Spectral estimation of nonstationary EEG using particle filtering with application to event-related desynchronization (ERD)

This paper proposes non-Gaussian models for parametric spectral estimation with application to event-related desynchronization (ERD) estimation of nonstationary EEG. Existing approaches for time-varying spectral estimation use time-varying autoregressive (TVAR) state-space models with Gaussian state noise. The parameter estimation is solved by a conventional Kalman filtering. This study uses non-Gaussian state noise to model autoregressive (AR) parameter variation with estimation by a Monte Carlo particle filter (PF). Use of non-Gaussian noise such as heavy-tailed distribution is motivated by its ability to track abrupt and smooth AR parameter changes, which are inadequately modeled by Gaussian models. Thus, more accurate spectral estimates and better ERD tracking can be obtained. This study further proposes a non-Gaussian state space formulation of time-varying autoregressive moving average (TVARMA) models to improve the spectral estimation. Simulation on TVAR process with abrupt parameter variation shows superior tracking performance of non-Gaussian models. Evaluation on motor-imagery EEG data shows that the non-Gaussian models provide more accurate detection of abrupt changes in alpha rhythm ERD. Among the proposed non-Gaussian models, TVARMA shows better spectral representations while maintaining reasonable good ERD tracking performance.     

Learning Automata-Based Algorithms for Solving the Target Coverage Problem in Directional Sensor Networks

Recently, directional sensor networks have received a great deal of attention due to their wide range of applications in different fields. A unique characteristic of directional sensors is their limitation in both sensing angle and battery power, which highlights the significance of covering all the targets and, at the same time, extending the network lifetime. It is known as the target coverage problem that has been proved as an NP-complete problem. In this paper, we propose four learning automata-based algorithms to solve this problem. Additionally, several pruning rules are designed to improve the performance of these algorithms. To evaluate the performance of the proposed algorithms, several experiments were carried out. The theoretical maximum was used as a baseline to which the results of all the proposed algorithms are compared. The obtained results showed that the proposed algorithms could solve efficiently the target coverage problem.     

Solid state photoelectrochemical cells utilising graphite thin films counter electrode

This paper reports the use of graphite thin films as a counter electrode of a solid state photoelectrochemical cells of ITO/TiO₂/PVC-LiClO₄/graphite. The photoelectrochemical cells material was a screen-printed layer of titanium dioxide onto an ITO-covered glass substrate which was used as a working electrode of the device. The solid electrolyte used was PVC-LiClO₄ that was prepared by solution casting technique. The graphite films which serve as a counter electrode were prepared onto glass substrate by electron beam evaporation technique at substrate temperatures variation of 25, 50, 100, 150 and 200 °C. The dependence of sheet resistance and surface morphology of the graphite films on substrate temperature were studied. The films deposited at 25 °C shows the smoothest surface morphology and the smallest grain size. Bigger grain size, rougher surface morphology of graphite film counter electrode. The current-voltage characteristics of four devices utilising the graphite counter electrode with different substrate temperature in dark as well as under illumination of 100 mWcm⁻² light from a tungsten halogen lamp were recorded at room temperature and at 50 °C, respectively. It was found that the photovoltaic parameters of the device such as short-circuit current density, J_sc and open-circuit voltage, V_oc increases with the decreasing average grain size of the graphite counter electrode. The device whose graphite film counter electrode was deposited onto the glass substrate at 25 °C gave the highest J_sc of 0.32 μA/cm² and V_oc of 117 mV, respectively.     

Poly(Methyl Methacrylate) as a matrix for immobilization of lipase

Poly(methyl methacrylate) (PMMA) was found to be suitable for the immobilization of lipase fromCandida rugosa. The best result based on hydrolytic activity was obtained by adsorption of the purified unbuffered enzyme solution onto PMMA beads without any modification of the beads. Prolonged exposure of the protein to the beads increased its adsorption but the expressed activity decreased after 1 h of exposure. The magnitude of the immobilized activity also varied with the size of the beads. Immobilization of the lipase shifted its optimal reaction temperature from 37 to 45°C. The immobilized enzyme is also more stable than the free enzyme in solution. The operational half-life of the immobilized lipase packed in a column and assayed in a closed system is 40 d.     

Application of Artificial Neural Network for Yield Prediction of Lipase-Catalyzed Synthesis of Dioctyl Adipate

In this study, an artificial neural network (ANN) trained by backpropagation algorithm, Levenberg–Marquadart, was applied to predict the yield of enzymatic synthesis of dioctyl adipate. Immobilized Candida antarctica lipase B was used as a biocatalyst for the reaction. Temperature, time, amount of enzyme, and substrate molar ratio were the four input variables. After evaluating various ANN configurations, the best network was composed of seven hidden nodes using a hyperbolic tangent sigmoid transfer function. The correlation coefficient (R ²) and mean absolute error (MAE) values between the actual and predicted responses were determined as 0.9998 and 0.0966 for training set and 0.9241 and 1.9439 for validating dataset. A simulation test with a testing dataset showed that the MAE was low and R ² was close to 1. These results imply the good generalization of the developed model and its capability to predict the reaction yield. Comparison of the performance of radial basis network with the developed models showed that radial basis function was more accurate but its performance was poor when tested with unseen data. In further part of the study, the feedforward backpropagation model was used for prediction of the ester yield within the given range of the main parameters.     

