Scanning electron microscopic observations on micro-organisms in the root nodules of Tribulus terrestris L. (Zygophyllaceae)

Scanning electron microscopic observations were made on the micro-organisms of root nodules of Tribulus terrestris L. The results showed that nodules of T. terrestris contained dual infection consisting of Rhizobium sp. and Newmania karachiensis. Based on these observations, T. terrestris should be grouped with nonlegume Parasponia-type bacterial nodules.     

Détermination du facteur de multiplication M pour un fonctionnement optimal des photodétecteurs à avalanche

The purpose of this paper is relative to the determination of the multiplication factor Mfor an optimum behaviour of the avalanche photodetector. Two types of avalanche photodetector s are compared: a silicon structure n ⁺ πpπp ⁺ Read diode and a GaAlAs heterostructure. Calculating the Noise Equivalent Power (nep)of the whole system, the law S_i(f) =2q I_phoM_x is used, where the parameter x is determined experimentally in both avalanche photodetectors. The exponent x (between 2 and 4) dominates in the nep expression and the ideal nep for the avalanche photodetector system is obtained when x equals 2.     

Three Dimensional Conceptualisation of Hydrogeological Environment to Underpin Groundwater Management in Irrigation Area

Australian irrigated agriculture utilises about 70?% of all water used in the country, 21?% of which is derived from groundwater. Sustainability for irrigated agriculture also depends on keeping the watertables at a safe level below the rootzone to avoid salinisation and reduction in crop yields. There is a vital need to understand groundwater and aquifer systems and their roles in the sustainability of irrigated agriculture in order to manage groundwater properly. This study builds on the previous hydrogeological and groundwater investigations of the Coleambally Irrigation Area (CIA) in New South Wales of Australia. It presents a new approach which systematically characterises regional hydrogeological environment using a three-dimensional (3-D) conceptual framework developed in ArcGIS. The 3-D hydrological conceptualisation of the CIA has integrated disparate sources of data into a coherent knowledge base for a better visualisation of hydrogeological characteristics and a comprehensive analysis of groundwater flow and aquifers. As an application example, the model was used to develop cross-sectional models of the area and to estimate regional-scale net recharge. The results have provided a basis for the numerical modelling and added values to procedures which underpin irrigation system management investment decisions through improving the understanding of hydrogeology underlying the area and creating an action-oriented dialogue among stakeholders.     

Assessment of in vitro antioxidant, antibacterial and immune activation potentials of aqueous and ethanol extracts of Phyllanthus niruri

BACKGROUND: Recently much attention has been paid to biologically active plants because of their low production cost and fewer adverse effects compared with chemical drugs. In the present investigation the bioactivity of Phyllanthus niruri ethanol and aqueous extracts was evaluated in vitro. RESULTS: The ethanol extract of P. niruri showed a high level of flavonoid content (123.9 ± 0.002 mg g^?1), while the aqueous extract showed the highest 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH; IC₅₀6.85 ± 1.80 µmol L^?1) and 2,2′‐azino‐bis(3‐ethylbenzothiazoline‐6‐sulfonic acid) (ABTS; 46.44 ± 0.53 µmol L^?1) free radical scavenging activities with high phenol content (376 ± 0.02 mg g^?1) and elevated levels of ferric reducing antioxidant power (FRAP; 23 883 ± 0.019 mmol g^?1) with excellent antibacterial activity against Staphylococcus aureus (20 mm inhibition zone) and Streptococcus agalactiae (12 mm inhibition zone), respectively, in addition to the best immune activation potential of human peripheral blood mononuclear cells (450.5%). CONCLUSIONS: It is clear from our results that both extracts of P. niruri has excellent bioactivity roles via elevated levels of antibacterial, antioxidant and percentage of peripheral blood mononuclear cell proliferation, which could lead to the development of medications for clinical use. Copyright © 2012 Society of Chemical Industry     

Measuring research performance of Iraqi universities using Scopus data

Scientometrics - This study investigates the research performance of the Iraqi public and private universities using the Scopus citation database. The investigation consists of three stages. The...     

Effect of Iron sulfate as electrolyte additive on plasma electrolytic oxidation of aluminum alloy

This study has been carried out to investigate the incorporation of Iron(II) sulfate as an additive of electrolyte on formed AA1010 aluminum alloy, using plasma electrolytic oxidation method in silicate-based electrolytes containing Iron(II) sulfate. In order to fabricate nanocomposite coating, silicon nitride nanopowder was added to electrolyte. The effects of iron(II) sulfate additive on the voltage-time trend, microstructure, compositions, wear, and corrosion resistances of PEO coatings were investigated. In addition, current density and concentration of additive were studied as parameters that were effective on coating. Results showed that although FeSO₄ enters to the coating structure, but it does not develop a new phase. The corrosion and wear behavior of coated samples with FeSO₄ indicate an improvement as compared to those without additive.     

Plasma Internal Inductance in the Presence of External Resonant Fields in IR-T1 Tokamak

In this paper we presented experimental investigation of effects of local limiter biasing (V_biasing = +200 v, V_biasing = +320 v) on the plasma parameters as plasma current, loop voltage, poloidal beta, plasma pressure, plasma energy, plasma resistance, plasma temperature, plasma displacement, Shafranov parameter and plasma internal inductance in IR-T1 tokamak. For these purposes, array of magnetic probes and also a diamagnetic loop have been used. The results show that applied biased voltage V_biasing = +200 v causes to decrease of about 40 % in plasma internal inductance. The plasma resistance and the plasma displacement have been decreased by V_biasing = +200 v. The main result of the application of V_biasing = +200 v is flatting the plasma parameters profiles. In other words, the addition of biasing voltage V_biasing = +200 v to plasma could be effective for improving the quality of tokamak plasma discharge by creating the steady state plasma. The plasma current, plasma pressure, plasma energy, plasma temperature and shift parameter have increased after the application of limiter biasing with V_biasing = +320 v but they decrease rapidly.     

Selection of optimal denoising-based regularization hyper-parameters for performance improvement in a sensor validation model

Multilayered auto-associative neural architectures have widely been used in empirical sensor modeling. Typically, such empirical sensor models are used in sensor calibration and fault monitoring systems. However, simultaneous optimization of related performance metrics, i.e., auto-sensitivity, cross-sensitivity, and fault-detectability, is not a trivial task. Learning procedures for parametric and other relevant non-parametric empirical models are sensitive to optimization and regularization methods. Therefore, there is a need for active learning strategies that can better exploit the underlying statistical structure among input sensors and are simple to regularize and fine-tune. To this end, we investigated the greedy layer-wise learning strategy and denoising-based regularization procedure for sensor model optimization. We further explored the effects of denoising-based regularization hyper-parameters such as noise-type and noise-level on sensor model performance and suggested optimal settings through rigorous experimentation. A visualization procedure was introduced to obtain insight into the internal semantics of the learned model. These visualizations allowed us to suggest an implicit noise-generating process for efficient regularization in higher-order layers. We found that the greedy-learning procedure improved the overall robustness of the sensor model. To keep experimentation unbiased and immune to noise-related artifacts in real sensors, the sensor data were sampled from simulators of a nuclear steam supply system of a pressurized water reactor and a Tennessee Eastman chemical process. Finally, we compared the performance of an optimally regularized sensor model with auto-associative neural network, auto-associative kernel regression, and fuzzy similarity-based sensor models.