the findings of this study, it remains plausible to recommend that more robust health surveillance strategy is required to control the transmission flow of the virus especially in USA.