Generalized relation between the relative entropy and dissipation for nonequilibrium systems

Recently, Kawai, Parrondo, and Van den Broeck have related dissipation to time-reversal asymmetry. We generalized the result by considering a protocol where the physical system is driven away from an initial thermal equilibrium state with temperature $\beta_0$ to a final thermal equilibrium state at a different temperature. We illustrate the result using a model with an exact solution, i.e., a particle in a moving one-dimensional harmonic well.Comment: 4 page

GASA-JOSH: a Hybrid Evolutionary-Annealing Approach for Job-Shop Scheduling Problem

The job-shop scheduling problem is well known for its complexity as an NP-hard problem. We have considered JSSPs with an objective of minimizing makespan. In this paper, we develope a hybrid approach for solving JSSPs called GASA-JOSH. In GASA-JOSH, the population is divided in non-cooperative groups. Each group must refer to a method pool and choose genetic algorithm or simulated annealing to solve the problem. The best result of each group is maintained in a solution set, and then the best solution to the whole population is chosen among the elements of the solution set and reported as outcome. The proposed approach have been compared with other algorithms for job-shop scheduling and evaluated with satisfactory results on a large set of JSSPs derived from classical job-shop scheduling benchmarks. We have solved 23 benchmark problems and compared results obtained with a number of algorithms established in the literature

An Effective Multi-Population Based Hybrid Genetic Algorithm for Job Shop Scheduling Problem

The job shop scheduling problem is a well known practical planning problem in the manufacturing sector. We have considered the JSSP with an objective of minimizing makespan. In this paper, a multi-population based hybrid genetic algorithm is developed for solving the JSSP. The population is divided in several groups at first and the hybrid algorithm is applied to the disjoint groups. Then the migration operator is used. The proposed approach, MP-HGA, have been compared with other algorithms for job-shop scheduling and evaluated with satisfactory results on a set of JSSPs derived from classical job-shop scheduling benchmarks. We have solved 15 benchmark problems and compared results obtained with a number of algorithms established in the literature. The experimental results show that MP-HGA could gain the best known makespan in 13 out of 15 problems

Negative life-cycle emissions growth rate through retrofit of existing institutional buildings: Energy Analysis and Life Cycle Assessment of a Case Study of University Dormitory Renovation

ABSTRACT: Buildings account for about one fifth of the world`s total delivered energy use, and thus methods for reducing energy consumption and carbon emission associated with buildings are crucial elements for climate change mitigation and sustainability. Voluntary challenges, mandates, and, particularly, public institutions have articulated these goals in terms of striving for “net-zero energy” buildings, and mandated measurable reductions in greenhouse gas emissions. Typically, the definition of net-zero and other energy consumption reduction goals only consider operational energy. By ignoring embodied energy during the entire life-cycle of the building (manufacture, use and demolition of materials and systems), such goals and mandates may drive suboptimal decisions in terms of cost-effective greenhouse gas emission reductions. Many new buildings will require decades of net-zero operational energy consumption to negate climate change and other environmental impacts during the construction process. Additionally, if a new building is part of a portfolio of institutional buildings, even with net-zero energy consumption, the most optimistic scenario is the eventual reduction of emission growth rate to zero. A more productive approach for reducing the life-cycle energy in a building and associated negative environmental impacts may be to focus on retrofitting existing buildings. However, since large investments in existing building stock can be difficult to justify and approve in an institutional context, fixed portions of life-cycle costs also highlight the importance of maximizing the operational energy impact associated with any renovation. This study uses life-cycle analysis to evaluate efficacy of energy retrofits for an existing institutional building located on the University of Massachusetts Amherst campus. Using data, energy models, and life-cycle analysis tools for an actual energy retrofit on an existing residential building, this study will show how poor controls and failing to address thermal bridges can affect our model expectations. By developing a process for life cycle based evaluating retrofit options this study will explore the implication of producing an institution-wide negative net-energy growth rate

Surface reconstructions and premelting of the (100) CaF2 surface

In this work, surface reconstructions on the (100) surface of CaF2 are comprehensively investigated. The configurations were explored by employing the Minima Hopping Method (MHM) coupled to a machine-learning interatomic potential, that is based on a charge equilibration scheme steered by a neural network (CENT). The combination of these powerful methods revealed about 80 different morphologies for the (100) surface with very similar surface formation energies differing by not more than 0.3 J m−2. To take into account the effect of temperature on the dynamics of this surface as well as to study the solid–liquid transformation, molecular dynamics simulations were carried out in the canonical (NVT) ensemble. By analyzing the atomic mean-square displacements (MSD) of the surface layer in the temperature range of 300–1200 K, it was found that in the surface region the F sublattice is less stable and more diffusive than the Ca sublattice. Based on these results we demonstrate that not only a bulk system, but also a surface can exhibit a sublattice premelting that leads to superionicity. Both the surface sublattice premelting and surface premelting occur at temperatures considerably lower than the bulk values. The complex behaviour of the (100) surface is contrasted with the simpler behavior of other low index crystallographic surfaces

Deep Spatiotemporal Clutter Filtering of Transthoracic Echocardiographic Images Using a 3D Convolutional Auto-Encoder

This study presents a deep convolutional auto-encoder network for filtering reverberation artifacts, from transthoracic echocardiographic (TTE) image sequences. Given the spatiotemporal nature of these artifacts, the filtering network was built using 3D convolutional layers to suppress the clutter patterns throughout the cardiac cycle. The network was designed by taking advantage of: i) an attention mechanism to focus primarily on cluttered regions and ii) residual learning to preserve fine structures of the image frames. To train the deep network, a diverse set of artifact patterns was simulated and the simulated patterns were superimposed onto artifact-free ultra-realistic synthetic TTE sequences of six ultrasound vendors to generate input of the filtering network. The artifact-free sequences served as ground-truth. Performance of the filtering network was evaluated using unseen synthetic as well as in-vivo artifactual sequences. Satisfactory results obtained using the latter dataset confirmed the good generalization performance of the proposed network which was trained using the synthetic sequences and simulated artifact patterns. Suitability of the clutter-filtered sequences for further processing was assessed by computing segmental strain curves from them. The results showed that the large discrepancy between the strain profiles computed from the cluttered segments and their corresponding segments in the clutter-free images was significantly reduced after filtering the sequences using the proposed network. The trained deep network could process an artifactual TTE sequence in a fraction of a second and can be used for real-time clutter filtering. Moreover, it can improve the precision of the clinical indexes that are computed from the TTE sequences. The source code of the proposed method is available at: https://github.com/MahdiTabassian/Deep-Clutter-Filtering/tree/main.Comment: 18 pages, 14 figure