Scientific Tamil Lexicon: The Revelation of Cryptographic Connection Between Tamil Language and Galois Field

This paper presents a computational framework that helps enhance the confidentiality protection of communication in cybersecurity by leveraging the scientific properties of the Tamil language and the advanced encryption standard (AES). It defines a product set of vowels and consonants sounds of the Tamil language and reveals its connection to Hardy-Ramanujan prime factors and Tamil letters as a one-to-one function. It also reveals that the letters of the Tamil alphabet, combined with the digits from 1 to 9, form a Galois field of 2^8 over an irreducible polynomial of degree 8. In addition, it implements these two mathematical properties and builds an encoder for the AES algorithm to transform the Tamil texts to their hexadecimal states, and replace the pre-round transformation module of AES. It empirically shows that the Tamil-based encoder enhances the cryptographic strength of the AES algorithm at every step of its encryption flow. The cryptographic strength is measured by the runs test scores of the bit sequences of the ciphers of AES and compared with that of the English language. This modeling and simulation approach concludes that the Tamil-based encryption enhances the cryptographic strength of AES than English-based encryption.

Understanding bias when estimating life expectancy from age at death: a simulation approach applied to Morquio syndrome A

Objective Life expectancy can be estimated accurately from a cohort of individuals born in the same year and followed from birth to death. However, due to the resource-consuming nature of following a cohort prospectively, life expectancy is often assessed based upon retrospective death record reviews. This conventional approach may lead to potentially biased estimates, in particular when estimating life expectancy of rare diseases such as Morquio syndrome A. We investigated the accuracy of life expectancy estimation using death records by simulating the survival of individuals with Morquio syndrome A under four different scenarios. Results When life expectancy was constant during the entire period, using death data did not result in a biased estimate. However, when life expectancy increased over time, as is often expected to be the case in rare diseases, using only death data led to a substantial underestimation of life expectancy. We emphasize that it is therefore crucial to understand how estimates of life expectancy are obtained, to interpret them in an appropriate context, and to assess estimation methods within a sensitivity analysis framework, similar to the simulations performed herein.

A Systematic Simulation-Based Multi-Criteria Decision-Making Approach for the Evaluation of Semi–Fully Flexible Machine System Process Parameters

Current manufacturing system health management is of prime importance due to the emergence of recent cost-effective and -efficient prognostics and diagnostics capabilities. This paper investigates the most used performance measures viz. Throughput Rate, Throughput Time, System Use, Availability, Average Stay Time, and Maximum Stay Time as alternatives that are responsible for the diagnostics of manufacturing systems during real-time disruptions. We have considered four different configurations as criteria on which to test with the proposed integrated MCDM (Multi-Criteria Decision-Making)-TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)-based simulation approach. The main objective of this proposed model is to improve the performance of semi–fully flexible systems and to maximize the production rate by ranking the parameters from most influenced to least. In this study, first, the performance of the considered process parameters are analyzed using a simulation approach, and furthermore the obtained results are validated using real-time experimental results. Thereafter, using an Entropy method, the weights of each parameter are identified and then the MCDM-based TOPSIS is applied to rank the parameters. The results show that Throughput tTme is the most affected parameter and that Availability, average stay time, and max stay time are least affected in the case of no breakdown of machine condition. Similarly, Throughput Time is the most affected parameter and Maximum Stay Time is the least affected parameter in the case of the breakdown of machine condition. Finally, the rankings from the TOPSIS method are compared with the PROMETHEE method rankings. The results demonstrate the ability to understand system behavior in both normal and uncertain conditions.

Design of Oral Sustained-Release Pellets by Modeling and Simulation Approach to Improve Compliance for Repurposing Sobrerol

Sobrerol, an oral mucolytic agent, in a recent study showed promise for treating multiple sclerosis. A human equivalent dose of 486 mg of sobrerol administered thrice daily (i.e., 1459 mg of daily dose) demonstrated the highest therapeutic efficacy for repurposing use, which also points out the poor compliance of administration. In this study, oral sustained-release pellets of sobrerol were successfully developed with evaluated manufacturing conditions and drug release kinetics. For design of the target drug product, we used a modeling and simulation approach to establish a predictive model of oral pharmacokinetic profile, by exploring the characteristics and correlations corresponding to the pharmacokinetics and pharmacodynamics of sobrerol, such as absorption lag time (0.18 h), time-scaling in vitro–in vivo correlation (tin-vitro = 0.494 tin-vivo − 0.0904), gastrointestinal transit time (8 h), minimum effective concentration (1.61 μg/mL), and duration of action (12.8 h). Results showed that the frequency of administration and the daily dose remarkably reduced by 33.3% (i.e., from thrice to twice daily) and 22.8%, respectively, which indicates that this prototype approach can be adopted for rapidly developing a modified-release dosage form of sobrerol, with improvement of compliance of administration and therapeutic efficacy.

Methodology to assess wind turbine blade throw risk to vehicles on nearby roads

The public road setback distance is often an important factor that drives wind farm design. This paper outlines a methodology for assessing the risk imposed by blade throw at various road setbacks using a physics-based simulation approach. Given a road setback distance, Monte Carlo simulation is performed wherein blade throw parameters and vehicle locations are randomized. Potential collisions are determined using an “impact circle” approach which assumes that impact occurs if the vehicle is inside the impact radius of the blade fragment when it lands. This approach is exercised on several example turbines and risk levels are calculated for various road setbacks. The method is also applied to a notional wind farm with turbines located at a typical road setback distance. Results show that the blade throw risk imposed to vehicles on public roads for the example wind farm is extremely small and commensurate with risks imposed by everyday activities.

Price Index Modeling and Risk Prediction of Sharia Stocks in Indonesia

This study aimed to predict the JKII (Jakarta Islamic Index) price as a price index of sharia stocks and predict the loss risk. This study uses geometric Brownian motion (GBM) and Value at Risk (VaR; with the Monte Carlo Simulation approach) on the daily closing price of JKII from 1 August 2020–13 August 2021 to predict the price and loss risk of JKII at 16 August 2021–23 August 2021. The findings of this study were very accurate for predicting the JKII price with a MAPE value of 2.03%. Then, using VaR with a Monte Carlo Simulation approach, the loss risk prediction for 16 August 2021 (one-day trading period after 13 August 2021) at the 90%, 95%, and 99% confidence levels was 2.40%, 3.07%, and 4.27%, respectively. Most Indonesian Muslims have financial assets in the form of Islamic investments as they offer higher returns within a relatively short time. The movement of all Islamic stock prices traded on the Indonesian stock market can be seen through the Islamic stock price index, namely the JKII (Jakarta Islamic Index). Therefore, the focus of this study was predicting the price and loss risk of JKII as an index of Islamic stock prices in Indonesia. This study extends the previous literature to determine the prediction of JKII price and the loss risk through GBM and VaR using a Monte Carlo simulation approach.