Modulation of sensorimotor rhythms for brain-computer interface using motor imagery with online feedback

This research studies the impact of the imagination of movements and associated feedbacks on the modulation of sensorimotor electroencephalographic (EEG) rhythms, for the online controls of a brain-computer interface (BCI). Nine subjects with no physical or mental impairments were selected. The number of sessions was five: one calibration and four feedback sessions. A computer screen’s cursor movement was controlled in one dimension using EEG-based four-class BCI involving motor imagery tasks of moving the right hand, the left hand, both hands, or both feet. Our findings reveal that the visual feedback applied during motor imagery movement modulates sensorimotor EEG rhythms clearly in the mu and beta bands. The analyses of event-related desynchronization/synchronization (ERD/ERS) suggest significant differences between brain activities in the calibration and feedback sessions; large ERDs during the online feedback sessions compared to that in the calibration session have been observed. The increasing ERDs in the online feedback session were noticed over the mu1 (8–10 Hz) and upper beta (18–24 Hz) rhythms, resulting in the cursor control success rate at 73.3%.     

Multi-step least squares support vector machine modeling approach for forecasting short-term electricity demand with application

Neural Computing and Applications - Electricity demand forecasting plays a crucial role in the operation of electrical power systems because it can provide management decisions related to load...     

Optimization of factors affecting current generation,biofilm formation and rhamnolipid production by electroactive Pseudomonas aeruginosa FA17

Microbial electrochemical cells (MXCs) have been developed as promising devices mainly for energy generation and wastewater treatment. Among the well-defined electroactive bacteria, Pseudomonas aeruginosa has been demonstrated for its electroactivity through mediated electron transfer in MXCs. In addition to phenazine pigments that mediate external electron transfer (EET), biofilm formation and rhamnolipid production are considered important factors affecting electricity generation by P. aeruginosa in MXCs. In the present study, a local electroactive strain of P. aeruginosa was isolated. Bacterial identification was done by Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS), and 16S rDNA as P. aeruginosa FA17. Cyclic voltammetric studies showed the appearance of oxidation peaks which contributes to pyocyanin. Plackett-Burman designs were employed to optimize the composition of artificial wastewater (AWW) media for current generation (represented the mediated electron transfer), biofilm formation and rhamnolipid production by FA17. Ammonium chloride was the significant variable affecting electron transfer of FA17 for current generation in three-electrode set-up by using cyclic voltammetry analyses. While phosphate, Sodium dodecyl sulfate (SDS) and chlorides were the major factors influencing biofilm formation by FA17, phosphate, inoculum size, MgSO₄.7H₂O, pH, and SDS were the significant variables for rhamnolipid production. This study provides knowledge about the electrochemical behavior of mediated electron transfer catalyzed by P. aeruginosa FA17, biofilm formation, and rhamnolipid production in response to medium composition, which leads to the possibility for the future development of power produced by MXCs using the electroactive FA17.     

Capturing the footprints of ground motion in the spatial distribution of rainfall-induced landslides

Bulletin of Engineering Geology and the Environment - The coupled effect of earthquakes and rainfall is rarely investigated in landslide susceptibility assessments although it could be crucial to...     

Computer-supported reflective learning: how apps can foster reflection at work

ABSTRACT

This paper discusses the potential of ICT to support reflective learning for professionals. We aggregated data collected in 20 field studies with 12 different applications, involving a total of 321 participants. The applications addressed individual reflection as well as collaborative reflection. Such a systematic analysis with different applications used across industry sectors and companies is unique in the current literature on computer-supported reflective learning in the workplace. Primarily, we assessed the reaction to reflection applications and their effectiveness regarding learning, behaviour change, and organisational impact. In addition, we investigated differences with respect to work experience. Results show that users had a positive reaction to the apps and perceived their use to be beneficial for their work by using them. In collaborative reflection an inexperienced employee can benefit from the experiences and perspectives of more experienced (co-)workers. In contrast, individual reflection was more profitable for more experienced workers. Notwithstanding the overall positive results, the actual implementation of reflection applications requires careful adaptation to the specific organisational and situational context, as well as introductory and accompanying measures to assure efficient and beneficial usage of the tools.     