Reductive Alkylation Causes the Formation of a Molten Globule-Like Intermediate Structure in <Emphasis Type="Italic">Geobacillus zalihae</Emphasis> Strain T1 Thermostable Lipase

A thermostable lipase from Geobacillus zalihae strain T1 was chemically modified using propionaldehyde via reductive alkylation. The targeted alkylation sites were lysines, in which T1 lipase possessed 11 residues. Far-UV circular dichroism (CD) spectra of both native and alkylated enzyme showed a similar broad minimum between 208 and 222 nm, thus suggesting a substantial amount of secondary structures in modified enzyme, as compared with the corresponding native enzyme. The hydrolytic activity of the modified enzymes dropped drastically by nearly 15-fold upon chemical modification, despite both the native and modified form showed distinctive α-helical bands at 208 and 222 nm in CD spectra, leading us to the hypothesis of formation of a molten globule (MG)-like structure. As cooperative unfolding transitions were observed, the modified lipase was distinguished from the native state, in which the former possessed a denaturation temperature (T _m) in lower temperature range at 61 °C while the latter at 68 °C. This was further supported by 8-anilino-1-naphthalenesulfonic acid (ANS) probed fluorescence which indicated higher exposure of hydrophobic residues, consequential of chemical modification. Based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis, a small number of lysine residues were confirmed to be alkylated.     

Effect of Ge4+ and Mg2+ doping on superconductivity,fluctuation induced conductivity and interplanar coupling of TlSr2CaCu2O7−δ superconductors

Substitution of Ge⁴⁺ in place of Cu in Tl_0.85Cr_0.15Sr₂CaCu_2?xGe_xO_7?δ (x = 0–0.6) showed initial increase in zero critical temperature value, T_{c zero} from 98 K (x = 0) to 100 K (x = 0.1) and in the range of 85–86 K for x = 0.2–0.3. The slow decrease in T_{c zero} is unexpected as tetravalent Ge⁴⁺ substitution is expected to strongly reduce hole concentration in the samples and suppress T_{c zero}. Excess conductivity analyses of resistance versus temperature data based on Asmalazov–Larkin (AL) theory revealed that the substitution induced 2D-to-3D transition of fluctuation induced conductivity with the highest transition temperature, T_2D–3D observed at x = 0.1. FTIR spectroscopy analysis indicates Ge⁴⁺ substitution cause reduction in CuO₂/GeO₂ interplanar distance while our calculation based on Lawrence–Doniach model revealed highest superconducting coherence length, ξ_c(0) and interplanar coupling, J at x = 0.3. On the other hand, substitution of divalent Mg²⁺ for Ca²⁺ in (Tl_0.5Pb_0.5)(Sr_1.8Yb_0.2)(Ca_1?yMg_y)Cu₂O₇ (y = 0–1.0), which is not expected to directly vary hole concentration, surprisingly caused T_{c zero} to increase from 89.6 K (y = 0) to an optimum value of 95.9 K (y = 0.6) before decreasing with further increase in y. Excess conductivity analyses showed 2D-to-3D transition of fluctuation induced conductivity for all samples where the highest T_2D–3D was at y = 0.4. Similar calculation revealed highest values of ξ_c(0) and J also at y = 0.4. FTIR analysis of the samples indicates inequivalent Cu(1)O(2)Pb/Tl lengths and possible tilting of CuO₂ plane as a result of Mg²⁺ substitution. The increased ξ_c(0) and J as a result of the Ge⁴⁺ and Mg²⁺ substitutions are suggested to contributed to sustenance of superconductivity above 80 K in the samples.     

Polymer electrolyte for photoelectrochemical cell and dye-sensitized solar cell: a brief review

This paper reviews the use of solid polymeric electrolyte (SPE) and gel polymeric electrolyte (GPE) in photoelectrochemical cell (PEC) and dye-sensitized solar cell (DSSC). The structure of PEC and its working principle are presented. The various types of polymer electrolytes utilized in PEC and DSSC have been highlighted in this review. It also highlights the comparison of performance of PEC and DSSC utilizing those polymer electrolytes. This review is completed with the list of other SPEs that potentially be tested in DSSC.     

Neighbourhood preserving discriminant embedding in face recognition

In this paper, we present an effective technique on discriminative feature extraction for face recognition. The proposed technique incorporates Graph Embedding and the Fisher’s criterion where we call it as Neighbourhood Preserving Discriminant Embedding (NPDE). Utilizing the Graph Embedding criterion, the underlying nonlinear face data structure is revealed as representative and discriminative features for analysis. We employ Neighbourhood Preserving Embedding (NPE) for the purpose. NPE takes into account the restriction that neighbouring points in the high-dimensional space must remain within the same neighbourhood in the low dimension space and be located in a similar relative spatial situation (without changing the local structure of the nearest neighbours of each data point). Furthermore, by taking the advantage of the Fisher’s criterion, the discriminating power of NPDE is further boosted. Based on this intuition, NPDE obtains better discriminative capability and experimentally verified in ORL, PIE and FRGC.     

Effect of Experimental Variables on the Combustion Characteristics of Water-in-Diesel Emulsion Fuels

Water-in-diesel (W/D) emulsion fuels were prepared through an ultrasonic processor by using high energy emulsification method. Accordingly, the physical and chemical properties were analyzed. A decrease in viscosity was found in the emulsion fuel in contrast to the neat diesel which signifies the enhanced fluidity of the fuel. The emulsion fuel was then used to carry combustion tests in an internal combustion engine. A decrease in exhaust temperature was observed when a high surfactant to water ratio was used, which lead to minimal heat loss. As water is emulsified with diesel, effectiveness of combustion is improved rather than neat diesel fuel. It was also explored that the addition of water-in-diesel is influential in terms of reduction in exhaust gas emission such as carbon dioxide, carbon monoxide, ammonia from the internal combustion engine. Therefore, this type of emulsion fuel would be a useful contribution in the fuel economy, but also in making it environmentally friendly since diesel fuel is now considered one of the leading fuels causing ecological contamination.