Palindromic Vectors, Symmetropy and Symmentropy as Symmetry Descriptors of Binary Data

Today, the palindromic analysis of biological sequences, based exclusively on the study of “mirror” symmetry properties, is almost unavoidable. However, other types of symmetry, such as those present in friezes, could allow us to analyze binary sequences from another point of view. New tools, such as symmetropy and symmentropy, based on new types of palindromes allow us to discriminate binarized 1/f noise sequences better than Lempel–Ziv complexity. These new palindromes with new types of symmetry also allow for better discrimination of binarized DNA sequences. A relative error of 6% of symmetropy is obtained from the HUMHBB and YEAST1 DNA sequences. A factor of 4 between the slopes obtained from the linear fits of the local symmentropies for the two DNA sequences shows the discriminative capacity of the local symmentropy. Moreover, it is highlighted that a certain number of these new palindromes of sizes greater than 30 bits are more discriminating than those of smaller sizes assimilated to those from an independent and identically distributed random variable.

On the 1-error linear complexity of two-prime generator

<abstract><p>Jing et al. dealed with all possible Whiteman generalized cyclotomic binary sequences $ s(a, b, c) $ with period $ N = pq $, where $ (a, b, c) \in \{0, 1\}^3 $ and $ p, q $ are distinct odd primes (Jing et al. arXiv:2105.10947v1, 2021). They have determined the autocorrelation distribution and the 2-adic complexity of these sequences in a unified way by using group ring language and a version of quadratic Gauss sums. In this paper, we determine the linear complexity and the 1-error linear complexity of $ s(a, b, c) $ in details by using the discrete Fourier transform (DFT). The results indicate that the linear complexity of $ s(a, b, c) $ is large enough and stable in most cases.</p></abstract>

Cardiorespiratory kinetics in exercise physiology: estimates and predictions using randomized changes in work rate

Purpose Kinetics of cardiorespiratory parameters (CRP) in response to work rate (WR) changes are evaluated by pseudo-random binary sequences (PRBS testing). In this study, two algorithms were applied to convert responses from PRBS testing into appropriate impulse responses to predict steady states values and responses to incremental increases in exercise intensity. Methods 13 individuals (age: 41 ± 9 years, BMI: 23.8 ± 3.7 kg m−2), completing an exercise test protocol, comprising a section of randomized changes of 30 W and 80 W (PRBS), two phases of constant WR at 30 W and 80 W and incremental WR until subjective fatigue, were included in the analysis. Ventilation ($$\dot{V}_{{\text{E}}}$$ V ˙ E ), O2 uptake ($$\dot{V}{\text{O}}_{2}$$ V ˙ O 2 ), CO2 output ($$\dot{V}{\text{CO}}_{2}$$ V ˙ CO 2 ) and heart rate (HR) were monitored. Impulse responses were calculated in the time domain and in the frequency domain from the cross-correlations of WR and the respective CRP. Results The algorithm in the time domain allows better prediction for $$\dot{V}{\text{O}}_{2}$$ V ˙ O 2 and $$\dot{V}{\text{CO}}_{2}$$ V ˙ CO 2 , whereas for $$\dot{V}_{{\text{E}}}$$ V ˙ E and HR the results were similar for both algorithms. Best predictions were found for $$\dot{V}{\text{O}}_{2}$$ V ˙ O 2 and HR with higher (3–4%) 30 W steady states and lower (1–4%) values for 80 W. Tendencies were found in the residuals between predicted and measured data. Conclusion The CRP kinetics, resulting from PRBS testing, are qualified to assess steady states within the applied WR range. Below the ventilatory threshold, $$\dot{V}{\text{O}}_{2}$$ V ˙ O 2 and HR responses to incrementally increasing exercise intensities can be sufficiently predicted.

Designing an Efficient and Highly Dynamic Substitution-Box Generator for Block Ciphers Based on Finite Elliptic Curves

Developing a substitution-box (S-box) generator that can efficiently generate a highly dynamic S-box with good cryptographic properties is a hot topic in the field of cryptography. Recently, elliptic curve (EC)-based S-box generators have shown promising results. However, these generators use large ECs to generate highly dynamic S-boxes and thus may not be suitable for lightweight cryptography, where the computational power is limited. The aim of this paper is to develop and implement such an S-box generator that can be used in lightweight cryptography and perform better in terms of computation time and security resistance than recently designed S-box generators. To achieve this goal, we use ordered ECs of small size and binary sequences to generate certain sequences of integers which are then used to generate S-boxes. We performed several standard analyses to test the efficiency of the proposed generator. On an average, the proposed generator can generate an S-box in 0.003 seconds, and from 20,000 S-boxes generated by the proposed generator, 93 % S-boxes have at least the nonlinearity 96. The linear approximation probability of 1000 S-boxes that have the best nonlinearity is in the range [0.117, 0.172] and more than 99% S-boxes have algebraic complexity at least 251. All these S-boxes have the differential approximation probability value in the interval [0.039, 0.063]. Computational results and comparisons suggest that our newly developed generator takes less running time and has high security against modern attacks as compared to several existing well-known generators, and hence, our generator is suitable for lightweight cryptography. Furthermore, the usage of binary sequences in our generator allows generating plaintext-dependent S-boxes which is crucial to resist chosen-plaintext attacks.

