Filarial diseases, including lymphatic filariasis and onchocerciasis, are considered among the most devastating of all tropical diseases, affecting about 145 million people worldwide. Efforts to control and eliminate onchocerciasis are impeded by a lack of effective treatments that target the adult filarial stage. Herein, we describe the discovery of a series of substituted di(pyridin-2-yl)-1,2,4-thiadiazol-5-amines as novel macrofilaricides for the treatment of human filarial infections.

In this paper, we propose using particle swarm optimization (PSO) which can improve weighted k-nearest neighbors (PWKNN) to diagnose the failure of a wind power system. PWKNN adjusts weight to correctly reflect the importance of features and uses the distance judgment strategy to figure out the identical probability of multi-label classification. The PSO optimizes the weight and parameter k of PWKNN. This testing is based on four classified conditions of the 300 W wind generator which include healthy, loss of lubrication in the gearbox, angular misaligned rotor, and bearing fault. Current signals are used to measure the conditions. This testing tends to establish a feature database that makes up or trains classifiers through feature extraction. Not lowering the classification accuracy, the correlation coefficient of feature selection is applied to eliminate irrelevant features and to diminish the runtime of classifiers. A comparison with other traditional classifiers, i.e., backpropagation neural network (BPNN), k-nearest neighbor (k-NN), and radial basis function network (RBFN) shows that PWKNN has a higher classification accuracy. The feature selection can diminish the average features from 16 to 2.8 and can reduce the runtime by 61%. This testing can classify these four conditions accurately without being affected by noise and it can reach an accuracy of 83% in the condition of signal-to-noise ratio (SNR) is 20dB. The results show that the PWKNN approach is capable of diagnosing the failure of a wind power system.

This article simulates the wind generator set which as two fault bearing collar rail destruction and the g ear box oil leak fault. The electric current signal which produced by the g enerator, We use Empirical Mode Decomposition (EMD) as well as Fast Fourier Transform (FFT) obtains the frequency range’s sign al figure and characteristic value. The last step is use a kind o f Artificial Neural Network (ANN) classifies which determination fault signal's type and reason. The ANN purpose of the automatic identifica on wind generator set fault.. Keywords—Wind-driven generator, Fast Fourier Transform, Neural network

A high level of power quality is required to avoid equipment malfunction and a smart grid might be a solution to avoid the problems. The paper proposes simple rules to classify various power quality disturbances (PQDs) through illustrating characteristics of the disturbances based on feature analysis of S-transform (ST) and TT-transform (TT). In the feature analysis, the six types of time-characteristic curves (TCCs) and the five types of frequency-characteristic curves (FCCs) are depicted from the ST and TT contours of the PQD waveforms, in which the each specific characteristic can be obviously revealed by the contours in time and frequency. Finally, a manner with seven rules is generalized from the specific characteristics in this paper and can be simply used to classify PQDs. Since the classified PQDs can be obtained by the proposed manner, the amalgamation of PQDs waveforms with small size data should be useful in smart grid applications. Copyright (C) 2012 Praise Worthy Prize S.r.l. - All rights reserved.

A novel approach of combination of radial basis function neural network (RBFNN) and particle swarm optimization (PSO) is proposed to achieve the maximum power point tracking (MPPT) in this study. The measured data of the small wind generator (250 W), including wind speed, generator speed and output power of wind power generator, are applied to estimate the wind speed and output power by the proposed wind speed ANNwind and power estimation ANNPe-PSO modules, respectively. Using the predicted results by the two modules of Matlab/Simulink, the MPPT point can be obtained by manipulating the generator speeds. The experimental results show that the proposed RBFNN-based approach can increase the maximum output power of the wind power generator even if the wind speed and load varies.

