Abstract:
Intelligent structures with built-in piezoelectric sensor and actuator that can actively change their physical geometry and/or properties have been known preferable in vibration control. However, it is often arguable to determine if measurement of piezoelectric sensor is strain rate, displacement, or velocity signal. This paper presents a neural sensor design to simulate the sensor dynamics. An artificial neural network with error backpropagation algorithm is developed such that the embedded and attached piezoelectric sensor can faithfully measure the displacement and velocity without any signal conditioning circuitry. Experimental verification shows that the neural sensor is effective to vibration suppression of a smart structure by embedded sensor/actuator and a building structure by surface-attached piezoelectric sensor and active mass damper. 1. Introduction Composite structures with surface-mounted or -embedded piezoelectric materials as sensors and/or actuators have been investigated for they possess mechanical simplicity, efficient electromechanical energy conversion, and ability to integrate within structures. Much attention to date has been on analysis and experiment of active vibration control by using piezoelectric sensors and actuators. Review on using piezoelectric materials to MEMS sensor [1], morphing aircraft [2], and structural repair [3] have been reported. Yang and Chiu [4] were among the first to embedded piezoelectric sensors inside composite-laminated structures. The sensors were found to have stiffening effects [5–8]. Among the applications; however, piezoelectric sensor measurement was considered as displacement signal [9–11], velocity signal [12, 13], or strain rate signal [14, 15]. There seems to be inconsistency on the signal nature, and signal conditioning circuit is often necessary. It is known that effective vibration control requires the system state of displacement and velocity; however, such signals are difficult to acquire as they are often obtained either by accelerometer with hardware integration for velocity or by piezoelectric sensor assuming velocity measurement. Accurate sensor dynamics modeling is required for designing a controller immune to modeling discrepancy. Artificial neural networks with the ability of self-learning, generalization, and robustness have been shown suitable for simulating sensor dynamics by system identification. The concept of neural sensor design is to use the piezoelectric sensor measurement to estimate online both the displacement and velocity at the sensor location. Recent development

Abstract:
We report on the preparation and characterization of single crystal gamma phase NaxCoO2 with 0.25 < x < 0.84 using a non-aqueous electrochemical chronoamperemetry technique. By carefully mapping the overpotential versus x (for x < 0.84), we find six distinct stable phases with Na levels corresponding to x ~ 0.75, 0.71, 0.50, 0.43, 0.33 and 0.25. The composition with x ~0.55 appears to have a critical Na concentration which separates samples with different magnetic behavior as well as different Na ion diffusion mechanisms. Chemical analysis of an aged crystal reveals different Na ion diffusion mechanisms above and below x_c ~ 0.53, where the diffusion process above x_c has a diffusion coefficient about five times larger than that below x_c. The series of crystals were studied with X-ray diffraction, susceptibility, and transport measurements. The crystal with x = 0.5 shows a weak ferromagnetic transition below T=27 K in addition to the usual transitions at T = 51 K and 88 K. The resistivity of the Curie-Weiss metallic Na0.71CoO2 composition has a very low residual resistivity, which attests to the high homogeneity of the crystals prepared by this improved electrochemical method. Our results on the various stable crystal compositions point to the importance of Na ion ordering across the phase diagram.

Abstract:
We report measurements of magnetothermopower and magnetoresistivity as a function of temperature on RuSr2Gd1-xLaxCu2O8 (x = 0, 0.1). The normal-state thermopower shows a dramatic decrease after applying a magnetic field of 5 T, whereas the resistivity shows only a small change after applying the same field. Our results suggest that RuO2 layers are conducting and the magnetic field induced decrease of the overall thermopower is caused by the decrease of partial thermopower decrease associated with the spin entropy decrease of the carriers in the RuO2 layers.

Abstract:
The sodium cobaltate family (NaxCoO2) is unique among transition metal oxides because the Co sits on a triangular lattice and its valence can be tuned over a wide range by varying the Na concentration x. Up to now detailed modeling of the rich phenomenology (which ranges from unconventional superconductivity to enhanced thermopower) has been hampered by the difficulty of controlling pure phases. We discovered that certain Na concentrations are specially stable and are associated with superlattice ordering of the Na clusters. This leads naturally to a picture of co-existence of localized spins and itinerant charge carriers. For x = 0.84 we found a remarkably small Fermi energy of 87 K. Our picture brings coherence to a variety of measurements ranging from NMR to optical to thermal transport. Our results also allow us to take the first step towards modeling the mysterious ``Curie-Weiss'' metal state at x = 0.71. We suggest the local moments may form a quantum spin liquid state and we propose experimental test of our hypothesis.

Abstract:
Hexagonal superlattice formed by sodium multi-vacancy cluster ordering in Na$_{0.77}$CoO$_2$ has been proposed based on synchrotron X-ray Laue diffraction study on electrochemically fine-tuned single crystals. The title compound sits closely to the proposed lower end of the miscibility gap of x ~ 0.77-0.82 phase separated range. The average sodium vacancy cluster size is estimated to be 4.5 Na vacancies per layer within a large superlattice size of sqrt{19}a*sqrt{19}a*3c. The exceptionally large Na vacancy cluster size favors large twinned simple hexagonal superlattice of sqrt{19}a, in competition with the smaller di-, tri- and quadri-vacancy clusters formed superlattices of sqrt{12}a and sqrt{13}a. Competing electronic correlations are revealed by the observed spin glass-like magnetic hysteresis below ~ 3K and the twin, triple and mono domain transformations during thermal cycling between 273-373K.