On dual-phase-lag magneto-thermo-viscoelasticity theory with memory-dependent derivative

A new mathematical model of generalized magneto-thermo-viscoelasticity theories with memory-dependent derivatives (MDD) of dual-phase-lag heat conduction law is developed. The equations for one-dimensional problems including heat sources are cast into matrix form using the state space and Laplace transform techniques. The resulting formulation is applied to a problem for the whole space with a plane distribution of heat sources. It is also applied to a perfect conducting semi-space problem with a traction-free surface and plane distribution of heat sources located inside the medium. The inversion of the Laplace transforms is carried out using a numerical approach. Numerical results for the temperature, displacement, stress and heat flux distributions as well as the induced magnetic and electric fields are given and illustrated graphically. A comparison is made with the results obtained in the coupled theory. The impacts of the MDD heat transfer parameter and Alfven velocity on a viscoelastic material, for example, poly (methyl methacrylate) (Perspex) are discussed.

     

Bayesian Estimation of Density via Multiple Sequential Inversions of Two-Dimensional Images With Application to Electron Microscopy

We present a new Bayesian methodology to learn the unknown material density of a given sample by inverting its two-dimensional images that are taken with a scanning electron microscope. An image results from a sequence of projections of the convolution of the density function with the unknown microscopy correction function that we also learn from the data; thus, learning of the unknowns demands multiple inversions. We invoke a novel design of experiment, involving imaging at multiple values of the parameter that controls the subsurface depth from which information about the density structure is carried, to result in the image. Real-life material density functions are characterized by high-density contrasts and are highly discontinuous, implying that they exhibit correlation structures that do not vary smoothly. In the absence of training data, modeling such correlation structures of real material density functions is not possible. So we discretize the material sample and treat values of the density function at chosen locations inside it as independent and distribution-free parameters. Resolution of the available image dictates the discretization length of the model; three models pertaining to distinct resolution classes (at micrometer to nanometer scale lengths) are developed. We develop priors on the material density, such that these priors adapt to the sparsity inherent in the density function. The likelihood is defined in terms of the distance between the convolution of the unknown functions and the image data. The posterior probability density of the unknowns given the data is expressed using the developed priors on the density and priors on the microscopy correction function as elicited from the microscopy literature. We achieve posterior samples using an adaptive Metropolis-within-Gibbs inference scheme. The method is applied to learn the material density of a three-dimensional sample of a nano-structure, using real image data. Illustrations on simulated image data of alloy samples are also included.     

Electron Beam Synthesis and Characterization of Poly Vinyl Alcohol/Poly Acrylic Acid Embedded Ni and Ag Nanoparticles

Electron beam irradiation was applied to prepare poly (vinyl alcohol) and poly (acrylic acid) P (PVA/AAc) containing nickel and silver nanoparticles. The prepared P (PVA/AAc)–Ni and P (PVA/AAc)–Ag nanoparticles were characterized by Fourier-transform infrared, UV–Visible spectroscopy, X-ray diffraction (XRD) and transmission electron microscope (TEM). The electrical conductivity and thermal gravimetrical analysis (TGA) have been investigated. Bacterial sensitivity toward nickel and silver nanoparticles was studied. The XRD and TEM confirmed that by increasing the Ni or Ag contents from 10 to 150?mmol in the copolymers, the metal particle size increases from 27.6 to 45.6?nm for Ni and from 14.8 to 37.4?nm for Ag. Meanwhile, the mean size particle increases from 33.02 to 45.05?nm for Ni and from 15.5 to 44.03?nm for Ag. The electrical conductivity of the polymer containing Ag is higher than that of Ni and it increased by increasing the metal content. The TGA studies confirmed that, the thermal stability increase by the introduction of metal into polymeric complex. Bacterial sensitivity to metal nanoparticles was found to vary depending on the microbial species. Disc diffusion studies with P. aeruginosa, E. coli and K. pneumoniae revealed greater effectiveness of the silver nanoparticles compared to the nickel nanoparticles, S. aureus depicted the highest sensitivity to nanoparticles compared to the other strains and was more adversely affected by the nickel nanoparticles.