CE/electrospray ionization-MS analysis of underivatized d/l-amino acids and several small neurotransmitters at attomole levels through the use of 18-crown-6-tetracarboxylic acid as a complexation reagent/background electrolyte

A new capillary electrophoresis/mass spectrometry technique is introduced for attomole detection of primary amines (including several neurotransmitters), amino acids, and their d/l enantiomers in one run through the use of a complexation reagent while using only approximately 1 nL of sample. The technique uses underivatized amino acids in conjunction with an underivatized capillary, which significantly reduces cost and analysis time. It was found that when (+)-(18-crown-6)-2,3,11,12-tetracarboxylic acid (18-C-6-TCA, MW 440) was used as the background electrolyte/complexation reagent during the capillary electrophoresis/electrospray ionization-mass spectrometry (CE/ESI-MS) analysis of underivatized amino acids, stable complexes were formed between the amino acids and the 18-C-6-TCA molecules. These complexes, which exhibited high ionization efficiencies, were detectable at attomole levels for most amino acids. The detection limits of the AA/18-C-6-TCA complexes were on the average more than 2 orders of magnitude lower than that of the free amino acids in solution. In addition to lower detection limits under CE/ESI-MS, a solution of 18-C-6-TCA in the concentration range of 5-30 mM provided high separation efficiency for mixtures of l-amino acids as well as mixtures of d/l-amino acids. By using a solution of 18-C-6-TCA as the background electrolyte in conjunction with an underivatized, 130-cm-long, 20-microm-i.d., 150-microm-o.d. fused-silica capillary and by monitoring the m/z range of the amino acid/18-C-6-TCA complexes (m/z 515-700), most of the standard amino acids and many of their enantiomers were separated and detected with high separation efficiency and high sensitivity (nanomolar concentration detection limits) in one run. The solutions of 18-C-6-TCA also worked well as the CE/ESI-MS BGE for low-level detection of several neurotransmitters and some of their d/l enantiomers as well as for the analysis of amino acids at endogenous levels in lysed red blood cells.     

3D Wave Propagation and an Ultra–Low-Frequency Instability in Magnetopause

A model set of equations for the low-frequency electromagnetic perturbations in a magnetized nonuniform plasma is presented. A more convenient and systematic procedure is suggested to treat the fluid equations in order to deduce electrostatic and electromagnetic limits. A general dispersion relation is derived for the waves propagating in 3D under local approximation in nonuniform plasmas, which includes almost all the known modes of cold ion magnetized plasmas in the limit < _i (where _i is the ion cyclotron frequency). Both the limits 1 and O(1) have been discussed briefly. The shear Alfvén waves and electromagnetic ion acoustic waves near ultra low frequency (ULF) range are found unstable in the high plasma of magnetopause. The results are in complete agreement with the satellite observations.     

Parameter identification of deep-bar induction motors using genetic algorithm

In recent years, the use of three-phase deep-bar induction motors in power systems has increased. Proper modeling and precise parameter identification of the model are essential for motors’ operating analysis. In this paper, among the proposed models of deep-bar induction motors, a model based on two-axis theory is discussed and developed to improve precision. A real coded genetic algorithm estimates the parameters of the model. The accuracy and validity of the model and its identified parameters are verified with the help of a 5.5 kW, 380 V, 50 Hz, 1,450 rpm deep-bar induction motor.     

Study of Half-Metallic Ferromagnetism in Be0.75Ti0.25Y (Y = S,Se, and Te) Using Ab Initio Calculations: Potential Candidate for Spintronic Devices

The structural, elastic, electronic, and magnetic properties of Be_0.75Ti_0.25Y (Y = S, Se, and Te) have been investigated to understand their potential applications in spintonic devices. Crystals of BeS, BeSe, and BeTe, individually doped with Ti with a dopant concentration of x = 0.25, have been evaluated by using full-potential linearized augmented plane-wave plus local orbital method within the framework of density functional theory. We employed the Wu–Cohen generalized gradient approximation for optimizing the crystal structure and evaluating elastic properties. In order improve bandgap values and optical parameters, the modified Becke and Johnson (mBJ) potential has been employed. The theoretical investigation of band structure and density of states confirms the half-metallic ferromagnetic nature of these compounds. The elastic constants are calculated by the charpin method which shows that the compounds under consideration are brittle and anisotropic. Moreover, it is noted that tetrahedral crystal field splits the 3d state of Ti into triple degenerate t_2g and double degenerate e_g states. The exchange splitting energies Δ _x (d) and Δ _x (pd) and exchange constants (N ₀ α) and (N ₀ β) are predicted from triple degenerate t_2g states, and negative values of N ₀ β justify that the nature of effective potential is more attractive in spin down case rather than that in the spin up case. We also find the crystal field splitting (ΔE _crystal = E _t2g?E _eg) energy and reduction of the local magnetic moment of Ti from its free space charge value and creation of small local magnetic moments on the non-magnetic Be, S, Se, and Te sites by p–d hybridization.     