Abstract:
We make some observations about Rosenberg's Levi-Civita connections on noncommutative tori, noting the non-uniqueness of torsion-free metric-compatible connections without prescribed connection operator for the inner *-derivations, the nontrivial curvature form of the inner *-derivations, and the validity of the Gauss-Bonnet theorem for two classes of non-conformal deformations of the flat metric on the noncommutative two-tori, including the case of non-commuting scalings along the principal directions of a two-torus.

Abstract:
The purpose of this paper is to study covariant Poisson structures on the complex Grassmannian obtained as quotients by coisotropic subgroups of the standard Poisson--Lie SU(n). Properties of Poisson quotients allow to describe Poisson embeddings generalizing those obtained in math.SG/9802082.

This study presents an experiment of improving the performance of spectral stochastic finite element method using high-order elements. This experiment is implemented through a two-dimensional spectral stochastic finite element formulation of an elliptic partial differential equation having stochastic coefficients. Deriving this spectral stochastic finite element formulation couples a two-dimensional deterministic finite element formulation of an elliptic partial differential equation with generalized polynomial chaos expansions of stochastic coefficients. Further inspection of the performance of resulting spectral stochastic finite element formulation with adopting linear and quadratic (9-node or 8-node) quadrilateral elements finds that more accurate standard deviations of unknowns are surprisingly predicted using quadratic quadrilateral elements, especially under high autocorrelation function values of stochastic coefficients. In addition, creating spectral stochastic finite element results using quadratic quadrilateral elements is not unacceptably time-consuming. Therefore, this study concludes that adopting high-order elements can be a lower-cost method to improve the performance of spectral stochastic finite element method.

This study presents a new tool for solving stochastic boundary-value
problems. This tool is created by modify the previous spectral
stochastic meshless local Petrov-Galerkin method using the MLPG5 scheme. This
modified spectral stochastic meshless local Petrov-Galerkin method is
selectively applied to predict the structural failure probability with the
uncertainty in the spatial variability of mechanical properties. Except for the
MLPG5 scheme, deriving the proposed spectral stochastic meshless local
Petrov-Galerkin formulation adopts generalized polynomial chaos expansions of
random mechanical properties. Predicting the structural failure
probability is based on the first-order reliability method. Further comparing
the spectral stochastic finite element-based and meshless local
Petrov-Galerkin-based predicted structural failure probabilities indicates that
the proposed spectral stochastic meshless local Petrov-Galerkin method predicts
the more accurate structural failure probability than the spectral stochastic
finite element method does. In addition, generating spectral stochastic meshless
local Petrov-Galerkin results are considerably time-saving than generating
Monte-Carlo simulation results does. In conclusion, the spectral stochastic
meshless local Petrov-Galerkin method serves as a time-saving tool for solving
stochastic boundary-value problems sufficiently accurately.

Abstract:
AIM: To determine the levels of salivary immunoglobulin classes in Nigerian smokers and non-smokers with periodontitis. METHODS: Sixty-nine individuals were recruited into this study after obtaining informed consent. They were subdivided into three groups that consisted of 20 (aged 46 ± 11 years) cigarette smokers with periodontitis (S+P); 24 (40 ± 12 years) smokers without periodontitis (S-P); and 25 (53 ± 11 years) non-smokers with periodontitis (NS+P). An oral and maxillofacial surgeon used radiographs for periodontal probing for the diagnosis of periodontitis. The smokers included subjects who smoked at least six cigarettes per day and all the periodontitis patients were newly diagnosed. About 5 mL of unstimulated saliva was expectorated by each subject into plain sample bottles. Salivary immunoglobulin levels were estimated using enzyme linked immunosorbent assay. Student’s t test was used to determine significant differences between the means. Values of P < 0.05 were regarded as significant. RESULTS: No significant differences were observed in the mean salivary levels of the immunoglobulin classes (IgG, IgA, IgM and IgE) when S+P was compared with S-P. Mean salivary levels of IgA (520.0 ± 155.1 ng/mL vs 670.0 ± 110 ng/mL, P = 0.000) and IgM (644.5 ± 160.0 ng/mL vs 791.4 ± 43.7 ng/mL, P = 0.000) were significantly lower in the S+P compared with NS+P group. Salivary IgA (570.4 ± 145.6 ng/mL vs 670.0 ± 110 ng/mL, P = 0.008) and IgM (703.1 ± 169.3 ng/mL vs 791.4 ± 43.7 ng/mL, P = 0.012) levels were significantly lower in the S-P compared with NS+P group. Only one (5%) periodontal patient had detectable levels of salivary IgE (0.20 IU/mL). Similarly, only one smoker (4.17%) had detectable levels of salivary IgE (0.04 IU/mL) and two non-smokers (9.52%) had detectable levels of IgE (0.24 IU/mL). CONCLUSION: Our study suggests that reduced salivary IgA and IgM levels in smokers with periodontitis could enhance increased susceptibility to periodontitis.