Bond of steel reinforcement with microwave cured concrete repair mortars

This paper investigates the effect of microwave curing on the bond strength of steel reinforcement in concrete repair. Pull-out tests on plain mild steel reinforcement bars embedded in four repair materials in 100 mm cube specimens were performed to determine the interfacial bond strength. The porosity and pore structure of the matrix at the steel interface, which influence the bond strength, were also determined. Test results show that microwave curing significantly reduces the bond strength of plain steel reinforcement. The reduction relative to normally cured (20 °C, 60% RH) specimens is between 21 and 40% with low density repair materials and about 10% for normal density cementitious mortars. The corresponding compressive strength of the matrix also recorded similar reduction and microwave curing resulted in increased porosity at the interface transition zone of the steel reinforcement. A unique relationship exists between bond strength and both compressive strength and porosity of all matrix materials. Microwave curing reduced shrinkage but despite the wide variation in the shrinkage of the repair mortars, its effect on the bond strength was small. The paper provides clear correlations between the three parameters (compressive strength, bond strength and porosity), which are common to both the microwave and conventionally cured mortars. Therefore, bond-compressive strength relationships used in the design of reinforced concrete structures will be also valid for microwave cured elements.     

A well‐conditioned and optimally convergent XFEM for 3D linear elastic fracture

A variation of the extended finite element method for three‐dimensional fracture mechanics is proposed. It utilizes a novel form of enrichment and point‐wise and integral matching of displacements of the standard and enriched elements in order to achieve higher accuracy, optimal convergence rates, and improved conditioning for two‐dimensional and three‐dimensional crack problems. A bespoke benchmark problem is introduced to determine the method's accuracy in the general three‐dimensional case where it is demonstrated that the proposed approach improves the accuracy and reduces the number of iterations required for the iterative solution of the resulting system of equations by 40% for moderately refined meshes and topological enrichment. Moreover, when a fixed enrichment volume is used, the number of iterations required grows at a rate which is reduced by a factor of 2 compared with standard extended finite element method, diminishing the number of iterations by almost one order of magnitude. Copyright © 2015 John Wiley & Sons, Ltd.     

ROSy+: 3D Object Pose Normalization Based on PCA and Reflective Object Symmetry with Application in 3D Object Retrieval

A novel pose normalization method based on 3D object reflective symmetry is presented. It is a general purpose global pose normalization method; in this paper it is used to enhance the performance of a 3D object retrieval pipeline. Initially, the axis-aligned minimum bounding box of a rigid 3D object is modified by requiring that the 3D object is also in minimum angular difference with respect to the normals to the faces of its bounding box. To estimate the modified axis-aligned bounding box, a set of predefined planes of symmetry are used and a combined spatial and angular distance, between the 3D object and its symmetric object, is calculated. By minimizing the combined distance, the 3D object fits inside its modified axis-aligned bounding box and alignment with the coordinate system is achieved. The proposed method is incorporated in a hybrid scheme, that serves as the alignment method in a 3D object retrieval system. The effectiveness of the 3D object retrieval system, using the hybrid pose normalization scheme, is evaluated in terms of retrieval accuracy and demonstrated using both quantitative and qualitative measures via an extensive consistent evaluation on standard benchmarks. The results clearly show performance boost against current approaches.     

Presenting DEViSE: Data Exchange for Visualizing Security Events

The Data Exchange for Visualizing Security Events (DEViSE) is an open-source architecture designed to enable data sharing between security visualization tools. The security visualization market currently lacks interoperability between different applications, which tend to be constrained to certain log formats. DEViSE is a middleware layer that manages these interactions so one visualization tool can transfer security-related information to another application. DEViSE uses XML for all communication purposes. This allows a much greater level of freedom for application integration. To demonstrate DEViSE, the authors have created several security visualization tools that adhere to different visualization paradigms.     

Investigation of isolated thunderstorms lightning activity over eastern Mediterranean using Meteosat rapid scan infrared imagery

An analysis of selected spatiotemporal characteristics of isolated thunderstorms in relation to cloud-to-ground (CG) lightning over part of the eastern Mediterranean is performed. The purpose of the study is twofold: to better understand and improve the basic knowledge of the physical mechanisms of the phenomenon and to offer new means of nowcasting the lightning activity in such thunderstorms. Meteosat Second Generation (MSG) Rapid Scan Service (RSS) infrared imagery which offers the option of tracking the examined storms with a time frequency of 5 minutes is one of the two utilized datasets, the other being ZEUS very low frequency (VLF) lightning detection system’s CG discharge data. It was shown that a cloud top temperature of about ?20°C is required for the onset of lightning activity. A rapid drop of the cloud-top temperature of about 11°C in 5 minutes on average is observed a few minutes before or during lightning initiation. The maximization of the activity is usually quite close to the overall minimum cloud-top temperature of the cell. A temperature increase of 3.5°C from this overall minimum can mark the end of the activity, which is also associated to the time evolution of the cell’s horizontal extent. In fact, after the cell’s horizontal area stops to increase and/or starts to gradually diminish, CG lightning activity is expected to stop.     