Abstract 3391 Granulocyte colony stimulating factor (GCSF) is the essential cytokine for the regulation of neutrophilic granulocytes. Binding of GCSF to its receptor (GCSFR) triggers receptor dimerization, leading to activation of JAK1 and JAK2, phosphorylation of GCSFR, STAT3, STAT5, and Ras/mitogen-activated protein kinase (MAPK), and results in proliferation and differentiation of granulocytic cells. Recombinant human GCSF (rhGCSF) is used successfully to alleviate chemotherapy-induced neutropenia, neutropenia associated with hematopoietic stem cell transplantation, and severe chronic neutropenia. A small molecule oral GCSFR agonist may offer a safer and more convenient alternative to the current injectable rhGCSF therapy. Whereas a previous effort to identify small-molecule mimetics of GCSF found SB-247464 that selectively activated the murine GCSFR, no small-molecule human GCSF mimetics have been developed. Recently, we have discovered a series of novel non-peptidyl small molecules that selectively activate human GCSFR (hGCSFR) function, and may provide a significant innovation in the treatment of neutropenia. In cells transiently transfected with an hGCSFR expression vector and a STAT3-responsive luciferase reporter, a lead compound, LG7455, activates luciferase expression with an efficacy of 50% relative to rhGCSF, and potency (EC50) of 100 nM. LG7455 also activates luciferase expression in cells transfected with hGCSFR and a STAT5-responsive luciferase reporter (65%, 40 nM EC50). The activity of LG7455 is dependent on the expression of hGCSFR, and LG7455 is not active in luciferase assays when human thrombopoietin receptor (hTPOR) or erythropoietin receptor (hEPOR) is expressed. In UT-7 cells made responsive to GCSF by stable transfection of hGCSFR (UTP-hGCSFR), LG7455 stimulated cell growth and increased the phosphorylation of STAT3 and STAT5. LG7455 did not increase growth of TPO- or EPO-responsive UT-7 cells. In CD34 positive human bone marrow hematopoietic cells (BM-HCs), LG7455, increased the percentage of cells positive for the granulocyte-specific marker CD15 (FUT4). The effect of LG7455 in BM-HCs was additive to the effect of rhGCSF. LG7455 is active in luciferase assays with expressed cynomolgus monkey GCSFR, but not mouse, guinea pig or rabbit GCSFR. Similar to what has been demonstrated for small-molecule human TPOR agonists such as eltrombopag, the activity of LG7455 is dependent on a specific residue in the hGCSFR transmembrane domain. When histidine 627 (His-627) in hGCSFR is changed to asparagine present at a similar location in the mouse GCSFR (Asp-602), unlike rhGCSF, LG7455 is no longer active. LG7455 is active, however, on mouse GCSFR with Asp-602 replaced by His. In radioligand-binding experiments using UTP-hGCSFR cells, LG7455 did not displace [125I]rhGCSF, however binding of [125I]rhGCSF was augmented in a concentration dependent manner consistent with allosteric receptor modulation. These data demonstrate that LG7455 is a novel small-molecule selective hGCSFR agonist that activates the receptor in a manner distinct from GCSF and similar to the mechanism of small-molecule hTPOR agonists. Further optimization of the LG7455 chemical series should provide orally-available molecules to treat neutropenia with improved safety and convenience compared to current injectable rhGCSF.Disclosures: Marschke: Ligand Pharmaceuticals: Employment. Rungta: Ligand Pharmaceuticals: Employment. Slavin: Ligand Pharmaceuticals: Employment. Sanders: Ligand Pharmaceuticals: Employment. Roach: Ligand Pharmaceuticals: Employment. Pickens: Ligand Pharmaceuticals: Employment. Shen: Ligand Pharmaceuticals: Employment. van Oeveren: Ligand Pharmaceuticals: Employment. Hong: Ligand Pharmaceuticals: Employment. Sun: Ligand Pharmaceuticals: Employment. Bissonnette: Ligand Pharmaceuticals: Employment. Syka: Ligand Pharmaceuticals: Employment. Zhi: Ligand Pharmaceuticals: Employment.

This paper proposes an optimal feature selection approach, namely, probabilistic neural network-based feature selection (PFS), for power-quality disturbances classification. The PFS combines a global optimization algorithm with an adaptive probabilistic neural network (APNN) to gradually remove redundant and irrelevant features in noisy environments. To validate the practicability of the features selected by the proposed PFS approach, we employed three common classifiers: multilayer perceptron, k-nearest neighbor and APNN. The results indicate that this PFS approach is capable of efficiently eliminating nonessential features to improve the performance of classifiers, even in environments with noise interference.

This paper proposes the method combining artificial neural network with particle swarm optimization (PSO) to implement the maximum power point tracking (MPPT) by controlling the rotor speed of the wind generator. With the measurements of wind speed, rotor speed of wind generator and output power, the artificial neural network can be trained and the wind speed can be estimated. The proposed control system in this paper provides a manner for searching the maximum output power of wind generator even under the conditions of varying wind speed and load impedance. Keywords—maximum power point tracking, artificial neural network, particle swarm optimization.

This paper proposes the method combining artificial neural network (ANN) with particle swarm optimization (PSO) to implement the maximum power point tracking (MPPT) by controlling the rotor speed of the wind generator. First, the measurements of wind speed, rotor speed of wind power generator and output power of wind power generator are applied to train artificial neural network and to estimate the wind speed. Second, the method mentioned above is applied to estimate and control the optimal rotor speed of the wind turbine so as to output the maximum power. Finally, the result reveals that the control system discussed in this paper extracts the maximum output power of wind generator within the short duration even in the conditions of wind speed and load impedance variation. Keywords—Maximum power point tracking, artificial neural network, particle swarm optimization.

This paper presents an application of particle swarm optimization (PSO) to the grounding grid planning which compares to the application of genetic algorithm (GA). Firstly, based on IEEE Std.80, the cost function of the grounding grid and the constraints of ground potential rise, step voltage and touch voltage are constructed for formulating the optimization problem of grounding grid planning. Secondly, GA and PSO algorithms for obtaining optimal solution of grounding grid are developed. Finally, a case of grounding grid planning is shown the superiority and availability of the PSO algorithm and proposal planning results of grounding grid in cost and computational time. Keywords—Genetic algorithm, particle swarm optimization, grounding grid.