Electroencephalogram (EEG) Brain Signals to Detect Alcoholism Based on Deep Learning

The detection of alcoholism is of great importance due to its effects on individuals and society. Automatic alcoholism detection system (AADS) based on electroencephalogram (EEG) signals is effective, but the design of a robust AADS is a challenging problem. AADS’ current designs are based on conventional, hand-engineered methods and restricted performance. Driven by the excellent deep learning (DL) success in many recognition tasks, we implement an AAD system based on EEG signals using DL. A DL model requires huge number of learnable parameters and also needs a large dataset of EEG signals for training which is not easy to obtain for the AAD problem. In order to solve this problem, we propose a multi-channel Pyramidal neural convolutional (MP-CNN) network that requires a less number of learnable parameters. Using the deep CNN model, we build an AAD system to detect from EEG signal segments whether the subject is alcoholic or normal. We validate the robustness and effectiveness of proposed AADS using KDD, a benchmark dataset for alcoholism detection problem. In order to find the brain region that contributes significant role in AAD, we investigated the effects of selected 19 EEG channels (SC-19), those from the whole brain (ALL-61), and 05 brain regions, i.e., TEMP, OCCIP, CENT, FRONT, and PERI. The results show that SC-19 contributes significant role in AAD with the accuracy of 100%. The comparison reveals that the state-of-the-art systems are outperformed by the AADS. The proposed AADS will be useful in medical diagnosis research and health care systems.     

A survey on search results diversification techniques

The quantity of information placed on the web has been greater than before and is increasing rapidly day by day. Searching through the huge amount of data and finding the most relevant and useful result set involves searching, ranking, and presenting the results. Most of the users probe into the top few results and neglect the rest. In order to increase user’s satisfaction, the presented result set should not only be relevant to the search topic, but should also present a variety of perspectives, that is, the results should be different from one another. The effectiveness of web search and the satisfaction of users can be enhanced through providing various results of a search query in a certain order of relevance and concern. The technique used to avoid presenting similar, though relevant, results to the user is known as a diversification of search results. This article presents a survey of the approaches used for search result diversification. To this end, this article not only provides a technical survey of existing diversification techniques, but also presents a taxonomy of diversification algorithms with respect to the types of search queries.     

Effect of Particle and Carbide Grain Sizes on a HVOAF WC-Co-Cr Coating for the Future Application on Internal Surfaces: Microstructure and Wear

The use of nanoscale WC grain or finer feedstock particles is a possible method of improving the performance of WC-Co-Cr coatings. Finer powders are being pursued for the development of coating internal surfaces, as less thermal energy is required to melt the finer powder compared to coarse powders, permitting spraying at smaller standoff distances. Three WC-10Co-4Cr coatings, with two different powder particle sizes and two different carbide grain sizes, were sprayed using a high velocity oxy-air fuel (HVOAF) thermal spray system developed by Castolin Eutectic-Monitor Coatings Ltd., UK. Powder and coating microstructures were characterized using XRD and SEM. Fracture toughness and dry sliding wear performance at three loads were investigated using a ball-on-disk tribometer with a WC-Co counterbody. It was found that the finer powder produced the coating with the highest microhardness, but its fracture toughness was reduced due to increased decarburization compared to the other powders. The sprayed nanostructured powder had the lowest microhardness and fracture toughness of all materials tested. Unlubricated sliding wear testing at the lowest load showed the nanostructured coating performed best; however, at the highest load this coating showed the highest specific wear rates with the other two powders performing to a similar, better standard.     

Liaison of phenolic acids and biological activity of escalating cultivars of Daucus carota

The objective of the present study was to analyze and compare the phenolic compounds and their antioxidant capacities of new lines of Dacus carota. The selected cultivars showed high variation in the contents of total phenolics (30.26–65.39 mg/100 g FW) and total ascorbic acid (41.12–58.36 mg/100 g FW). Analysis on RP-HPLC revealed that hydroxycinnamic acids and its derivatives were major phenolic compounds present in D. carota extracts, whereas 5-caffeolquinic acid was a major hydroxycinnamic acid (ranged from 30.26 to 65.39 mg/100 g FW). DCP cultivar showed high total antioxidant capacity (77.69 mg/100 g), 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging capacity (52.36 mg/100 g), superoxide radical scavenging capacity (53.69 mg/100 g), and hydroxyl radical scavenging capacity (51.91 mg/100 g). A linear relationship was found between total phenolic acid contents and antioxidant capacity. Both phenolic compounds and antioxidant capacities varied significantly (ρ < 0.05) among cultivars. DCP cultivar was found to be a rich source of phenolics and ascorbic acid with high antioxidant activity.     

