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The emerging growth of edge devices and the expansion of IoT systems produced the demand to enhance their security algorithms to run on diverse applications and appliances. Lightweight cipher algorithms are mainly designed for IoT-constrained devices. Thus, due to the new services and applications of IoT that require extensive networks and devices, the need for efficient lightweight cipher algorithm implementations is a critical issue. In this paper, an enhanced cipher LED-128 algorithm based on Galois Fields (GF) operations is presented. The mathematical model of the LED algorithm simply requires a slight execution time for encryption-decryption processing. The Enhanced LED algorithm is applied with 64 bits of block plaintext and 128 bits of the key size which turn out 64 bits block size of ciphertext. The proposed algorithm is presented to advance the randomness of cipher text by enhancing the key schedule based on GF operations. The developed key schedule is enhanced to generate a robust key for the proposed algorithm. The NIST statistical tests have been examined and experimented for the proposed enhanced algorithm by utilizing diverse keys with distinct cipher text, which passed the randomness for each test. The proposed algorithm demonstrated a high confusion and diffusion based on the correlation coefficient analysis. The executed time for the encryption and decryption processes of the proposed enhanced LED algorithm is concentrated at minimum compared to the original LED-128 cipher algorithm. Hence, the proposed enhanced algorithm can be implemented for diverse IoT systems and can reach the trade-off between cost and performance as an efficient lightweight algorithm. The results of the enhanced algorithm are benchmarked with the current LED 64/128 algorithms and presented significant improvements in the performance compared to original design of the LED cipher algorithm. [ABSTRACT FROM AUTHOR]

Investment decision is difficult to be determined as the investors need to earn highest possible return in generating profit and protecting capital. Mathematical model which can provide the ranking and proportion of the investment alternatives will benefit investors as selection guideline. This paper aims to investigate in ranking and allocating proportion of asset funds using fuzzy preference relation and weighting algorithm. The evaluation of asset fund is expressed in trapezoidal fuzzy number. The data used for this study is 30 asset funds that listed in the Bursa Malaysia. This study found that 14 asset funds are suggested based on ranking and proportion allocation obtained from this model. As a conclusion, this study can assist investors in making decision in terms of ranking and allocating asset funds. [ABSTRACT FROM AUTHOR]

The paper presents the problem of calculating the remaining oil reserves and determining the forecast production rate of wells. The mathematical model for the calculation of this problem. Created computational algorithm and software for mathematical model. Forecast production rate was determined and calculated residual reserves the example of the well-birth Umid place to use to create a program. Show amenities, calculated using established task program. [ABSTRACT FROM AUTHOR]