Activities Study of Short-lived Radioisotopes with a Filippov-type Plasma Focus

Plasma focus devices are characterized by short-lived dense and hot plasma “pinch” due to the radial compression and electromagnetic acceleration. Through a short period of time, typically a few tens of nanoseconds, the pinched plasma goes through two phases of compression (thermal) and expansion (non-thermal or beam target) that provide necessary conditions for high nuclear reaction rates. If appropriate gas admixture at a desirable pressure is used, the processes can generate short-lived radioisotopes (SLRs) which the level of activities depends on the design and operational parameters. In this paper, the results of simulated theoretical works of two SLRs such as ¹⁰B (d, n) ¹¹C and ¹⁴N (d, n) ¹⁵O are presented using a Filippov-type plasma focus “Dena” as a breeder with the bank energy ranges from 20 to 90 kJ at the repetition rates from 1 to 10 Hz. The admixture gas pressure of ¹⁰B and ¹⁴ N were taken to be approximately 0.05 of initial working pressure at optimum neutron yield regime. The results obtained are discussed.     

Visual search is postponed during the attentional blink until the system is suitably reconfigured.

J. S. Joseph, M. M. Chun, and K. Nakayama (1997) found that pop-out visual search was impaired as a function of intertarget lag in an attentional blink (AB) paradigm in which the 1st target was a letter and the 2nd target was a search display. In 4 experiments, the present authors tested the implication that search efficiency should be similarly impaired (steeper search slopes at shorter lags). A conventional AB deficit was found, but, contrary to expectations, search slopes were invariant with lag. These results suggest that no search can be carried out during the period of the AB. Instead, the search is postponed until after the 1st target has been processed. The authors conclude that efficient visual search cannot be carried out unless the visual system is configured appropriately for the search task. If the initial configuration is inappropriate, processing of the 2nd target is held in abeyance until the system has been suitably reconfigured. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Drought Mitigation through Long-Term Operation of Reservoirs: Case Study

Dealing with climate variability in a river basin presents many challenges in managing a water resources system. Occurrence of severe and persistent droughts deplete reservoirs storage to critical levels, which may lead to future water supply disaster. This paper illustrates certain benefits of using long-lead streamflow forecasts as well as restriction rules for reservoir operation to help manage the water resources system in the Zayandeh-rud River Basin in Iran. An approach is developed for activating restrictions on allocating water to agricultural demands during a drought and predicting low flow regimes using long-lead forecasts. The long-lead forecasts could utilize valuable hydroclimatic information such as the El-Nino southern Oscillation and northern Atlantic Oscillation to predict seasonal streamflow values. Hedging rules for optimal water supply releases is developed based on the benefit functions of release and carryover storage at each agricultural season. Hedging rules are triggered by different levels of drought indices determined by the predicted water availability at the beginning of each agricultural season. The method is used on an historical data set of hydroclimatic variables of the system to simulate the real time operation of the Zayandeh-rud Reservoir. The utility of the method is demonstrated for operating the Zayandeh-rud Reservoir from the drought mitigation point of view. Furthermore, the proposed model is compared to a stochastic dynamic programming model by investigating different indices such as drought duration, drought severity, drought loss, and reliability of agricultural water demands allocation. The results indicate that the use of the proposed approach can significantly reduce the vulnerability of the system during hydrological droughts and increases the long-term benefits of agricultural water demand allocation.     

Reservoir Inflow Modeling Using Temporal Neural Networks with Forgetting Factor Approach

In this paper, a recursive training procedure with forgetting factor is proposed for on-line calibration of temporal neural networks. The forgetting factor discounts old measurements through an on-line model calibration. The forgetting factor approach enables the recursive algorithm to reduce the effect of the older error data by multiplying the error data by a discounting factor. The proposed procedure is used to calibrate a temporal neural network for reservoir inflow modeling. The mean monthly inflow of the Karoon-III reservoir dam in the south-western part of Iran is used to test the performance of the proposed approach. An autoregressive moving average (ARMA) model is also applied to the same data. The temporal neural network, which is trained with the proposed approach, has shown a significant improvement in the forecast accuracy in comparison with the network trained by the conventional method. It is also demonstrated that the neural network trained with forgetting factor results in better forecasts compared to the statistical ARMA model, which has been calibrated through this approach.     

