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.     

Novel Insights into Factor D Inhibition

Complement-mediated diseases or complementopathies, such as Paroxysmal nocturnal hemoglobinuria (PNH), cold agglutinin disease (CAD), and transplant-associated thrombotic microangiopathy (TA-TMA), demand advanced complement diagnostics and therapeutics be adopted in a vast field of medical specialties, such as hematology, transplantation, rheumatology, and nephrology. The miracle of complement inhibitors as “orphan drugs” has dramatically improved morbidity and mortality in patients with otherwise life-threatening complementopathies. Efficacy has been significantly improved by upstream inhibition in patients with PNH. Different molecules may exert diverse characteristics in vitro and in vivo. Further studies remain to show safety and efficacy of upstream inhibition in other complementopathies. In addition, cost and availability issues are major drawbacks of current treatments. Therefore, further developments are warranted to address the unmet clinical needs in the field of complementopathies. This state-of-the-art narrative review aims to delineate novel insights into factor D inhibition as a promising target for complementopathies.     

A low-complexity 3-D geometric model for the description of CCI in cellular systems

In this paper, we propose a three-dimensional geometrically based stochastic model for the study of co-channel interference in cellular mobile systems. Exact analytical expressions for the statistics of the angle-of-arrival (AoA) of the interfering signals at the base station and the outage performance of a cellular network are derived. Simulated results show the merits of our proposal compared to the two-dimensional approach. The uplink AoA distribution statistics are examined in relation to cell size, antennas’ height and frequency reuse factor. Furthermore, the impact of base station antenna radiation pattern, beam tilting and cell sectorization on system performance is investigated.     

Structure–properties investigations in hydrophilic nanocomposites based on polyurethane/poly(2–hydroxyethyl methacrylate) semi‐interpenetrating polymer networks and nanofiller densil for biomedical application

Nanocomposites based on sequential semi–interpenetrating polymer networks (semi–IPNs) of crosslinked polyurethane and linear poly(2‐hydroxyethyl methacrylate) filled with 1–15 wt % of nanofiller densil were prepared and investigated. Nanofiller densil used in an attempt to control the microphase separation of the polymer matrix by polymer–filler interactions. The morphology (SAXS, AFM), mechanical properties (stress–strain), thermal transitions (DSC) and polymer dynamics (DRS, TSDC) of the nanocomposites were investigated. Special attention has been paid to the raising of the hydration properties and the dynamics of water molecules in the nanocomposites in the perspective of biomedical applications. Nanoparticles were found to aggregate partially for higher than 3 and 5 wt % filler loading in semi–IPNs with 17 and 37 wt % PHEMA, respectively. The results show that the good hydration properties of the semi–IPN matrix are preserved in the nanocomposites, which in combination with results of thermal and dielectric techniques revealed also the existence of polymer–polymer and polymer–filler interactions. These interactions results also in the improvement of physical and mechanical properties of the nanocomposites in compare with the neat matrix. The improvement of mechanical properties in combination with hydrophilicity and biocompatibility of nanocomposites are promising for use these materials for biomedical application namely as surgical films for wound treatment and as material for producing the medical devises. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43122.     

An Advanced Centrifugal Technique to Characterize the Sticking Properties of Tomato Pulp during Drying

An advanced centrifugal technique was developed to characterize the stickiness of tomato pulp at various moisture contents and temperatures. To provide tomato pulp samples with different moisture contents, tomato powder produced by a laboratory spray dryer was wetted to six different moisture levels. By noting the rotational speed for a tomato pulp droplet detachment and weighing the mass of the droplet remaining on the surface it is possible to gauge the approximate attachment forces holding the droplet to the substrate. The effects of droplet mass and centrifugation time after reaching the desired rotor speed on the detachment rate were also studied. The detachment force was found to increase at the beginning and then started decreasing as the moisture content decreased presenting a maximum at moisture content of about 34%. In addition, the higher the droplet temperature, the higher the attachment force.