Identifying hazardousness of sewer pipeline gas mixture using classification methods: a comparative study

In this work, we formulated a real-world problem related to sewer pipeline gas detection using the classification-based approaches. The primary goal of this work was to identify the hazardousness of sewer pipeline to offer safe and non-hazardous access to sewer pipeline workers so that the human fatalities, which occurs due to the toxic exposure of sewer gas components, can be avoided. The dataset acquired through laboratory tests, experiments, and various literature sources was organized to design a predictive model that was able to identify/classify hazardous and non-hazardous situation of sewer pipeline. To design such prediction model, several classification algorithms were used and their performances were evaluated and compared, both empirically and statistically, over the collected dataset. In addition, the performances of several ensemble methods were analyzed to understand the extent of improvement offered by these methods. The result of this comprehensive study showed that the instance-based learning algorithm performed better than many other algorithms such as multilayer perceptron, radial basis function network, support vector machine, reduced pruning tree. Similarly, it was observed that multi-scheme ensemble approach enhanced the performance of base predictors

A new multivariate empirical mode decomposition method for improving the performance of SSVEP-based brain–computer interface

Objective: Accurate and efficient detection of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG) is essential for the related brain–computer interface (BCI) applications. Approach: Although the canonical correlation analysis (CCA) has been applied extensively and successfully to SSVEP recognition, the spontaneous EEG activities and artifacts that often occur during data recording can deteriorate the recognition performance. Therefore, it is meaningful to extract a few frequency sub-bands of interest to avoid or reduce the influence of unrelated brain activity and artifacts. This paper presents an improved method to detect the frequency component associated with SSVEP using multivariate empirical mode decomposition (MEMD) and CCA (MEMD-CCA). EEG signals from nine healthy volunteers were recorded to evaluate the performance of the proposed method for SSVEP recognition. Main results: We compared our method with CCA and temporally local multivariate synchronization index (TMSI). The results suggest that the MEMD-CCA achieved significantly higher accuracy in contrast to standard CCA and TMSI. It gave the improvements of 1.34%, 3.11%, 3.33%, 10.45%, 15.78%, 18.45%, 15.00% and 14.22% on average over CCA at time windows from 0.5 s to 5 s and 0.55%, 1.56%, 7.78%, 14.67%, 13.67%, 7.33% and 7.78% over TMSI from 0.75 s to 5 s. The method outperformed the filter-based decomposition (FB), empirical mode decomposition (EMD) and wavelet decomposition (WT) based CCA for SSVEP recognition. Significance: The results demonstrate the ability of our proposed MEMD-CCA to improve the performance of SSVEP-based BCI

Edible bio-based nanostructures: delivery, absorption and potential toxicity

The development of bio-based nanostructures as nanocarriers of bioactive compounds to specific body sites has been presented as a hot topic in food, pharmaceutical and nanotechnology fields. Food and pharmaceutical industries seek to explore the huge potential of these nanostructures, once they can be entirely composed of biocompatible and non-toxic materials. At the same time, they allow the incorporation of lipophilic and hydrophilic bioactive compounds protecting them against degradation, maintaining its active and functional performance. Nevertheless, the physicochemical properties of such structures (e.g., size and charge) could change significantly their behavior in the gastrointestinal (GI) tract. The main challenges in the development of these nanostructures are the proper characterization and understanding of the processes occurring at their surface, when in contact with living systems. This is crucial to understand their delivery and absorption behavior as well as to recognize potential toxicological effects. This review will provide an insight into the recent innovations and challenges in the field of delivery via GI tract using bio-based nanostructures. Also, an overview of the approaches followed to ensure an effective deliver (e.g., avoiding physiological barriers) and to enhance stability and absorptive intestinal uptake of bioactive compounds will be provided. Information about nanostructures potential toxicity and a concise description of the in vitro and in vivo toxicity studies will also be given.Joana T. Martins, Oscar L. Ramos, Ana C. Pinheiro, Ana I. Bourbon, Helder D. Silva and Miguel A. Cerqueira (SFRH/BPD/89992/2012, SFRH/BPD/80766/2011, SFRH/BPD/101181/2014, SFRH/BD/73178/2010, SFRH/BD/81288/2011, and SFRH/BPD/72753/2010, respectively) are the recipients of a fellowship from the Fundacao para a Ciencia e Tecnologia (FCT, POPH-QREN and FSE, Portugal). The authors thank the FCT Strategic Project PEst-OE/EQB/LA0023/2013 and the project "BioInd-Biotechnology and Bioengineering for improved Industrial and Agro-Food processes," REF.NORTE-07-0124-FEDER-000028, co-funded by the Programa Operacional Regional do Norte (ON.2-O Novo Norte), QREN, FEDER. We also thank to the European Commission: BIOCAPS (316265, FP7/REGPOT-2012-2013.1) and Xunta de Galicia: Agrupamento INBIOMED (2012/273) and Grupo con potencial de crecimiento. The support of EU Cost Action FA1001 is gratefully acknowledged