I-CYP binding sites) was determinedafter 24 hours of isoproterenol treatment and ex-pressed as the percentage of receptor number as-sessed in nonstimulated cells. Where necessary,MG132 (20 mM) or lactacystin (20 mM) mixed inserum-free media was added to cells 1 hour beforestimulation.16. P. van Kerkhof, R. Govers, C. M. Alves dos Santos, G. J.Strous,

https://cml.harvard.edu/assets/science294_1313.pdf

Logic Gates and Computation from Assembled Nanowire Building Blocks

A new evolutionary algorithm known as the shuffled frog leaping algorithm is presented in this paper, to solve the unit commitment (UC) problem. This integer-coded algorithm has been developed to minimize the total energy dispatch cost over the scheduling horizon while all of the constraints should be satisfied. In addition, minimum up/down-time constraints have been directly coded not using the penalty function method. The proposed algorithm has been applied to ten up to 100 generating units, considering one-day and seven-day scheduling periods. The most important merit of the proposed method is its high convergence speed. The simulation results of the proposed algorithm have been compared with the results of algorithms such as Lagrangian relaxation, genetic algorithm, particle swarm optimization, and bacterial foraging. The comparison results testify to the efficiency of the proposed method.

Unit Commitment Problem Solution Using Shuffled Frog Leaping Algorithm

A novel image encryption scheme based on conservative hyperchaotic system and closed-loop diffusion between blocks

The combinatorial nature of many important mathematical problems, including nondeterministic-polynomial-time (NP)-complete problems, places a severe limitation on the problem size that can be solved with conventional, sequentially operating electronic computers. There have been significant efforts in conceiving parallel-computation approaches in the past, for example: DNA computation, quantum computation, and microfluidics-based computation. However, these approaches have not proven, so far, to be scalable and practical from a fabrication and operational perspective. Here, we report the foundations of an alternative parallel-computation system in which a given combinatorial problem is encoded into a graphical, modular network that is embedded in a nanofabricated planar device. Exploring the network in a parallel fashion using a large number of independent, molecular-motor-propelled agents then solves the mathematical problem. This approach uses orders of magnitude less energy than conventional computers, thus addressing issues related to power consumption and heat dissipation. We provide a proof-of-concept demonstration of such a device by solving, in a parallel fashion, the small instance {2, 5, 9} of the subset sum problem, which is a benchmark NP-complete problem. Finally, we discuss the technical advances necessary to make our system scalable with presently available technology.

https://www.pnas.org/content/pnas/113/10/2591.full.pdf

Parallel computation with molecular-motor-propelled agents in nanofabricated networks

A discrete invasive weed optimization algorithm for solving traveling salesman problem

Encoding and processing information in DNA-, RNA- and other biomolecule-based devices is an important requirement for DNA based computing with potentially important applications. To make DNA computing more reliable, much work has focused on designing the good DNA sequences. However, this is a bothersome task as encoding problem is an NP problem. In this paper, a new methodology based on the IWO algorithm is developed to optimize encoding sequences. Firstly, the mathematics models of constrained objective optimization design for encoding problems based on the thermodynamic criteria are set up. Then, a modified IWO method is developed by defining the colonizing behavior of weeds to overcome the obstacles of the original IWO algorithm, which cannot be applied to discrete problems directly. The experimental results show that the proposed method is effective and convenient for the user to design and select effective DNA sequences in silicon for controllable DNA computing.

Application of a novel IWO to the design of encoding sequences for DNA computing

Based on the Feistel network and dynamic deoxyribonucleic acid (DNA) encoding technology, an image encryption method is proposed using the “permutation-diffusion-scrambling” structure. First, the SHA-3 algorithm is used to calculate the hash value of the plaintext image as the initial value of the hyperchaotic system, and the chaos-generated sequence is used to generate the Hill cipher matrix to replace the image pixel. Second, the DNA sequence operation is used as the F function of the Feistel network. The DNA sequence database is used as the key K of Feistel network, and the image pixel value diffusion is realized by the Feistel network. Finally, further diffusion is carried out through the ciphertext feedback and through the ciphertext confusion and diffusion of three rounds of “chaotic scrambling-DNA encoding-Feistel transformation-DNA decoding,” making the ciphertext more random and resistant to attacks and ensuring that the encrypted ciphertext is more secure. The experimental results show that the proposed method can effectively encrypt the image and has prominent characteristics, such as strong plaintext sensitivity, a large key space, and excellent ciphertext statistical properties.

