LBAA: A novel load balancing mechanism in cloud environments using ant colony optimization and artificial bee colony algorithms

Recently, cloud computing has been recognized as an effective paradigm for offering an on-demand platform, software services, and an efficient infrastructure to cloud clients. Due to the exponential growth of cloud tasks and the rapidly increasing number of cloud users, scheduling and balancing these tasks among involved heterogeneous virtual machines becomes an Non-deterministic Polynomial hard (NP-hard) optimization problem considering significant constraints, such as high rate of resource usage, low scheduling time, and low implementation cost. Therefore, various meta-heuristic algorithms have been widely used to tackle the issue. The current paper proposes a novel load balancing mechanism using the ant colony optimization and artificial bee colony algorithms, called LBAA, which aims to balance the load division among systems in data centers. The simulation outcomes confirm that our algorithm outperforms previous works regarding response time, imbalance degree, makespan, and resource utilization up to 25%, 15%, 12%, and 10%, respectively.     

Test-Time Optimization for Video Depth Estimation Using Pseudo Reference Depth

In this paper, we propose a learning-based test-time optimization approach for reconstructing geometrically consistent depth maps from a monocular video. Specifically, we optimize an existing single image depth estimation network on the test example at hand. We do so by introducing pseudo reference depth maps which are computed based on the observation that the optical flow displacement for an image pair should be consistent with the displacement obtained by depth-reprojection. Additionally, we discard inaccurate pseudo reference depth maps using a simple median strategy and propose a way to compute a confidence map for the reference depth. We use our pseudo reference depth and the confidence map to formulate a loss function for performing the test-time optimization in an efficient and effective manner. We compare our approach against the state-of-the-art methods on various scenes both visually and numerically. Our approach is on average 2.5× faster than the state of the art and produces depth maps with higher quality.     

Integrated process design and control of divided-wall distillation columns using molecular tracking

This work explores the dynamic and control behavior of dividing wall distillation columns from two different steady-state design approaches (molecular tracking and optimization method) for three different mixtures. The controllability of the six design cases was evaluated using singular value decomposition and the closed-loop performance was evaluated using integral absolute error in Aspen Dynamics. The results demonstrate that the side-draw location obtained by molecular tracking (MT) provides optimal controllability. As a result, there is a slight advantage in control properties while obtaining designs by reducing the time to find the optimal solution through the MT method.     

Defect Engineering of Graphene for Dynamic Reliability

The interface between two-dimensional (2D) materials and soft, stretchable polymeric substrates is a governing criterion in proposed 2D materials-based flexible devices. This interface is dominated by weak van der Waals forces and there is a large mismatch in elastic constants between the contact materials. Under dynamic loading, slippage, and decoupling of the 2D material is observed, which then leads to extensive damage propagation in the 2D lattice. Herein, graphene is functionalized through mild and controlled defect engineering for a fivefold increase in adhesion at the graphene-polymer interface. Adhesion is characterized experimentally using buckling-based metrology, while molecular dynamics simulations reveal the role of individual defects in the context of adhesion. Under in situ cyclic loading, the increased adhesion inhibits damage initiation and interfacial fatigue propagation within graphene. This work offers insight into achieving dynamically reliable and robust 2D material-polymer contacts, which can facilitate the development of 2D materials-based flexible devices.