Cost and Land Functions for Wastewater Treatment Projects: Typical Simple Linear Regression versus Fuzzy Linear Regression

This paper analyzes cost data pertinent to the municipal wastewater treatment plants (MWTP) in Greece. First, data have been collected with onsite visits and contain the land size necessary for a MWTP, the construction cost, and the operation and maintenance cost of existing wastewater treatment facilities. Second, they come from analytical budgeting of natural wastewater treatment system units. Twelve equations of the form ln?Yi = A0i+A1i?ln?Xi are estimated both with ordinary least squares (OLS) and fuzzy linear regression (FLR). The root mean square error and the mean absolute error are used as fitting measures for the comparison of the OLS with the fuzzy estimations. It is shown that the FLR did produce very similar estimates but slightly inferior to those of OLS in most of the cases for these particular datasets.     

A higher-order unsplit-field perfectly matched layer for the reflectionless truncation of 3-D spherical FDTD lattices

An enhanced higher-order finite-difference time-domain (FDTD) method for the systematic implementation of 3-D reflectionless perfectly matched layers (PML) in spherical coordinates is presented. By establishing a topologically unsplit-field formulation, the novel technique introduces accurate nonstandard schemes that eliminate the notably intricate lattice dispersion errors. Moreover, the wider spatial increments are treated via self-adaptive compact operators, while a mesh expansion process yields a significant reduction of the absorber's depth. For the temporal variable, the proposed method employs a multistage leapfrog integration that guarantees stability and excitation universality. Hence, because of the optimal configuration of the new PML, it attains a critical annihilation of both propagating and spurious wave families, even for complicated domains. Numerical investigation verifies the superiority of the higher-order algorithm via several unbounded radiation and scattering spherical problems.     

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.     

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.     

Algorithm visualization using tree graphs

Recent advances in graphics workstations allow the development of improved visualization tools for algorithm and program development. Algorithm visualization permits better analysis, development, and presentation of the algorithm characteristics. In this paper, we present a simple algorithm visualization technique using tree graphs. The technique is applied to the visualization of three sorting algorithms: the bubble sort, the quicksort, and the merge and sort, and one matrix algorithm, the Gaussian elimination. Key states of the data are displayed on the nodes, while the graph itself represents the underlying structure of the algorithm. All graphics are displayed under the X Window environment using simple graphics and window programming techniques.     

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.     

Introducing web service accessibility assessment techniques through a unified quality of service context

This paper introduces novel web service (WS) accessibility assessment techniques through a unified Quality of Services (QoS) context. The goal is to enable future QoS-aware service selection systems to select and provide accessible web services, ones that are properly designed so as to allow their consumption from end-user applications, used from people with disabilities. In this line, a WS accessibility assessment Framework (WSaaF) has been developed, on the basis of WS accessibility guidelines, dealing with accessibility issues that can appear both on the presentation level of content delivered through WSs and on the content level itself. The WSaaF and its guidelines follow the rationale behind W3C WCAG 2.0-based accessibility standardization of web content. It provides the basis toward building future accessible WSs, a task that can be further facilitated by the use of an appropriate Tool (WSaaT), developed with the aim to provide automatic assessment of services, against guidelines of the proposed framework. Then, the WS accessibility attribute is introduced, as a metric that can be used in conjunction to ones typically utilized so far, within QoS-aware service selection systems. As a result, a novel unified QoS framework is proposed, incorporating the notion of accessibility in the service selection process. The proposed unified QoS framework can eventually lead to the provision of services, which are selected from appropriate repositories and better suit the special needs of people with disabilities.     

Reliability assessment of cutting tool life based on surrogate approximation methods

A novel reliability estimation approach to the cutting tools based on advanced approximation methods is proposed. Methods such as the stochastic response surface and surrogate modeling are tested, starting from a few sample points obtained through fundamental experiments and extending them to models able to estimate the tool wear as a function of the key process parameters. Subsequently, different reliability analysis methods are employed such as Monte Carlo simulations and first- and second-order reliability methods. In the present study, these reliability analysis methods are assessed for estimating the reliability of cutting tools. The results show that the proposed method is an efficient method for assessing the reliability of the cutting tool based on the minimum number of experimental results. Experimental verification for the case of high-speed turning confirms the findings of the present study for cutting tools under flank wear.