Atomic Resolution Imaging of Currents in Nanoscopic Quantum Networks via Scanning Tunneling Microscopy

We propose a new method for atomic-scale imaging of spatial current patterns in nanoscopic quantum networks by using scanning tunneling microscopy (STM). By measuring the current flowing from the STM tip into one of the leads attached to the network as a function of tip position, one obtains an atomically resolved spatial image of "current riverbeds" whose spatial structure reflects the coherent flow of electrons out of equilibrium. We show that this method can be successfully applied in variety of network topologies, and is robust against dephasing effects.Comment: 5 page

The behaviour of reinforced concrete flat plate structures subjected to various combinations of vertical and horizontal loads

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Current Eigenmodes and Dephasing in Nanoscopic Quantum Networks

Using the non-equilibrium Keldysh Green's function formalism, we show that the non-equilibrium charge transport in nanoscopic quantum networks takes place via {\it current eigenmodes} that possess characteristic spatial patterns. We identify the microscopic relation between the current patterns and the network's electronic structure and topology and demonstrate that these patterns can be selected via gating or constrictions, providing new venues for manipulating charge transport at the nanoscale. Finally, decreasing the dephasing time leads to a smooth evolution of the current patterns from those of a ballistic quantum network to those of a classical resistor network.Comment: 6 pages, 4 figure

Effect of various factors on the rigidity of furniture cases

Cases are one of the most important types of furniture produced, yet relatively little research has been done on the case rigidity. In this study, types of the fastener properties to overall case rigidity wereinvestigated along with the effect of material type and thickness on stiffness. A total of sixteen cases were constructed and tested. Results indicated that panel thickness and material type significantlyincreased structural stiffness of case type furniture. The results of experiments showed that the stiffness of case furniture could be increased by increasing the material thickness from 16 to 18 mm.Medium density fiberboard (MDF) cases, in both doweled and screwed ones, were stiffer than particleboard cases. Results indicated that the stiffness of case furniture could be increased by increasing the stiffness of corner joints, e.g. by using screw with glue instead of using only screw orapplying glue to the dowels and whole edges instead of dowels only. Case furniture designs using screws with glue resulted in higher case stiffness than similar designs using glued dowel joints. In general, the stiffer the end connection, the less the deflection of the case was observed. The results also indicated that rigidity of case furniture comes mainly from the gluing of the joining surfaces. Therefore, knowing the rigidity of the case furniture made of wood composites is fundamental to the design of safe, cost efficient and aesthetic design

Re-mining item associations: methodology and a case study in apparel retailing

Association mining is the conventional data mining technique for analyzing market basket data and it reveals the positive and negative associations between items. While being an integral part of transaction data, pricing and time information have not been integrated into market basket analysis in earlier studies. This paper proposes a new approach to mine price, time and domain related attributes through re-mining of association mining results. The underlying factors behind positive and negative relationships can be characterized and described through this second data mining stage. The applicability of the methodology is demonstrated through the analysis of data coming from a large apparel retail chain, and its algorithmic complexity is analyzed in comparison to the existing techniques

Solution methods for controlled queueing networks

In this dissertation we look at a controlled queueing network where a controller routes the incoming arrivals to parallel queues using state-dependent rules. Besides this general arrival there are dedicated arrivals to each queue. The dedicated arrivals can only be served by their designated server, hence there is no routing decision involved. The goal of the controller is to find a stationary policy that will minimize the average number of customers in the system;The problem is modeled as a semi-Markov decision process and solved using techniques from the theory of Markov decision processes. We develop an efficient policy iteration based methodology which performs better than the value iteration method which is widely thought of as the best method to use for large-scale problems. The novelty in our approach is to use iterative methods in solving the system of linear equations, and also take advantage of the sparsity of matrices. The methodology could be used for other problems that are similar in nature. Using this methodology we solve much larger problems than reported in the literature. We also look at how several heuristic methods perform on our problem. No heuristic method is suitable to use for all instances. In general, however, these heuristic methods offer quick and reasonable solutions to very large problems

Vehicle Travel Time Estimation Using Sequence Prediction

This paper proposes a region-based travel time and traffic speed prediction method using sequence prediction. Floating Car Data collected from 8,317 vehicles during 34 days are used for evaluation purposes. Twelve districts are chosen and the spatio-temporal non-linear relations are learned with Recurrent Neural Networks. Time estimation of the total trip is solved by travel time estimation of the divided sub-trips, which are constituted between two consecutive GNSS measurement data. The travel time and final speed of sub-trips are learned with Long Short-term Memory cells using sequence prediction. A sequence is defined by including the day of the week meta-information, dynamic information about vehicle route start and end positions, and average travel speed of the road segment that has been traversed by the vehicle. The final travel time is estimated for this sequence. The sequence-based prediction shows promising results, outperforms function mapping and non-parametric linear velocity change based methods in terms of root-mean-square error and mean absolute error metrics.</p