/pdf/an-image-encryption-method-based-on-the-feistel-network-and-gdjg1m4qre.pdf

An Image Encryption Method Based on the Feistel Network and Dynamic DNA Encoding

Shuffled frog leaping (SFL) is a population based, cooperative search metaphor inspired by natural memetics. Its ability of adapting to dynamic environment makes SFL become one of the most important memetic algorithms. In order to improve the algorithmpsilas stability and the ability to search the global optimum, a novel dasiacognition componentpsila is introduced to enhance the effectiveness of the SFL, namely frog not only adjust its position according to the best individual within the memeplex or the global best of population but also according to thinking of the frog itself. To validate the improved SFL (ISFL) method, numerous simulations were conducted to compare SFL and ISFL using six benchmark problems for continuous and discrete optimization. According to the simulation results, adding the cognitive behavior to SFL significantly enhances the performance of SFL in solving the optimization problems, and the improvements are more evident with the scale of the problem increasing.

An improved shuffled frog leaping algorithm with cognitive behavior

Image encryption is the most direct and effective technical means for protecting the security of image information. Based on the space filling property of the Hilbert curve and the infinite property of the H-geometric fractal, a new image encryption technique is proposed, which combines the pseudo-randomness of a hyperchaotic system and the sensitivity to initial values. First, the hash value of a plaintext image is calculated using the secure hash algorithm 3 (SHA-3) as the initial value of the piece-wise linear chaotic map (PWLCM) and Rossler chaotic systems, which associates the key with the plaintext. In addition, the chaotic sequences that are generated by the chaotic systems are used to scramble the global pixel positions and the pixel values of the images, thereby disturbing the distribution of the pixel positions and the pixel values. Second, the Hilbert curve and H-fractal are alternately used to scramble the local pixel positions and diffuse the pixel values twice. Finally, the ciphertext feedback is used to further enhance the confusion and diffusion characteristics of the algorithm in order to achieve higher security. The experimental results and security analysis show that the encryption technique has enough key space to resist exhaustive attacks and can effectively resist statistical attacks, differential attacks, noise attacks, and cropping attacks. It can be used for military, judicial, and other privacy-related digital images secure storage and network security transmissions.

A Chaos-Based Image Encryption Technique Utilizing Hilbert Curves and H-Fractals

This paper puts forward an image encryption approach using chaotic maps and genetic operations First, the Keccak algorithm is employed to compute the hash values of a plain-image as the initial values for chaotic map The sensitivity and pseudo randomness of chaotic map used for the initial conditions allows for pseudorandom sequences to be obtained by iterative logistic map to shuffle and permute the pixel positions and values of the image Second, in combination with the Henon map and the DNA coding technique, genetic operations at the bit level are used to achieve pixel selection, crossover and mutation, as well as further completion of pixel diffusion and scrambling, which significantly increases the difficulty of deciphering the algorithm Finally, the diffusion and confusion features of the algorithm are further strengthened by bidirectional exclusive OR operations with chaotic sequences The theoretical analysis and simulation results indicate that the algorithm is sensitive to keys and can effectively defend statistical and differential attacks, indicating that it has good security and application potential

Ying Niu

Papers

Application of a novel IWO to the design of encoding sequences for DNA computing

An Image Encryption Method Based on the Feistel Network and Dynamic DNA Encoding

An improved shuffled frog leaping algorithm with cognitive behavior

A Chaos-Based Image Encryption Technique Utilizing Hilbert Curves and H-Fractals

An image encryption approach based on chaotic maps and genetic operations