Introducing a new classification of soft rocks based on the main geological and engineering aspects

Bulletin of Engineering Geology and the Environment - Soft rock masses represent a significant percentage of rock material on the earth’s crust. The engineering properties of these rocks are...     

CDA: a novel multicore scheduling for cost-aware deadline-constrained scientific workflows on the IaaS cloud

The Journal of Supercomputing - The variety of pricing models offered by cloud service providers and the availability of a wide diversity of computing resources has increased the popularity of this...     

Highly efficient electrocatalysts fabricated via electrophoretic deposition for alcohol oxidation,oxygen reduction,hydrogen evolution,and oxygen evolution reactions

Electrophoretic deposition (EPD) is a versatile technique that has drawn attention due to its ease of use and performance in depositing high-quality layers at room temperature. This technique principle is based on the deposition of charged particles from a stable colloidal suspension on a conductive substrate using either a direct or alternating current. Using relatively simple and low-cost equipment, the EPD technique enables the deposition of layers with controlled microstructures at nanoscale. The EPD technique has been particularly successful in the fabrication of the electrocatalyst layers for low-temperature fuel cells, which are anchored on the top of the fuel cell electrodes. In comparison with other electrocatalyst layer deposition techniques such as drop-casting, the EPD technique offers clear advantages for the control of the thickness and packing density of the electrocatalyst layers. Owing to the dense packing density, electrocatalyst layers deposited by EPD could achieve enhanced conductivity and efficiency. The present review aims at comprehensively evaluating the recently published results on the electrocatalyst layers fabricated by EPD and applied in oxygen reduction reactions, alcohol electro-oxidation reactions, hydrogen evolution reactions, and oxygen evolution reactions.     

Improved impossible differential and biclique cryptanalysis of HIGHT

HIGHT is a lightweight block cipher introduced in CHES 2006 by Hong et al as a block cipher suitable for low‐resource applications. In this paper, we propose improved impossible differential and biclique attacks on HIGHT block cipher both exploiting the permutation‐based property of the cipher's key schedule algorithm as well as its low diffusion. For impossible differential attack, we found a new 17‐round impossible differential characteristic that enables us to propose a new 27‐round impossible differential attack. The total time complexity of the attack is 2^120.4 where an amount of 2^59.3 chosen plaintext‐ciphertext pairs and 2^107.4 memory are required. We also instantiate a new biclique cryptanalysis of HIGHT, which is based on the new idea of splitting each of the forward and backward keys into 2 parts where the computations associated to each one are performed independently. The time complexity and data complexity of this attack are 2^125.7 and 2⁴², respectively. To the best of our knowledge, this is the fastest biclique attack on full‐round HIGHT.     

Progressive Collapse Analysis of Cable-Stayed Bridges

Journal of Failure Analysis and Prevention - Several codes have proposed guidelines to prevent progressive collapse. Although most of these standards are in progress, few recommendations for...     

Formation-Containment Control Using Dynamic Event-Triggering Mechanism for Multi-Agent Systems

The paper proposes a novel approach for formation-containment control based on a dynamic event-triggering mechanism for multi-agent systems. The leader-leader and follower-follower communications are reduced by utilizing the distributed dynamic event-triggered framework. We consider two separate sets of design parameters: one set comprising control and dynamic event-triggering parameters for the leaders and a second set similar to the first one with different values for the followers. The proposed algorithm includes two novel stages of co-design optimization to simultaneously compute the two sets of parameters. The design optimizations are convex and use the weighted sum approach to enable a structured trade-off between the formation-containment convergence rate and associated communications. Simulations based on non-holonomic mobile robot multi-agent systems quantify the effectiveness of the proposed approach.      

Prediction of ultimate bearing capacity through various novel evolutionary and neural network models

Engineering with Computers - In the current study, various evolutionary artificial intelligence and machine learning models namely, optimized artificial neural network (ANN), genetic algorithm...     

Feedback and Surround Modulated Boundary Detection

Edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes. The human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations. The “classical approach” assumes that these cells are only responsive to the stimulus present within their receptive fields, however, recent studies demonstrate that surrounding regions and inter-areal feedback connections influence their responses significantly. In this work we propose a biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1). In our model we account for four kinds of receptive field surround, i.e. full, far, iso- and orthogonal-orientation, whose contributions are contrast-dependant. The output signal from V1 is pooled in its perpendicular direction by larger V2 neurons employing a contrast-variant centre-surround kernel. We further introduce a feedback connection from higher-level visual areas to the lower ones. The results of our model on three benchmark datasets show a big improvement compared to the current non-learning and biologically-inspired state-of-the-art algorithms while being competitive to the learning-based methods.