A New IOT Image Encryption Algorithm based on Chaotic Map

IOT information is always subjected to attacks, because component of the IOT system always unsupervised for most of time, also due to simplicity of wireless communication media, so there is high chance for attack, lastly, IOT is constraint device in terms of energy and computation complexity. So, different research and study are proposed to provide cryptographic algorithm. In this paper, a new image encryption is proposed based on anew chaotic map used to generate the binary key. The proposed map is three dimensional map, which is more sensitive to initial condition, each dimension of the 3-D chaotic map is depended on the others dimension, which may increase the randomness of the behavior trajectory for the next values and this gives the algorithm the ability to resist any attacks. At first, 3-D chaotic map is proposed, which is very sensitive for initial condition, the three dimension is depended on each other, which make the system more randomness, then the produced sequences is converted on binary key by using mod operation. The original image is scrambled based on mod operation to exchange the row and interleaving them, the same operations are repeated for column of the image. Later, the image is divided into blocks of size (8*8) and scrambled by using negative diagonal scan, the final pixels are converted into binary sequences, which are XORed with the generated key to produce the encrypted image. The experiment is performed on different images with different properties and tested with different metrics such as entropy, correlation, key sensitivity, number of pixel change rate (NPCR) and histogram of the original and encrypted images. T results shows that the proposed encryption algorithm is more efficient and outperform other methods.

Assessing the Frequency of Floods in Ice-Covered Rivers under a Changing Climate: Review of Methodology

Ice-influenced hydrologic and hydrodynamic processes often cause floods in cold regions of the globe. These floods are typically associated with ice jams and can have negative socio-economic impacts, while their impacts on riverine ecosystems can be both detrimental and beneficial. Several methods have been proposed for constructing frequency distributions of ice-influenced annual peak stages where historical data are scarce, or for estimating future frequencies under different climate change scenarios. Such methods rely on historical discharge data, which are generally easier to obtain than peak stages. Future discharges can be simulated via hydrological models, driven by climate-model output. Binary sequences of historical flood/no-flood occurrences have been studied using logistic regression on physics-based explanatory variables or exclusively weather-controlled proxies, bypassing the hydrological modelling step in climate change projections. Herein, background material on relevant river ice processes is presented first, followed by descriptions of various proposed methods to quantify flood risk and assess their advantages and disadvantages. Discharge-based methods are more rigorous; however, projections of future flood risk can benefit from improved hydrological simulations of winter and spring discharges. The more convenient proxy-based regressions may not adequately reflect the controlling physics-based variables, while extrapolation of regression results to altered climatic conditions entails further uncertainty.

Selection of Moment Vectors in Protein Sequence Comparison Under Binary Representation

Similarity/dissimilarity study of protein and genome sequences remains a challenging task and selection of techniques and descriptors to be adopted, plays an important role in computational biology. Again, genome sequence comparison is always preferred to protein sequence comparison due the presence of 20 amino acids in protein sequence compared to only 4 nucleotides in genome sequence. So it is important to consider suitable representation that is both time and space efficient and also equally applicable to protein sequences of equal and unequal lengths. In the binary form of representation, Fourier transform of a protein sequence reduces to the transformation of 20 simple binary sequences in Fourier domain, where in each such sequence, Perseval’s Identity gives a very simple computable form of power spectrum. This gives rise to readily acceptable forms of moments of different degrees. Again such moments, when properly normalized, show a monotonically descending trend with the increase in the degrees of the moments. So it is better to stick to moments of smaller degrees only. In this paper, descriptors are taken as 20 component vectors, where each component corresponds to a general second order moment of one of the 20 simple binary sequences. Then distance matrices are obtained by using Euclidean distance as the distance measure between each pair of sequence. Phylogenetic trees are obtained from the distance matrices using UPGMA algorithm. In the present paper, the datasets used for similarity/dissimilarity study are 9 ND4, 16 ND5, 9 ND6, 24 TF proteins and 12 Baculovirus proteins. It is found that the phylogenetic trees produced by the present method are at par with those produced by the earlier methods adopted by other authors and also their known biological references. Further it takes less computational time and also it is equally applicable to sequences of equal and unequal lengths.