Trends Prediction Using Social Diffusion Models

The importance of the ability of predict trends in social media has been growing rapidly in the past few years with the growing dominance of social media in our everyday's life. Whereas many works focus on the detection of anomalies in networks, there exist little theoretical work on the prediction of the likelihood of anomalous network pattern to globally spread and become "trends". In this work we present an analytic model the social diffusion dynamics of spreading network patterns. Our proposed method is based on information diffusion models, and is capable of predicting future trends based on the analysis of past social interactions between the community's members. We present an analytic lower bound for the probability that emerging trends would successful spread through the network. We demonstrate our model using two comprehensive social datasets - the "Friends and Family" experiment that was held in MIT for over a year, where the complete activity of 140 users was analyzed, and a financial dataset containing the complete activities of over 1.5 million members of the "eToro" social trading community.Comment: 6 Pages + Appendi

Basic investigation of the chemical deactivation of V2O5/WO3-TiO2 SCR catalysts by potassium, calcium, and phosphate

The influence of the combustion products of different lubrication oil additives and impurities in fuel or urea solution on the activity and selectivity of V2O5/WO3-TiO2 catalysts in the selective catalytic reduction (SCR) of nitrogen oxides by ammonia was investigated. Focusing on the deactivation by calcium, phosphate, and potassium, the DeNO x activity followed the order K ≫ Ca >PO4. This trend was investigated on the structural level of the catalyst by means of temperature programmed desorption of ammonia (NH3-TPD) and a DRIFT characterization of the adsorbed ammonia species. The results suggest that the studied elements strongly reduce the acidity of the SCR catalyst in the order K ≫ Ca >PO4 by mainly affecting the Brønsted acidity of the surfac

biogas from municipal solid waste landfills a simplified mathematical model

Abstract Municipal solid waste (MSW) landfills now represent one of the most important issues related to the waste management cycle. Knowledge of biogas production is a key aspect for the proper exploitation of this energy source, even in the post-closure period. In the present study, a simple mathematical model was proposed for the simulation of biogas production. The model is based on first-order biodegradation kinetics and also takes into account the temperature variation in time and depth as well as landfill settlement. The model was applied to an operating landfill located in Sicily, in Italy, and the first results obtained are promising. Indeed, the results showed a good fit between measured and simulated data. Based on these promising results, the model can also be considered a useful tool for landfill operators for a reliable estimate of the duration of the post-closure period

Modularity measure of networks with overlapping communities

In this paper we introduce a non-fuzzy measure which has been designed to rank the partitions of a network's nodes into overlapping communities. Such a measure can be useful for both quantifying clusters detected by various methods and during finding the overlapping community-structure by optimization methods. The theoretical problem referring to the separation of overlapping modules is discussed, and an example for possible applications is given as well

Materials recovery from WEEE: current situation in Sicily.

The potential recovery of materials and energy in one year in Italy and in Sicily was estimated assuming that all WEEEs were gathered through the collection – treatment – disposal system implemented according to the rules in force. The embodied energy (EE) recovery associated to material recovery was also estimated, starting from standard values of EE and from yields declared for each component. Mass fractions composition for some categories of WEEE given by a facility in Catania agree with the national averages. Starting from data given by another facility - located in Siracusa - which processes all the five R categories (R1 to R5), potential mass and energy recovery was estimated for this plant. The results compared with national estimates lead to the conclusion that currently this plant contributes by 6% as mass and by 5% for EE recovery. National figures for potential energy recovery from WEEE shows that 10 670 GWh could be theoretically recovered, that is as much as the energy used for civil needs in Italy by two millions people / yr

Influence of the Height of Municipal Solid Waste Landfill on the Formation of Perched Leachate Zones

Waste settlement as well as consolidation phenomena, which occur inside a landfill for municipal solid waste (MSW), can cause a decrease in waste permeability. This can lead to a reduction in conveyance of the leachate drainage system. It is therefore possible that a so-called perched leachate zone will form. Such a zone is constituted by an area in the body of the landfill where the leachate is temporarily trapped and is unable to infiltrate downward. This phenomenon is influenced by many factors, which include rain infiltration rate, waste moisture and composition, landfill height, and so on. The main aim of the paper is to elucidate the role played by landfill height in the formation of perched leachate zones using a one-dimensional (1D) mathematical model. The model allows for the simulation of the perco- lation fluxes throughout an MSW landfill based on mass-balance equations. The results showed a different response in terms of flow rates throughout the landfill, highlighting the important role of landfill height in the formation of perched leachate zones. Landfill height influences not only the formation of perched leachate zones but also their extension throughout the body of the landfill

