Ant colony optimization with immigrants schemes for the dynamic railway junction rescheduling problem with multiple delays

Train rescheduling after a perturbation is a challenging task and is an important concern of the railway industry as delayed trains can lead to large fines, disgruntled customers and loss of revenue. Sometimes not just one delay but several unrelated delays can occur in a short space of time which makes the problem even more challenging. In addition, the problem is a dynamic one that changes over time for, as trains are waiting to be rescheduled at the junction, more timetabled trains will be arriving, which will change the nature of the problem. The aim of this research is to investigate the application of several different ant colony optimization (ACO) algorithms to the problem of a dynamic train delay scenario with multiple delays. The algorithms not only resequence the trains at the junction but also resequence the trains at the stations, which is considered to be a first step towards expanding the problem to consider a larger area of the railway network. The results show that, in this dynamic rescheduling problem, ACO algorithms with a memory cope with dynamic changes better than an ACO algorithm that uses only pheromone evaporation to remove redundant pheromone trails. In addition, it has been shown that if the ant solutions in memory become irreparably infeasible it is possible to replace them with elite immigrants, based on the best-so-far ant, and still obtain a good performance.     

E-commerce system quality assessment using a model based on ISO 9126 and Belief Networks

As business transitions into the new economy, e-system successful use has become a strategic goal. Especially in business to consumer (e-commerce) applications, users highly evaluate the quality of their interactive shopping experience. However, quality is difficult to define and measure and most importantly, it is difficult to measure its impact on the end-user. Among the many research questions that arise, some of the most important concern the exact nature of the quality attributes that define an e-commerce system, and how one could model these attributes in order to increase its acceptance. Bearing in mind that e-commerce systems are actually user/data-intensive web-based software systems, this work performed a survey which resulted in a theoretical model that helps to measure such systems’ dynamics through their decomposition into primary quality characteristics. The proposed model is based on Bayesian Networks and ISO 9126. Besides the emphasis on specific software quality attributes, it also provides a quality assessment process aiding developers to design and produce e-commerce systems of high quality. Using a Bayesian Network the model can be used to combine different types of evidences and provide reasoning from effect to cause and vice versa.

Efficient range query processing in metric spaces over highly distributed data

Similarity search in P2P systems has attracted a lot of attention recently and several important applications, like distributed image search, can profit from the proposed distributed algorithms. In this paper, we address the challenging problem of efficient processing of range queries in metric spaces, where data is horizontally distributed across a super-peer network. Our approach relies on SIMPEER (Doulkeridis et al. in Proceedings of VLDB, pp. 986–997, 2007), a framework that dynamically clusters peer data, in order to build distributed routing information at super-peer level. SIMPEER allows the evaluation of exact range and nearest neighbor queries in a distributed manner that reduces communication cost, network latency, bandwidth consumption and computational overhead at each individual peer. In this paper, we extend SIMPEER by focusing on efficient range query processing and providing recall-based guarantees for the quality of the result retrieved so far. This is especially useful for range queries that lead to result sets of high cardinality and incur high processing costs, while the complete result set becomes overwhelming for the user. Our framework employs statistics for estimating an upper limit of the number of possible results for a range query and each super-peer may decide not to propagate further the query and reduce the scope of the search. We provide an experimental evaluation of our framework and show that our approach performs efficiently, even in the case of high degree of distribution.     

A genetically optimized level set approach to segmentation of thyroid ultrasound images

This paper presents a novel framework for thyroid ultrasound image segmentation that aims to accurately delineate thyroid nodules. This framework, named GA-VBAC incorporates a level set approach named Variable Background Active Contour model (VBAC) that utilizes variable background regions, to reduce the effects of the intensity inhomogeneity in the thyroid ultrasound images. Moreover, a parameter tuning mechanism based on Genetic Algorithms (GA) has been considered to search for the optimal VBAC parameters automatically, without requiring technical skills. Experiments were conducted over a range of ultrasound images displaying thyroid nodules. The results show that the proposed GA-VBAC framework provides an efficient, effective and highly objective system for the delineation of thyroid nodules.     

