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.     

Statistical Analysis and Modeling of Jobs in a Grid Environment

The existence of good probabilistic models for the job arrival process and the delay components introduced at different stages of job processing in a Grid environment is important for the improved understanding of the Grid computing concept. In this study, we present a thorough analysis of the job arrival process in the EGEE infrastructure and of the time durations a job spends at different states in the EGEE environment. We define four delay components of the total job delay and model each component separately. We observe that the job inter-arrival times at the Grid level can be adequately modelled by a rounded exponential distribution, while the total job delay (from the time it is generated until the time it completes execution) is dominated by the computing element’s register and queuing times and the worker node’s execution times. Further, we evaluate the efficiency of the EGEE environment by comparing the job total delay performance with that of a hypothetical ideal super-cluster and conclude that we would obtain similar performance if we submitted the same workload to a super-cluster of size equal to 34% of the total average number of CPUs participating in the EGEE infrastructure. We also analyze the job inter-arrival times, the CE’s queuing times, the WN’s execution times, and the data sizes exchanged at the kallisto.hellasgrid.gr cluster, which is node in the EGEE infrastructure. In contrast to the Grid level, we find that at the cluster level the job arrival process exhibits self-similarity/long-range dependence. Finally, we propose simple and intuitive models for the job arrival process and the execution times at the cluster level.     

Variation of density of diesel and biodiesel mixtures in three different temperature ranges

The use of biodiesel fuel is becoming increasingly imperative nowadays and it is necessary to know the change of density. In this work the variation of density of diesel-biodiesel blends across the scale of 0–100% by adding each time 2% biodiesel and then measuring the density of three different temperatures (5, 15, 25°C) covering and the usual scale of temperatures the use of mixtures of diesel-biodiesel. Through the extraction of equations can be known in advance the relationship of density of diesel-biodiesel blend, and temperature that is used.     

A structured methodology for assessing and improving e-services in digital cities

Many cities all over the world are making large investments for the construction of big network infrastructures, in order to offer to local public organizations, businesses and citizens high speed connectivity, and on top of them useful e-services, aiming to achieve various social and economic objectives. The value generated from these costly ‘digital city’ investments is actually the value provided to the citizens and businesses by the e-services that will be developed and run on these network infrastructures. This paper proposes and validates a structured methodology for assessing and improving e-services developed in digital cities. The proposed methodology assesses the various types of value generated by an e-service, and also the relations among them, allowing a more structured evaluation, a deeper understanding of the value generation process and also identification and prioritization of the improvements that should be made in the e-service with the usually scarce resources in municipalities. This methodology is applied for the evaluation of an e-learning service developed in the Greek city of Karlovassi for young citizens, and also for the prioritization of the necessary improvements in it. The above application provided positive and encouraging evidence concerning the validity, usefulness and applicability of the proposed method.