Creating corroborated crisis reports from social media data through formal concept analysis

During a crisis citizens reach for their smart phones to report, comment and explore information surrounding the crisis. These actions often involve social media and this data forms a large repository of real-time, crisis related information. Law enforcement agencies and other first responders see this information as having untapped potential. That is, it has the capacity extend their situational awareness beyond the scope of a usual command and control centre. Despite this potential, the sheer volume, the speed at which it arrives, and unstructured nature of social media means that making sense of this data is not a trivial task and one that is not yet satisfactorily solved; both in crisis management and beyond. Therefore we propose a multi-stage process to extract meaning from this data that will provide relevant and near real-time information to command and control to assist in decision support. This process begins with the capture of real-time social media data, the development of specific LEA and crisis focused taxonomies for categorisation and entity extraction, the application of formal concept analysis for aggregation and corroboration and the presentation of this data via map-based and other visualisations. We demonstrate that this novel use of formal concept analysis in combination with context-based entity extraction has the potential to inform law enforcement and/or humanitarian responders about on-going crisis events using social media data in the context of the 2015 Nepal earthquake.     

HISYCOL a hybrid computational intelligence system for combined machine learning: the case of air pollution modeling in Athens

The analysis of air quality and the continuous monitoring of air pollution levels are important subjects of the environmental science and research. This problem actually has real impact in the human health and quality of life. The determination of the conditions which favor high concentration of pollutants and most of all the timely forecast of such cases is really crucial, as it facilitates the imposition of specific protection and prevention actions by civil protection. This research paper discusses an innovative threefold intelligent hybrid system of combined machine learning algorithms HISYCOL (henceforth). First, it deals with the correlation of the conditions under which high pollutants concentrations emerge. On the other hand, it proposes and presents an ensemble system using combination of machine learning algorithms capable of forecasting the values of air pollutants. What is really important and gives this modeling effort a hybrid nature is the fact that it uses clustered datasets. Moreover, this approach improves the accuracy of existing forecasting models by using unsupervised machine learning to cluster the data vectors and trace hidden knowledge. Finally, it employs a Mamdani fuzzy inference system for each air pollutant in order to forecast even more effectively its concentrations.     

A survey of multilinear subspace learning for tensor data

Increasingly large amount of multidimensional data are being generated on a daily basis in many applications. This leads to a strong demand for learning algorithms to extract useful information from these massive data. This paper surveys the field of multilinear subspace learning (MSL) for dimensionality reduction of multidimensional data directly from their tensorial representations. It discusses the central issues of MSL, including establishing the foundations of the field via multilinear projections, formulating a unifying MSL framework for systematic treatment of the problem, examining the algorithmic aspects of typical MSL solutions, and categorizing both unsupervised and supervised MSL algorithms into taxonomies. Lastly, the paper summarizes a wide range of MSL applications and concludes with perspectives on future research directions.     

Prefetching in Content Distribution Networks via Web Communities Identification and Outsourcing

Content distribution networks (CDNs) improve scalability and reliability, by replicating content to the “edge” of the Internet. Apart from the pure networking issues of the CDNs relevant to the establishment of the infrastructure, some very crucial data management issues must be resolved to exploit the full potential of CDNs to reduce the “last mile” latencies. A very important issue is the selection of the content to be prefetched to the CDN servers. All the approaches developed so far, assume the existence of adequate content popularity statistics to drive the prefetch decisions. Such information though, is not always available, or it is extremely volatile, turning such methods problematic. To address this issue, we develop self-adaptive techniques to select the outsourced content in a CDN infrastructure, which requires no apriori knowledge of request statistics. We identify clusters of “correlated” Web pages in a site, called Web site communities, and make these communities the basic outsourcing unit. Through a detailed simulation environment, using both real and synthetic data, we show that the proposed techniques are very robust and effective in reducing the user-perceived latency, performing very close to an unfeasible, off-line policy, which has full knowledge of the content popularity.