Petroleum reservoir characterization with the aid of artificial neural networks

We introduce a new application of artificial neural network technology in the characterization of reservoir heterogeneity. Different reservoir properties, such as porosity, permeability and fluid saturation, in highly heterogeneous formations can be predicted with good accuracy using information deduced from readily available geophysical well logs. The methodology by which this is carried out is based on the intelligent and adaptive pattern recognition capabilities of an artificial neural network (three-layer feed forward, back propagation). The need for expensive processes to acquire porosity, permeability and fluid saturation data (such as well testing and extensive coring of the formation) may therefore be greatly reduced. Examples of several neural networks developed during this study are presented.     

Temperature-stable surface-acoustic-wave delay lines on gallium arsenide:second-order effects and sensitivities

Temperature-compensated surface-acoustic-wave delay lines on GaAs have been realised. Temperature coefficient of frequency (TCF) variation against temperature exhibited a 2nd-order coefficient comparable to that of the quartz ST-X. Investigations in sensitivities have shown that the compensating layer thicknesses can be easily controlled to achieve the temperature compensation.     

Accurate estimation of formation permeability by means of petrophysical logs

Permeability can be considered as the one of the main petro-physical parameters that plays an important role in commercial production of reservoir. On the other hand, measuring the permeability is actually a principal challenge for investigators. Inasmuch as, taking core samples from every well and also surveying well-tested data require a large amount of time and capital, using an economical process is more interesting and it is the main cause to utilize electronic logging as a repeatable method. Artificial intelligence-based methods and especially least squares support vector machines (LSSVM) are reliable and accurate models. In the present work, the LSSVM has been trained by the Cuckoo optimization algorithm to predict permeability by means well-logging data including five different types of logs as input data. The correlation coefficient between the model prediction and the relevant real data is found to be about 0.99602 that can be nominated as an accurate yield.     

Synthesis, characterization, and application of chiral ionic liquids and their polymers in micellar electrokinetic chromatography

Two amino acid-derived (leucinol and N-methylpyrrolidinol) chiral ionic liquids are synthesized and characterized in both monomeric and polymeric forms. Leucinol-based chiral cationic surfactant is a room-temperature ionic liquid, and pyrrolidinol-based chiral cationic surfactant melts at 30-35 degrees C to form an ionic liquid (IL). The monomeric and polymeric ILs are thoroughly characterized to determine critical micelle concentration, aggregation number, polarity, optical rotation, and partial specific volume. Herein, we present the first enantioseparation using chiral IL as a pseudostationary phase in capillary electrophoresis. Chiral separation of two acidic analytes, (+/-)-alpha-bromophenylacetic acid and (+/-)-2-(2-chlorophenoxy)propanoic acid (+/-)-(2-PPA) can be achieved with both monomers and polymers of undecenoxycarbonyl-L-pryrrolidinol bromide (L-UCPB) and undecenoxycarbonyl-L-leucinol bromide (L-UCLB) at 25 mM surfactant concentration using phosphate buffer at pH 7.50. The chiral recognition seems to be facilitated by the extent of interaction of the acidic analytes with the cationic headgroup of chiral selectors. Polysodium N-undecenoxycarbonyl-L-leucine sulfate (poly-L-SUCLS) and polysodium N-undecenoxycarbonyl-L-leucinate (poly-L-SUCL) were compared at high and low pH for the enantioseparation of (+/-)-(2-PPA). At pH 7.5, poly-L-SUCLS, poly-L-SUCL, and (+/-)-(2-PPA) are negatively charged resulting in no enantioseparation. However, chiral separation was observed for (+/-)-(2-PPA) using poly-L-SUCLS at low pH (pH 2.00) at which the analyte is neutral. The comparison of chiral separation of anionic and cationic surfactants demonstrates that the electrostatic interaction between the acidic analyte and cationic micelle plays a profound role in enantioseparation.     

Synthesis of TiO2–Ag nanocomposite with sol–gel method and investigation of its antibacterial activity against E. coli

TiO₂–Ag nanocomposite was prepared by the sol–gel method and an azeotropic distillation with benzene was used for dehydration of the gel. Because of gel dehydration by distillation method a nanopowder with a surface area of 230 m²/g was produced which decreased to 80 m²/g after calcination. TEM micrographs and XRD patterns showed that spherical nanosized Ag particles (≈ 10 nm) were deposited among TiO₂ particles. The antibacterial activity of calcined powder at 300 and 500 °C was studied in the presence and in the absence of UV irradiation against Escherichia coli as a model for Gram-negative bacteria. The antibacterial tests confirmed the powder calcined at 300 °C possessed more antibacterial activity than the pure TiO₂, amorphous powder and the powder calcined at 500 °C under UV irradiation. In the absence of UV, the reduction in viable cells was observed only with calcinated powder at 300 °C.