Coupled nanowire-based hybrid plasmonic nanocavities on thin substrates

We theoretically analyze nanowire-based hybrid plasmonic nanocavities on thin substrates at visible wavelengths. In the presence of thin suspended substrates, the hybrid plasmonic modes, formed by the coupling between a metal nanowire and a dielectric nanowire with optical gain, exhibit negligible substrate-mediated characteristics and overlap better with the gain region. Consequently, the confinement factor of the guided hybrid modes is enhanced by more than 42%. However, the presence of significant mirror loss remains the main challenge to lasing. By adding silver coatings with a sufficient thickness range on the two end facets, we show that the reflectivity is substantially enhanced to above 50%. For a coating thickness of 50 nm and cavity length of about 4 μm, the quality factor is above 100.     

Mammalian exocrine secretions: X. constituents of preorbital secretion of grysbok,Raphicerus melanotis

Using electron impact and chemical ionization mass spectrometry with methane and nitric oxide as reactant gases in conjunction with mass spectral analysis of the dimethyl disulfide derivatives of unsaturated constituents, 51 compounds have been identified in addition to the compounds previously found in the preorbital secretion of the grysbokRaphicerus melanotis. The compounds identified are saturated, monounsaturated, and diunsaturated alcohols and formates, saturated and monounsaturated aldehydes, saturated carboxylic acids,- and-lactones, alkyl methyl sulfides, and-hydroxyalkyl acetates. All of these compounds have unbranched structures. The constituent previously incorrectly identified as (Z)-5-tetradecen-1-ol, was found to consist of the coeluting (Z)-7- and (Z)-8-tetradecenols.     

Interleukin-1 Receptor Antagonist Reduces Neonatal Lipopolysaccharide-Induced Long-Lasting Neurobehavioral Deficits and Dopaminergic Neuronal Injury in Adult Rats

Our previous study showed that a single lipopolysaccharide (LPS) treatment to neonatal rats could induce a long-lasting neuroinflammatory response and dopaminergic system injury late in life. This is evidenced by a sustained activation of microglia and elevated interleukin-1β (IL-1β) levels, as well as reduced tyrosine hydroxylase (TH) expression in the substantia nigra (SN) of P70 rat brain. The object of the current study was to test whether co-administration of IL-1 receptor antagonist (IL-1ra) protects against LPS-induced neurological dysfunction later in life. LPS (1 mg/kg) with or without IL-1ra (0.1 mg/kg), or sterile saline was injected intracerebrally into postnatal day 5 (P5) Sprague-Dawley male rat pups. Motor behavioral tests were carried out from P7 to P70 with subsequent examination of brain injury. Our results showed that neonatal administration of IL-1ra significantly attenuated LPS-induced motor behavioral deficits, loss of TH immunoreactive neurons, as well as microglia activation in the SN of P70 rats. These data suggest that IL-1β may play a pivotal role in mediating a chronic neuroinflammation status by a single LPS exposure in early postnatal life, and blockading IL-1β might be a novel approach to protect the dopaminergic system against perinatal infection/inflammation exposure.     

Analysis of weakly bonded oxygen in HfO<Subscript>2</Subscript>/SiO<Subscript>2</Subscript>/Si stacks by using HRBS and ARXPS

We have used transmission electron microscopy, high-resolution Rutherford backscattering spectrometry (HRBS), and angle-resolved X-ray photoelectron spectroscopy (ARXPS) to investigate the interfacial oxidized states of hafnium oxide/silicon oxide/Si gate oxide stacks. The atomic concentrations and profiles of HRBS analysis are similar before and after annealing; however, ARXPS shows a clear difference in bond status. These results imply that weakly bonded oxygen atoms existed in the stacks alongside the suboxides. In the as-deposited layers, dioxides are found at the interfaces and suboxides in the layers whereas after annealing suboxides are found at the interfaces and dioxides are found in the layers because of redistribution of bonds during annealing. The combination of HRBS and ARXPS analyses indicated that the main oxidized states transformed from the suboxides to the dioxides with no obvious quantitative difference in the content of oxygen atoms, suggesting that reactions of the weakly bonded oxygen atoms occurred with the suboxides within the layers.     

A novel hybrid artificial intelligent approach based on neural fuzzy inference model and particle swarm optimization for horizontal displacement modeling of hydropower dam

Horizontal displacement of hydropower dams is a typical nonlinear time-varying behavior that is difficult to forecast with high accuracy. This paper proposes a novel hybrid artificial intelligent approach, namely swarm optimized neural fuzzy inference system (SONFIS), for modeling and forecasting of the horizontal displacement of hydropower dams. In the proposed model, neural fuzzy inference system is used to create a regression model whereas Particle swarm optimization is employed to search the best parameters for the model. In this work, time series monitoring data (horizontal displacement, air temperature, upstream reservoir water level, and dam aging) measured for 11 years (1999–2010) of the Hoa Binh hydropower dam were selected as a case study. The data were then split into a ratio of 70:30 for developing and validating the hybrid model. The performance of the resulting model was assessed using RMSE, MAE, and R ². Experimental results show that the proposed SONFIS model performed well on both the training and validation datasets. The results were then compared with those derived from current state-of-the-art benchmark methods using the same data, such as support vector regression, multilayer perceptron neural networks, Gaussian processes, and Random forests. In addition, results from a Different evolution-based neural fuzzy model are included. Since the performance of the SONFIS model outperforms these benchmark models with the monitoring data at hand, the proposed model, therefore, is a promising tool for modeling horizontal displacement of hydropower dams.

     

Prediction of ultimate bearing capacity through various novel evolutionary and neural network models

Engineering with Computers - In the current study, various evolutionary artificial intelligence and machine learning models namely, optimized artificial neural network (ANN), genetic algorithm...     

Deep Appearance Models: A Deep Boltzmann Machine Approach for Face Modeling

International Journal of Computer Vision - The “interpretation through synthesis” approach to analyze face images, particularly Active Appearance Models (AAMs) method, has become one of...