Genetic programming based blind image deconvolution for surveillancesystems

Image acquisition, segmentation, object detection and tracking are essential parts of surveillance systems. Usually, image filtering approaches are employed as preprocessing step to reduce the effect of motion or out-of-focus blur problem. In this paper, we propose genetic programming (GP) based blind-image deconvolution filter. A GP based numerical expression is developed for image restoration which optimally combines and exploits dependencies among features of the blurred image. In order to develop such function, first, a set of feature vectors is formed by considering a small neighborhood around each pixel. At second stage, the estimator is trained and developed through GP process that automatically selects and combines the useful feature information under a fitness criterion. The developed function is then applied to estimate the image pixel intensity of the degraded images. The performance of filter function is estimated using various degraded image sequences. Our comparative analysis highlight the effectiveness of GP based proposed filter.     

Adaptive Automated Teller Machines

Nowadays, Automated Teller Machines (ATMs) provide significant online support to bank customers. A limitation of ATM usage is that customers often have to wait in a queue, especially at ATMs installed at busy locations. Also, old people tend to consume more ATM usage time, possibly frustrating customers in the queue. In these situations, ATMs should “adapt” to the behavior of the customers to minimize the usage time. To this end, we apply data mining techniques to an ATM transaction dataset obtained from an international bank based in Kuwait. We pre-process this dataset, and convert it into a specific XML format to mine it through the ProM (process mining) tool. Our results reveal that customers withdraw money most frequently, followed by purchases (through an ATM card) and balance inquiry transactions. Customers re-do these transactions frequently, and also employ them one after the other. We acquire the distributions of the withdrawn amount, based on individual customers, the location (ATM terminal) and time of the withdrawl. Based on these results, we have proposed a set of five adaptive ATM interfaces, which show only frequent transactions and frequently-withdrawn amounts, display the current balance autonomously, and query explicitly for viewing purchase history, or for performing another withdrawl. An online survey on 216 ATM customers reveals that a majority of customers are willing to use these interfaces for minimizing their usage time. Our work has been approved by the banking authority of Pakistan, and we are currently implementing our interfaces for a Pakistani bank.     

Modelling Energy Dissipation Over Stepped-gabion Weirs by Artificial Intelligence

The hydraulics of energy dissipation over stepped-gabion weirs is investigated by carrying out a series of laboratory experiments, building models to explain the experimental data, and testing their robustness by using the data reported by other researchers. The experiments comprise: six different stepped-gabion weirs tested in a horizontal laboratory flume, a wide range of discharge values, two weir slopes (V:H): 1:1 and 1:2, and gabion filling material gravel size (porosity equal to 38 %, 40 % and 42 %). These experimental setups were selected to ensure the development of both the nappe and skimming flow regimes within the measured dataset. The models developed for computing energy dissipation over stepped-gabion weirs comprise: multiple regression equations based on dimensional analysis theory, Artificial Neural Network (ANN) and Gene Expression Programming (GEP). The analysis shows that the measured data capture both flow regimes and the transition in between them and above all, and by using all of the data, it may be possible to identify the range of each regime. Energy dissipation modelled by the ANN formulation is successful and may be recommended for reliable estimates but those by GEP and regression analysis can still serve for rough-and-ready estimates in engineering applications.     

Effect of bank slope on the flow patterns in river intakes

In this work, we studied the dimensions of stream tube in the vertical as well as inclined bank conditions. Data were collected from both a physical model and a 3-D numerical model（SSIIM 2）. Equations for predicting stream tube dimensions were presented and compared with existing formulae. In comparison with vertical bank, it is found that inclining bank causes the bottom stream tube width to be greater than at the surface. The strength of secondary current formed at the entrance of branch channel is reduced. These changes in flow pattern can reduce the amount of sediment delivery into the intake.     

Addendum to ‘(Singular) state models and (singular) LTID systems’

In this note, a construction of state representations of singular linear time-invariant differential systems is described directly in terms of trajectories.