Quantum Walk on a Line with Two Entangled Particles

We introduce the concept of a quantum walk with two particles and study it for the case of a discrete time walk on a line. A quantum walk with more than one particle may contain entanglement, thus offering a resource unavailable in the classical scenario and which can present interesting advantages. In this work, we show how the entanglement and the relative phase between the states describing the coin degree of freedom of each particle will influence the evolution of the quantum walk. In particular, the probability to find at least one particle in a certain position after $N$ steps of the walk, as well as the average distance between the two particles, can be larger or smaller than the case of two unentangled particles, depending on the initial conditions we choose. This resource can then be tuned according to our needs, in particular to enhance a given application (algorithmic or other) based on a quantum walk. Experimental implementations are briefly discussed

Spin-Space Entanglement Transfer and Quantum Statistics

Both the topics of entanglement and particle statistics have aroused enormous research interest since the advent of quantum mechanics. Using two pairs of entangled particles we show that indistinguishability enforces a transfer of entanglement from the internal to the spatial degrees of freedom without any interaction between these degrees of freedom. Moreover, sub-ensembles selected by local measurements of the path will in general have different amounts of entanglement in the internal degrees of freedom depending on the statistics (either fermionic or bosonic) of the particles involved.Comment: 5 figures. Various changes for clarification and references adde

Optimal State Discrimination Using Particle Statistics

We present an application of particle statistics to the problem of optimal ambiguous discrimination of quantum states. The states to be discriminated are encoded in the internal degrees of freedom of identical particles, and we use the bunching and antibunching of the external degrees of freedom to discriminate between various internal states. We show that we can achieve the optimal single-shot discrimination probability using only the effects of particle statistics. We discuss interesting applications of our method to detecting entanglement and purifying mixed states. Our scheme can easily be implemented with the current technology

Harnessing the Power Within: The Consequences of Salesperson Moral Identity and the Moderating Role of Internal Competitive Climate

The purpose of this research is to examine the notion of salesperson moral identity as a prosocial individual trait and its associated effects on customer and coworker relationships. In addition, this study examines the underlying processes in which these effects occur as well as the moderating role of internal competitive climate. Our empirical investigation of business-to-business (B2B) sales professionals reveals that moral identity has both direct and indirect effects on a salesperson’s customer- and team-directed outcomes. Specifically, our results demonstrate that salesperson moral identity positively affects both salesperson-customer identification and organizational identification, which, in turn, impact customer service provision and teamwork. Our findings also indicate that internal competitive climate exacerbates the positive effects of salesperson moral identity on customer service provision and teamwork

First and second laws of thermodynamics analysis of nanofluid flow inside a heat exchanger duct with wavy walls and a porous insert

This paper investigates the combined effects of using nanofluid, a porous insert and corrugated walls on heat transfer, pressure drop and entropy generation inside a heat exchanger duct. A series of numerical simulations are conducted for a number of pertinent parameters. It is shown that the waviness of the wall destructively affects the heat transfer process at low wave amplitudes and that it can improve heat convection only after exceeding a certain amplitude. Further, the pressure drop in the duct is found to be strongly influenced by the wave amplitude in a highly non-uniform way. The results, also, show that the second law and heat transfer performances of the system improve considerably by thickening the porous insert and decreasing its permeability. Yet, this is associated with higher pressure drops. It is argued that the hydraulic, thermal and entropic behaviours of the system are closely related to the interactions between a vortex formation near the wavy walls and nanofluid flow through the porous insert. Viscous irreversibilities are shown to be dominant in the core region of duct where the porous insert is placed. However, in the regions closer to the wavy walls, thermal entropy generation is the main source of irreversibility. A number of design recommendations are made on the basis of the findings of this study

Biometric signature verification system based on freeman chain code and k-nearest neighbor

Signature is one of human biometrics that may change due to some factors, for example age, mood and environment, which means two signatures from a person cannot perfectly matching each other. A Signature Verification System (SVS) is a solution for such situation. The system can be decomposed into three stages: data acquisition and preprocessing, feature extraction and verification. This paper presents techniques for SVS that uses Freeman chain code (FCC) as data representation. Before extracting the features, the raw images will undergo preprocessing stage; binarization, noise removal, cropping and thinning. In the first part of feature extraction stage, the FCC was extracted by using boundary-based style on the largest contiguous part of the signature images. The extracted FCC was divided into four, eight or sixteen equal parts. In the second part of feature extraction, six global features were calculated against split image to test the feature efficiency. Finally, verification utilized Euclidean distance to measured and matched in k-Nearest Neighbors. MCYT bimodal database was used in every stage in the system. Based on the experimental results, the lowest error rate for FRR and FAR were 6.67 % and 12.44 % with AER 9.85 % which is better in term of performance compared to other works using that same database

Incorporating capacitative constraint to the preference-based conference scheduling via domain transformation approach

No AbstractKeywords: conference scheduling; domain transformation approach; capacity optimizatio

Monetary costs of agitation in older adults with Alzheimer's disease in the UK: prospective cohort study

While nearly half of all people with Alzheimer's disease (AD) have agitation symptoms every month, little is known about the costs of agitation in AD. We calculated the monetary costs associated with agitation in older adults with AD in the UK from a National Health Service and personal social services perspective

Software framework for optimization problems and meta-heuristics based on scripting language

No AbstractKeywords: software framework; scripting language;optimization;meta-heuristic

A Comprehensive Overview of Classical and Modern Route Planning Algorithms for Self-Driving Mobile Robots

Mobile robots are increasingly being applied in a variety of sectors, including agricultural, firefighting, and search and rescue operations. Robotics and autonomous technology research and development have played a major role in making this possible. Before a robot can reliably and effectively navigate a space without human aid, there are still several challenges to be addressed. When planning a path to its destination, the robot should be able to gather information from its surroundings and take the appropriate actions to avoid colliding with obstacles along the way. The following review analyses and compares 200 articles from two databases, Scopus and IEEE Xplore, and selects 60 articles as references from those articles. This evaluation focuses mostly on the accuracy of the different path-planning algorithms. Common collision-free path planning methodologies are examined in this paper, including classical or traditional and modern intelligence techniques, as well as both global and local approaches, in static and dynamic environments. Classical or traditional methods, such as Roadmaps (Visibility Graph and Voronoi Diagram), Potential Fields, and Cell Decomposition, and modern methodologies such as heuristic-based (Dijkstra Method, A* Algorithms, and D* Algorithms), metaheuristics algorithms (such as PSO, Bat Algorithm, ACO, and Genetic Algorithm), and neural systems such as fuzzy neural networks or fuzzy logic (FL) and Artificial Neural Networks (ANN) are described in this report. In this study, we outline the ideas, benefits, and downsides of modeling and path-searching technologies for a mobile robot