Observer-based leader-following consensus of one-sided Lipschitz multi-agent systems over input saturation and directed graphs

This paper investigates a local observer-based leader-following consensus control of one-sided Lipschitz (OSL) multi-agent systems (MASs) under input saturation. The proposed consensus control scheme has been formulated by using the OSL property, input saturation, directed graphs, estimated states, and quadratic inner-boundedness condition by attaining the regional stability. It is assumed that the graph always includes a (directed) spanning tree with respect to the leader root to develop matrix inequalities for investigating parameters of the proposed observer and consensus protocols. Further, a new observer-based consensus tracking method for MASs with saturation, concerning independent topologies for communicating outputs and estimates over the network, is explored to deal with a more perplexing and realistic situation. In contrast to the traditional methods, the proposed consensus approach considers output feedback and deals with the input saturation for a generalized class of nonlinear systems. The efficiency of the obtained results is illustrated via application to a group of five moving agents in the Cartesian coordinates.     

A systematic mapping of semi-formal and formal methods in requirements engineering of industrial Cyber-Physical systems

Journal of Intelligent Manufacturing - The requirements engineering of Industrial Cyber-Physical Systems is extremely challenging due to large system sizes, component heterogeneity, involvement of...     

Sentic Computing for social media marketing

In a world in which millions of people express their opinions about commercial products in blogs, wikis, fora, chats and social networks, the distillation of knowledge from this huge amount of unstructured information can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product, brand or organization. Opinion mining for product positioning, in fact, is getting a more and more popular research field but the extraction of useful information from social media is not a simple task. In this work we merge AI and Semantic Web techniques to extract, encode and represent this unstructured information. In particular, we use Sentic Computing, a multi-disciplinary approach to opinion mining and sentiment analysis, to semantically and affectively analyze text and encode results in a semantic aware format according to different web ontologies. Eventually we represent this information as an interconnected knowledge base which is browsable through a multi-faceted classification website.     

Effect of Fe-doping on the structural and optical properties of ZnO thin films prepared by spray pyrolysis

Fe-doped ZnO thin films have been prepared by spray pyrolysis on glass substrates and the influence of Fe-doping concentration on the structural and optical properties of the films has been studied.The X-ray diffraction (XRD) analysis shows that Fe doping has a significant effect on crystalline quality,grain size and strain in the thin films.The best crystalline structure is obtained for 3 at%Fe doping as observed from scanning electron microscopy (SEM) and XRD.However,lower or higher Fe-doping degrades the crystalline quality in turn.Moreover,UV spectroscopy demonstrates the influence of Fe-incorporation on visible range transmittance of ZnO where the best transmittance is obtained for 3 at%doping.The results have been illustrated simultaneously focusing previous results obtained from literature.     

Effective participation of wind turbines in frequency control of a two-area power system using coot optimization

In this paper, load frequency control is performed for a two-area power system incorporating a high penetration of renewable energy sources. A droop controller for a type 3 wind turbine is used to extract the stored kinetic energy from the rotating masses during sudden load disturbances. An auxiliary storage controller is applied to achieve effective frequency response. The coot optimization algorithm (COA) is applied to allocate the optimum parameters of the fractional-order proportional integral derivative (FOPID), droop and auxiliary storage controllers. The fitness function is represented by the summation of integral square deviations in tie line power, and Areas 1 and 2 frequency errors. The robustness of the COA is proven by comparing the results with benchmarked optimizers including: atomic orbital search, honey badger algorithm, water cycle algorithm and particle swarm optimization. Performance assessment is confirmed in the following four scenarios: (i) optimization while including PID controllers; (ii) optimization while including FOPID controllers; (iii) validation of COA results under various load disturbances; and (iv) validation of the proposed controllers under varying weather conditions.     

Electrophoretic deposition of graphene oxide on carbon brush as bioanode for microbial fuel cell operated with real wastewater

Microbial fuel cell (MFC) is a promising technology for simultaneous wastewater treatment and energy harvesting. The properties of the anode material play a critical role in the performance of the MFC. In this study, graphene oxide was prepared by a modified hummer's method. A thin layer of graphene oxide was incorporated on the carbon brush using an electrophoretic technique. The deoxygenated graphene oxide formed on the surface of the carbon brush (RGO-CB) was investigated as a bio-anode in MFC operated with real wastewater. The performance of the MFC using the RGO-CB was compared with that using plain carbon brush anode (PCB). Results showed that electrophoretic deposition of graphene oxide on the surface of carbon brush significantly enhanced the performance of the MFC, where the power density increased more than 10 times (from 33 mWm^?2 to 381 mWm^?2). Although the COD removal was nearly similar for the two MFCs, i.e., with PCB and RGO-CB; the columbic efficiency significantly increased in the case of RGO-CB anode. The improved performance in the case of the modified electrode was related to the role of the graphene in improving the electron transfer from the microorganism to the anode surface, as confirmed from the electrochemical impedance spectroscopy measurements.