Applying version management of object-oriented database technology in reactive scheduling

The focal points of reactive scheduling are to resolve the impact of emergent and unexpected situations under a time constraint. This study proposes a reactive scheduling architecture based on version management technology of objectoriented database technology. In this architecture, three independent workspaces are used to generate schedule plans while five function managers interact with one another to support version management functions. This study will demonstrate the different types of strategies for reactive scheduling by considering two types of unexpected events: machine breakdown and lack of materials.     

Static analysis of multi-core TDMA resource arbitration delays

In the development of hard real-time systems, knowledge of the Worst-Case Execution Time (WCET) is needed to guarantee the safety of a system. For single-core systems, static analyses have been developed which are able to derive guaranteed bounds on a program’s WCET. Unfortunately, these analyses cannot directly be applied to multi-core scenarios, where the different cores may interfere with each other during the access to shared resources like for example shared buses or memories. For the arbitration of such resources, TDMA arbitration has been shown to exhibit favorable timing predictability properties. In this article, we review and extend a methodology for analyzing access delays for TDMA-arbitrated resources. Formal proofs of the correctness of these methods are given and a thorough experimental evaluation is carried out, where the presented techniques are compared to preexisting ones on an extensive set of real-world benchmarks for different classes of analyzed systems.     

Superhydrophobicity vs. Ice Adhesion: The Quandary of Robust Icephobic Surface Design

The mesoscale, multitier texture of the lotus leaf has served as an inspiration to fabricate surface designs with controllable superhydrophobic properties, targeting a broad range of applications. The choice of material for such designs is directly related to surface performance, in particular under adverse and realistic conditions. Due to its importance in many applications, here aluminium is employed as a material platform and identify key porous hierarchical textures, yielding extraordinary impalement‐resistant behavior: Droplet repellency is demonstrated consistently for water impact velocities up to 12 m s⁻¹ (extreme Weber number, We ≈ 3500). Despite impressive superhydrophobic behavior, if ice forms on such surfaces, ice adhesion is markedly stronger than on less hydrophobic alumina nanotube array structures. In a departure from the findings of the well‐accepted shear stress‐based ice adhesion criterion, a deviation between decreasing ice adhesion strength and increasing hydrophobicity is observed. This is explained with ice adhesion mechanism, depending strongly on the applied stress field orientation and the respective effective ice–substrate contact area. Our results indicate that ice adhesion criteria for the performance of icephobic surfaces should account for the simultaneous presence of shear and tensile stresses, instead of shear stresses alone.     

Continuous auditing with a multi-agent system

Information technology has dramatically changed the way businesses and business information are managed. In electronic business, much of the information is in electronic format, and the resulting change in the auditing environment has forced audit professionals to audit electronic evidence. Moreover, the emergence of real time accounting reports has put increasing pressure on audit professionals to provide real-time auditing services, or continuous auditing, in which the time between the occurrence of events and the provision of an auditor's opinion is minimized to an acceptable level. This paper proposes an agent-based system for continuous auditing called the agent-based continuous audit model (ABCAM). The system can be implemented independently of the client's information system, is able to undertake automatic auditing in real time, and can easily adapt to changes in auditing requirements and information systems. Five scenarios are developed to illustrate the model.     

Forecasting Damage Mechanics By Deep Learning

We in this paper exploit time series algorithm based deep learning in forecasting damage mechanics problems. The methodologies that are able to work accurately for less computational and resolving attempts are a significant demand nowadays. Relied on learning an amount of information from given data, the long short-term memory (LSTM) method and multi-layer neural networks (MNN) method are applied to predict solutions. Numerical examples are implemented for predicting fracture growth rates of L-shape concrete specimen under load ratio, single-edge-notched beam forced by 4-point shear and hydraulic fracturing in permeable porous media problems such as storage-toughness fracture regime and fracture-height growth in Marcellus shale. The predicted results by deep learning algorithms are well-agreed with experimental data.     

Shear stress distribution prediction in symmetric compound channels using data mining and machine learning models

Shear stress distribution prediction in open channels is of utmost importance in hydraulic structural engineering as it directly affects the design of stable channels. In this study, at first, a series of experimental tests were conducted to assess the shear stress distribution in prismatic compound channels. The shear stress values around the whole wetted perimeter were measured in the compound channel with different floodplain widths also in different flow depths in subcritical and supercritical conditions. A set of, data mining and machine learning algorithms including Random Forest (RF), M5P, Random Committee, KStar and Additive Regression implemented on attained data to predict the shear stress distribution in the compound channel. Results indicated among these five models; RF method indicated the most precise results with the highest R² value of 0.9. Finally, the most powerful data mining method which studied in this research compared with two well-known analytical models of Shiono and Knight method (SKM) and Shannon method to acquire the proposed model functioning in predicting the shear stress distribution. The results showed that the RF model has the best prediction performance compared to SKM and Shannon models.     

Extended meshfree methods without branch enrichment for cohesive cracks

An extended meshless method for both static and dynamic cohesive cracks is proposed. This new method does not need any crack tip enrichment to guarantee that the crack closes at the tip. All cracked domains of influence are enriched by only the sign function. The domain of influence which includes a crack tip is modified so that the crack tip is always positioned at its edge. The modification is only applied for the discontinuous displacement field and the continuous field is kept unchanged. In addition to the new method, the use of Lagrange multiplier is explored to achieve the same goal. The crack is extended beyond the actual crack tip so that the domains of influence containing the crack tip are completely cut. It is enforced that the crack opening displacement vanishes along the extension of the crack. These methods are successfully applied to several well-known static and dynamic problems.     

A Meshfree Method based on the Local Partition of Unity for Cohesive Cracks

We will present a meshfree method based on the local partition of unity for cohesive cracks. The cracks are described by a jump in the displacement field for particles whose domain of influence is cut by the crack. Particles with partially cut domain of influence are enriched with branch functions. Crack propagation is governed by the material stability condition. Due to the smoothness and higher order continuity, the method is very accurate which is demonstrated for several quasi static and dynamic crack propagation examples.