Investigation of Magnetotaxis of Reconfigurable Micro‐Origami Swimmers with Competitive and Cooperative Anisotropy

Recent advances in magnetic nanocomposites have enabled untethered micromachines with controllable shape transformations and programmable magnetic anisotropy, paving the way for a variety of biomedical applications using soft microrobots. Magnetic anisotropy is programmed by assembling the embedded magnetic nanoparticles (MNPs) in polymeric materials to overcome the shape anisotropy of a given structure. However, this approach is questionably effective in reconfigurable structures, as shape changes naturally result in rearrangement of the embedded MNPs. A naturally occurring solution to this problem is found in magnetotactic bacteria, which build chains of MNPs in a linear‐chain formation in their cells to create a permanent magnetic dipole moment. This dipole moment enables them to actively sense magnetic fields and coordinate their movement in response, a behavior called magnetotaxis. Inspired by this, self‐folding micro‐origami swimmers comprising magnetic nanocomposite bilayer structures that exhibit controllable shape transformations and programmable, shape‐independent magnetotaxis is fabricated. A study of these structures reveals that their magnetic anisotropy results from competition or cooperation between anisotropy of assembled chains of MNPs and overall shape anisotropy. Moreover, how the magnetotaxis of the reconfigurable micro‐origami swimmers depends only on the embedded permanent dipole moment, independent of the overall magnetic anisotropy, is demonstrated.     

Entropy-Based Sensor Placement Optimization for Waterloss Detection in Water Distribution Networks

The work presented herein addresses the problem of sensor placement optimization in urban water distribution networks by use of an entropy-based approach, for the purpose of efficient and economically viable waterloss incident detection. The proposed method is applicable to longitudinal rather than spatial sensing, thus to devices such as acoustic, pressure, or flow sensors acting on pipe segments. The method utilizes the maximality, subadditivity and equivocation properties of entropy, coupled with a statistical definition of the probability of sensing within a pipe segment, to assign an entropy metric to each pipe segment and subsequently optimize the location of sensors in the network based on maximizing the total entropy in the network. The method proposed is a greedy-search heuristic.     

Multimedia search and retrieval using multimodal annotation propagation and indexing techniques

In this paper, a complete solution for search and retrieval of rich multimedia content over modern databases is presented. The framework proposed in this paper combines the advantages of multimodal search with those of annotation propagation into a unified system. Moreover, an effective technique, which is appropriate for large-scale indexing, is adopted, extended and integrated to the proposed framework so as to achieve optimized search and retrieval of rich media content even from large-scale databases.     

Prospective energy needs in Mediterranean offshore aquaculture: Renewable and sustainable energy solutions

Offshore aquaculture industry is considered among the fastest growing industries worldwide. However, further expansion of this industry requires larger breeding installations positioned even further from shore. These installations inevitably would require substantial automation powered by appropriate energy sources. Thus, this paper investigates appropriate sustainable renewable energy generation solutions to meet anticipated needs. Firstly, an account of energy requirements of a typical offshore aquaculture installation is presented to realise the scale of energy required. Subsequently, current status of solar, wind, wave and current renewable energy technologies is given and their applicability for Eastern Mediterranean offshore aquaculture is investigated. Finally, further challenges and research milestones to overcome are discussed.     

‘Closing the loop’ in biological systems modeling — From the in silico to the in vitro

This work presents a holistic ‘closed loop’ approach for the development of models of biological systems. The ever-increasing availability of experimental information necessitates the advancement of a systematic methodology to organise and utilise these data. Herein, we present a biological model building framework that maps the treatment of the information from the initial conception of the model, through its experimental validation and finally to its application in model-based optimisation studies. We highlight and discuss current issues associated with the development of mathematical models of biological systems and share our perspective towards a holistic ‘closed loop’ approach that will facilitate the control of the in vitro through the in silico.     

Solar assisted heat pump on air collectors: A simulation tool

The heating system of the bioclimatic building of the Greek National Centre for Renewable Energy Sources (CRES) comprises two heating plants: the first one includes an air source heat pump, Solar Air Collectors (SACs) and a heat distribution system (comprising a fan coil unit network); the second one is, mainly, a geothermal heat pump unit to cover the ground floor thermal needs. The SAC configuration as well as the fraction of the building heating load covered by the heating plant are assessed in two operation modes; the direct (hot air from the collectors is supplied directly to the heated space) and the indirect mode (warm air from the SAC or its mixture with ambient air is not supplied directly to the heated space but indirectly into the evaporator of the air source heat pump). The technique of the indirect mode of heating aims at maximizing the efficiency of the SAC, saving electrical power consumed by the compressor of the heat pump, and therefore, at optimizing the coefficient of performance (COP) of the heat pump due to the increased intake of ambient thermal energy by means of the SAC. Results are given for three research objectives: assessment of the heat pump efficiency whether in direct or indirect heating mode; Assessment of the overall heating plant efficiency on a daily or hourly basis; Assessment of the credibility of the suggested simulation model TSAGAIR by comparing its results with the TRNSYS ones.