Comparison between two MBR pilot plants treating synthetic shipboard slops: the effect of salinity increase on biological performance, biomass activity and fouling tendency

The paper reports the main results of an experimental campaign carried out on two bench scale pilot plants for the treatment of synthetic shipboard slops. In particular, two membrane bioreactors (MBRs) with submerged configuration were analyzed. One MBR pilot plant (namely, Line A) was fed with synthetic shipboard slop and was subjected to a gradual increase of salinity. Conversely, the second MBR pilot plant (namely, Line B) was fed with the same synthetic shipboard slop but without salt addition, therefore operating as a \ue2\u80\u9ccontrol\ue2\u80\u9d unit. Organic carbon, hydrocarbons and ammonium removal, kinetic constants, extracellular polymeric substances (EPSs) production and membranes fouling rates have been assessed. The observed results highlighted a stress effect exerted by salinity on the biological performances, with lower removal efficiencies in the Line A compared to Line B. Significant releases of soluble EPS in Line A promoted an increase of the resistance related to particle deposition into membrane pores (pore fouling tendency), likely due to a worsening of the mixed liquor features. Such a condition enhanced the reduction of the \ue2\u80\u9cpre-filter\ue2\u80\u9d effect of the cake layer

Expression of Semaphorin 3F and Its Receptors in Epithelial Ovarian Cancer, Fallopian Tubes, and Secondary Müllerian Tissues

While semaphorins and their receptors appear to play a role in tumor carcinogenesis, little is known about the role of semaphorin 3F (S3F) in epithelial ovarian cancer (EOC) development. Therefore, we sought to determine the clinical relationship between S3F and its receptors, neuropilin-2 (NP-2) and neuropilin-1 (NP-1) with EOC progression. We analyzed the immunohistological expression of S3F, NP-2, and NP-1 in clinical specimens of normal ovaries (N), benign cystadenomas (Cy), well-differentiated adenocarcinomas (WD), poorly-differentiated adenocarcinomas (PD), inclusion cysts (IC), paraovarian cysts (PC), and fallopian tubes (FT). Tissue sections were evaluated for staining intensity and percentage of immunoreactive epithelia. We found that expression of S3F and NP-2 decreased while NP-1 expression increased with EOC progression. Interestingly, we also found elevated expression of S3F, NP-2, and NP-1 in epithelia of ICs, PCs, and FT. Our findings indicate that loss or deregulation of semaphorin signaling may play an important role in EOC development

Trends Prediction Using Social Diffusion Models

The importance of the ability to predict trends in social media has been growing rapidly in the past few years with the growing dominance of social media in our everyday’s life. Whereas many works focus on the detection of anomalies in networks, there exist little theoretical work on the prediction of the likelihood of anomalous network pattern to globally spread and become “trends”. In this work we present an analytic model for the social diffusion dynamics of spreading network patterns. Our proposed method is based on information diffusion models, and is capable of predicting future trends based on the analysis of past social interactions between the community’s members. We present an analytic lower bound for the probability that emerging trends would successfully spread through the network. We demonstrate our model using two comprehensive social datasets — the Friends and Family experiment that was held in MIT for over a year, where the complete activity of 140 users was analyzed, and a financial dataset containing the complete activities of over 1.5 million members of the eToro social trading community

Finding overlapping communities in networks by label propagation

We propose an algorithm for finding overlapping community structure in very large networks. The algorithm is based on the label propagation technique of Raghavan, Albert, and Kumara, but is able to detect communities that overlap. Like the original algorithm, vertices have labels that propagate between neighbouring vertices so that members of a community reach a consensus on their community membership. Our main contribution is to extend the label and propagation step to include information about more than one community: each vertex can now belong to up to v communities, where v is the parameter of the algorithm. Our algorithm can also handle weighted and bipartite networks. Tests on an independently designed set of benchmarks, and on real networks, show the algorithm to be highly effective in recovering overlapping communities. It is also very fast and can process very large and dense networks in a short time