Influence of deposited amine-functionalized Si-MCM-41 in polyacrylonitrile electrospun membranes applied for separation of water in oil emulsions

Among several oil/water emulsion separation technologies, the utilization of nanoparticle-decorated membranes with diverse functionalities has received considerable attention in recent years, particularly if the antifouling capacity can be improved. In this article, we propose a new membrane based on surface-hydrolyzed polyacrylonitrile electrospun membranes and/or decorated with amine-functionalized Si-MCM-41 nanoparticles to be used as oil/water emulsion separation treatment and to determine their antifouling ability. X-Ray photoelectron spectrometry, attenuated total reflectance Fourier transform infrared spectroscopy, and toluidine blue O assay, scanning electron microscopy, contact angle measurements for oil under water and thermogravimetry were used for characterizing the membranes and an assay of permeability was developed to quantify the diffusion of oil molecules across the electrospun membrane. The electrospun and/or decorated membranes showed an underwater oleophobic wettability, which can separate oil-in-water emulsions with 87% separation efficiency, results of fouling experiments, evaluated in terms of rejection and flux recovery ratio, exhibited good antifouling ability, but the membrane decoration process did not lead to superior outcomes compared with undecorated membranes.     

SONIC: streaming overlapping community detection

A community within a graph can be broadly defined as a set of vertices that exhibit high cohesiveness (relatively high number of edges within the set) and low conductance (relatively low number of edges leaving the set). Community detection is a fundamental graph processing analytic that can be applied to several application domains, including social networks. In this context, communities are often overlapping, as a person can be involved in more than one community (e.g., friends, and family); and evolving, since the structure of the network changes. We address the problem of streaming overlapping community detection, where the goal is to maintain communities in the presence of streaming updates. This way, the communities can be updated more efficiently. To this end, we introduce SONIC—a find-and-merge type of community detection algorithm that can efficiently handle streaming updates. SONIC first detects when graph updates yield significant community changes. Upon the detection, it updates the communities via an incremental merge procedure. The SONIC algorithm incorporates two additional techniques to speed-up the incremental merge; min-hashing and inverted indexes. Results show that SONIC can provide high quality overlapping communities, while handling streaming updates several orders of magnitude faster than the alternatives performing from-scratch computation.     

A novel methodology to predict urban traffic congestion with ensemble learning

Urban traffic congestion prediction is a very hot topic due to the environmental and economical impacts that currently implies. In this sense, to be able to predict bottlenecks and to provide alternatives to the circulation of vehicles becomes an essential task for traffic management. A novel methodology, based on ensembles of machine learning algorithms, is proposed to predict traffic congestion in this paper. In particular, a set of seven algorithms of machine learning has been selected to prove their effectiveness in the traffic congestion prediction. Since all the seven algorithms are able to address supervised classification, the methodology has been developed to be used as a binary classification problem. Thus, collected data from sensors located at the Spanish city of Seville are analyzed and models reaching up to 83 % are generated.     

Image blur and energy broadening effects in XPEEM

We report image blurring and energy broadening effects in energy-filtered XPEEM when illuminating the specimen with soft X-rays at high flux densities. With a flux of 2×10¹³ photons/s, the lateral resolution in XPEEM imaging with either core level or secondary electrons is degraded to more than 50 nm. Fermi level broadening up to several hundred meV and spectral shift to higher kinetic energies are also systematically observed. Simple considerations suggest that these artifacts result from Boersch and Loeffler effects, and that the electron-electron interactions are strongest in the initial part of the microscope optical path. Implications for aberration corrected instruments are discussed.     

Managing Citizen Profiles in the Domain of e-Government: The cPortfolio Project

The provision of support for holistic operations in the scope of a horizontal digital administration requires the fulfillment of a number of previous steps that guarantee the viability of services in the domain. One of these tasks is the proper management of documents. Documents are a key element of any democratic administration and their digital management is a clear pre-requisite for the arrival of digital government. This article tackles how this service can be provided using the support of semantics as a technological cornerstone. The implementation of such a tool is made through the so-called cPortfolio. This platform is deeply discussed and details about its design and implementation are provided. This system is designed to manage both the personal information from the citizen and the documents they possess. Tests on the prototype showed interesting features regarding the simplicity of use and the interoperability support provided to third party agents.     

A multi-start tabu search method for a single-machine scheduling problem with periodic maintenance and sequence-dependent set-up times

In this paper we study a problem of sequencing jobs in a machine with programmed preventive maintenance and sequence-dependent set-up times. The problem combines two NP-hard problems, so we propose a heuristic method for solving it, which hybridizes multi-start strategies with Tabu Search. We compare our method with the only published metaheuristic algorithm for this problem on a set of 420 instances. The comparison favors the method developed in this work, showing that is able to find high quality solutions in very short computational times.     

A multiobjective approach based on artificial bee colony for the static routing and wavelength assignment problem

Nowadays, the most promising technology for designing optical networks is the wavelength division multiplexing. This technique divides the huge bandwidth of an optical fiber link into different wavelengths, providing different available channels per link. However, a problem comes up when it is necessary to interconnect a set of traffic demands. This problem is known as routing and wavelength assignment problem, and due to its complexity (NP-hard problem) it is very suitable for being solved using evolutionary computation. The selected heuristics is the artificial bee colony algorithm, an heuristics based on the behavior of honeybee foraging for nectar. Therefore, we have applied multiobjective optimization to solve the static routing and wavelength assignment problem, and adapted this algorithm to the multiobjective context. New results have been obtained that significantly improve those published in previous researches.     

A document is known by the company it keeps: neighborhood consensus for short text categorization

During the last decades the Web has become the greatest repository of digital information. In order to organize all this information, several text categorization methods have been developed, achieving accurate results in most cases and in very different domains. Due to the recent usage of Internet as communication media, short texts such as news, tweets, blogs, and product reviews are more common every day. In this context, there are two main challenges; on the one hand, the length of these documents is short, and therefore, the word frequencies are not informative enough, making text categorization even more difficult than usual. On the other hand, topics are changing constantly at a fast rate, causing the lack of adequate amounts of training data. In order to deal with these two problems we consider a text classification method that is supported on the idea that similar documents may belong to the same category. Mainly, we propose a neighborhood consensus classification method that classifies documents by considering their own information as well as information about the category assigned to other similar documents from the same target collection. In particular, the short texts we used in our evaluation are news titles with an average of 8 words. Experimental results are encouraging; they indicate that leveraging information from similar documents helped to improve classification accuracy and that the proposed method is especially useful when labeled training resources are limited.