An Investigation Into the Mathematics of Decryption Techniques in RSA Encryption, With an Implementation in Python

This study explores the mathematics of two different techniques that can be used to access the decryption key in RSA encryption including semi-prime factorization and a logarithmic method. The study then presents a Python program, written by the author, that automates the calculations for either of the decryption techniques and also calculates the number of iterations required to determine the decryption key in either circumstance. Most importantly, the program utilizes only values of the RSA encryption algorithm that would be made publicly available in actual circumstances to calculate the decryption key so as to mimic real-life occurrences with as much integrity and accuracy as possible.

295 Radiotherapy for Curative-intent Metastatic Rectal Cancer: A Systematic Review and Meta-Analysis

Introduction The role and optimal regimen of radiotherapy in curative-intent treatment of metastatic rectal cancer is unclear and hence a single arm meta-analysis was performed. Method Medline, Embase and Cochrane Library databases were searched up to 16 May 2020. A Meta-analysis of binomial data was performed using a Freeman-Tukey double arcsine transformation, and pooled estimates were used to construct risk ratios and confidence intervals via the Katz-logarithmic method. Additionally, comparative meta-analysis was performed with the Mantel Haenszel model. Results 18 studies were included. Rectal pathological complete response (pCR) was observed in 14% of tumours treated with radiotherapy (n = 57/388, CI 0.07 to 0.23). Comparative meta-analysis of cohort studies showed that treatment regimens including radiotherapy were associated with higher pT1 tumour and better oncological outcomes compared to regimens without radiotherapy. Katz-logarithmic method showed that neoadjuvant radiotherapy had a higher proportion of pN0 staging (RR = 1.81, 95% CI 1.06 to 3.09, p = 0.029) and better oncological outcomes compared to adjuvant radiotherapy, and that short course radiotherapy (SCRT) had a lower proportion of pT3 tumours (RR = 0.778, 95% CI 0.609 to 0.994, p = 0.044) and similar oncological outcomes compared to long course radiotherapy (LCRT). Conclusions This study supports the evidence that radiotherapy should be used in curative intent metastatic rectal cancer.

Effect of radiative heat loss on thermal diffusivity evaluated using normalized logarithmic method in laser flash technique

The laser flash technique is a standard method to measure the thermal diffusivity of solid samples especially at high temperatures. To understand the reliability of thermal diffusivity evaluation at high temperature for solid samples with low-thermal-diffusivity values, we analyzed the effect of radiative heat loss using the logarithmic method. The results revealed that when the Biot number was 0.1, the deviation from the input thermal diffusivity value was approximately −1.6%. In addition, when an aluminum silicate (AS) sample was heated to 1,273 K, the maximum deviation was approximately −0.35%. In contrast, the difference between the input value and the thermal diffusivity evaluated by the halftime method when AS was heated to 1,273 K was approximately 2.38%. Thus, since the effect of radiative heat loss was found to be negligible, it is concluded that the normalized logarithmic method should be very useful for the thermal diffusivity analysis of low-thermal-diffusivity solid samples at high temperature.

A Novel Method for Measuring the Color of Edible Oil on the Lovibond Scale Based on Spectral Detection and Convolutional Neural Network

A fast, high-precision method for measuring the color of edible oil on the Lovibond scale was developed and validated. It involves two components: spectral detection and data processing. The former was used for measuring the edible oil’s transmission spectrum in the visible range. The latter included a logarithmic method and a convolutional neural network (CNN) for calculating the color value from the transmission spectrum. The logarithmic method converted the multiplicative combination of spectra to an additive combination, which greatly improved the performance. A CNN with three convolutional layers was developed using Tensorflow. Validation tests of the method using ten different oil types showed a maximum error of 0.5, average error of 0.32, and error variance of 0.04. Thus, the proposed method can meet the needs of the edible oil industry, and it has great application potential. Keywords: CNN, Color of edible oil, Convolutional neural network, Lovibond scale, Spectral detection.