Component-based Analysis of Dynamic Search Performance

In many search scenarios, such as exploratory, comparative, or survey-oriented search, users interact with dynamic search systems to satisfy multi-aspect information needs. These systems utilize different dynamic approaches that exploit various user feedback granularity types. Although studies have provided insights about the role of many components of these systems, they used black-box and isolated experimental setups. Therefore, the effects of these components or their interactions are still not well understood. We address this by following a methodology based on Analysis of Variance (ANOVA). We built a Grid Of Points that consists of systems based on different ways to instantiate three components: initial rankers, dynamic rerankers, and user feedback granularity. Using evaluation scores based on the TREC Dynamic Domain collections, we built several ANOVA models to estimate the effects. We found that (i) although all components significantly affect search effectiveness, the initial ranker has the largest effective size, (ii) the effect sizes of these components vary based on the length of the search session and the used effectiveness metric, and (iii) initial rankers and dynamic rerankers have more prominent effects than user feedback granularity. To improve effectiveness, we recommend improving the quality of initial rankers and dynamic rerankers. This does not require eliciting detailed user feedback, which might be expensive or invasive.

An accurate star identification approach based on spectral graph matching for attitude measurement of spacecraft

Star identification is the foundation of star trackers, which are used to precisely determine the attitude of spacecraft. In this paper, we propose a novel star identification approach based on spectral graph matching. In the proposed approach, we construct a feature called the neighbor graph for each main star, transforming the star identification to the problem of finding the most similar neighbor graph. Then the rough search and graph matching are cooperated to form a dynamic search framework to solve the problem. In the rough search stage, the total edge weight in the minimum spanning tree of the neighbor graph is selected as an indicator, then the k-vector range search is applied for reducing the search scale. Spectral graph matching is utilized to achieve global matching, identifying all stars in the neighbor circle with good noise-tolerance ability. Extensive simulation experiments under the position noise, lost-star noise, and fake-star noise show that our approach achieves higher accuracy (mostly over 99%) and better robustness results compared with other baseline algorithms in most cases.

Co-researchers in motion: a journey of evolving relationships. A critical reflection on the co-operation between two older adults and a nurse researcher

Background: There is a growing amount of research in which older adults contribute as co-researchers. The quality of this research depends, among other factors, on the nature of relationships between professional researchers and co-researchers. Reflections on these relationships can offer structured insight into this form of research. Aim: Our reflection on the co-operation between two older adults and a nurse researcher aims to share the lessons learned based on a critical understanding of our journey. Our main questions are: 1. How has the relationship developed over time, including in terms of mutuality and equality? 2. Which moments have been decisive in this development? Conclusion: We regard our co-operative relationship as a ‘dynamic search’. The meaning of mutuality and equality may change over time and so enrich the relationships. There is a need for further understanding into how these values can be nurtured in different configurations of researchers and co-researchers. Implications for practice: Evolving relations can be nurtured through deliberative sharing of the perceptions, expectations and experiences of the researchers and co-researchers Combining a formal working atmosphere with informal moments helps the research team respond to the individual needs of its members To enhance equality and mutuality, it is important to appreciate and value everyone’s contribution rather than concentrating on ‘what ’or ‘how ’individuals contribute

Dynamic Automated Search of Shunting Routes within Mesoscopic Rail-Traffic Simulators

Software tools using computer simulations are frequently used in the research and optimization of railway transport systems. Such simulations serve to examine different railway traffic scenarios (which typically reflect different timetables and railway infrastructure configurations). During the simulation experiments, it is necessary, among other things, to solve tasks related to the determination of track routes along which individual trains or parts of train sets are moved. Many simulation tools require the basic and alternative permissible track routes to be manually specified before starting the simulations, which is a relatively tedious and time-consuming process. Classical graph algorithms cannot be applied to solve the problem of automatic calculation of the routes because they are unable to take into account the length of the object being moved or recognise changes in the direction of its movement. This article presents original innovative algorithms focused on automated dynamic search of track routes (applying an appropriate optimization criterion), which is performed during simulation experiments within simulators working at the mesoscopic level of detail. The algorithms are based on a mathematical model (represented by a specifically designed weighted digraph) that appropriately reflects the actual track infrastructure. The dynamic calculation of each specific track route for a train or a group of railway vehicles considers both the total train set length and the current railway infrastructure occupancy, including blocked parts of the infrastructure due to intervention of the interlocking system. In addition, the places where the train set movement direction is changed can be identified on each route found. Applications of the algorithms and of the mathematical model of the track layout are demonstrated on a model track infrastructure.

Optimization of Loneys Solenoid Design Using a Dynamic Search Based Technique

Particle swarm optimizer is one of the searched based stochastic technique that has a weakness of being trapped into local optima. Thus, to tradeoff between the local and global searches and to avoid premature convergence in PSO, a new dynamic quantum-based particle swarm optimization (DQPSO) method is proposed in this work. In the proposed method a beta probability distribution technique is used to mutate the particle with the global best position of the swarm. The proposed method can ensure the particles to escape from local optima and will achieve the global optimum solution more easily. Also, to enhance the global searching capability of the proposed method, a dynamic updated formula is proposed that will keep a good balance between the local and global searches. To evaluate the merit and efficiency of the proposed DQPSO method, it has been tested on some well-known mathematical test functions and a standard benchmark problem known as Loney’s solenoid design.

Search for a moving target in a competitive environment

We consider a discrete-time dynamic search game in which a number of players compete to find an invisible object that is moving according to a time-varying Markov chain. We examine the subgame perfect equilibria of these games. The main result of the paper is that the set of subgame perfect equilibria is exactly the set of greedy strategy profiles, i.e. those strategy profiles in which the players always choose an action that maximizes their probability of immediately finding the object. We discuss various variations and extensions of the model.