Data Mining Using Surface and Deep Agents Based on Neural Networks

This paper presents an approach to data mining based on an architecture that uses two kinds of neural network-based agents: (i) an instantaneously-trained surface learning agent that quickly adapts to new modes of operation; and, (ii) a deep learning agent that is very accurate within a specific regime of operation. The two agents perform complementary functions that improve the overall performance. The performance of the hybrid architecture has been compared with that of a back propagation network for a variety of classification problems and found to be superior based on the RMS error criterion

Performance and Emission Characteristics of Diesel Engine Fueled with Ethanol-Diesel Blends in Different Altitude Regions

In order to investigate the effects ethanol-diesel blends and altitude on the performance and emissions of diesel engine, the comparative experiments were carried out on the bench of turbo-charged diesel engine fueled with pure diesel (as prototype) and ethanol-diesel blends (E10, E15, E20 and E30) under different atmospheric pressures (81 kPa, 90 kPa and 100 kPa). The experimental results indicate that the equivalent brake-specific fuel consumption (BSFC) of ethanol-diesel blends are better than that of diesel under different atmospheric pressures and that the equivalent BSFC gets great improvement with the rise of atmospheric pressure when the atmospheric pressure is lower than 90 kPa. At 81 kPa, both HC and CO emissions rise greatly with the increasing engine speeds and loads and addition of ethanol, while at 90 kPa and 100 kPa their effects on HC and CO emissions are slightest. The changes of atmospheric pressure and mix proportion of ethanol have no obvious effect on NOx emissions. Smoke emissions decrease obviously with the increasing percentage of ethanol in blends, especially atmospheric pressure below 90 kPa

A Flexible Endoscopic Machining Tool

AbstractFlexible endoscopic tools are considerably applied in industrial image based inspecting operations, but none of them are currently effective enough to carry out machining tasks, such as grinding. If machining and inspection can be done in a single step, significant amount of labor force, money and energy can be saved in industrial repairing and maintenance tasks. This paper proposed a concept design of novel endoscopic machining tool, which aims at quantitatively and precisely removing material from imperfect components in hard-to-reach cavities, such as turbine blades in a jet engine. Prediction models are built to estimate the pose, force and material removal rate (MRR) of a modified PENTAX ES-3801 endoscope. Preliminary experimental results show that in two-dimensional (2D) grinding configuration the MRR average error of 22% has been achieved for 18 samples tested. In the end, concept designs of self-stabilized endoscopic grinding tool are proposed and discussed

Tetra­kis{μ3-2-[(2-hy­droxy­eth­yl)amino]­ethano­lato}tetra­kis­[chloridonickel(II)] methanol solvate

The complex mol­ecule of the title compound, [Ni4(C4H10NO2)4Cl4]·CH3OH, consists of a cubane-like {Ni4O4} core in which each nickel(II) atom is six-coordinated in a distorted octa­hedral geometry by one N and four O atoms of three mono-deprotonated diethano­lamine ligands and by a chloride anion. The mol­ecular conformation is stabilized by intra­molecular O—H⋯Cl bonds. In the crystal structure, complex mol­ecules and methanol solvent mol­ecules are linked into a three-dimensional network by N—H⋯Cl, N—H⋯O and O—H⋯Cl hydrogen-bonding inter­actions

A consensus-based global optimization method for high dimensional machine learning problems

We improve recently introduced consensus-based optimization method, proposed in [R. Pinnau, C. Totzeck, O. Tse and S. Martin, Math. Models Methods Appl. Sci., 27(01):183--204, 2017], which is a gradient-free optimization method for general non-convex functions. We first replace the isotropic geometric Brownian motion by the component-wise one, thus removing the dimensionality dependence of the drift rate, making the method more competitive for high dimensional optimization problems. Secondly, we utilize the random mini-batch ideas to reduce the computational cost of calculating the weighted average which the individual particles tend to relax toward. For its mean-field limit--a nonlinear Fokker-Planck equation--we prove, in both time continuous and semi-discrete settings, that the convergence of the method, which is exponential in time, is guaranteed with parameter constraints {\it independent} of the dimensionality. We also conduct numerical tests to high dimensional problems to check the success rate of the method

The p53 Pathway Controls SOX2-Mediated Reprogramming in the Adult Mouse Spinal Cord

Although the adult mammalian spinal cord lacks intrinsic neurogenic capacity, glial cells can be reprogrammed in vivo to generate neurons after spinal cord injury (SCI). How this reprogramming process is molecularly regulated, however, is not clear. Through a series of in vivo screens, we show here that the p53-dependent pathway constitutes a critical checkpoint for SOX2-mediated reprogramming of resident glial cells in the adult mouse spinal cord. While it has no effect on the reprogramming efficiency, the p53 pathway promotes cell-cycle exit of SOX2-induced adult neuroblasts (iANBs). As such, silencing of either p53 or p21 markedly boosts the overall production of iANBs. A neurotrophic milieu supported by BDNF and NOG can robustly enhance maturation of these iANBs into diverse but predominantly glutamatergic neurons. Together, these findings have uncovered critical molecular and cellular checkpoints that may be manipulated to boost neuron regeneration after SCI

PolyMetformin combines carrier and anticancer activities for in vivo siRNA delivery

Metformin, a widely implemented anti-diabetic drug, exhibits potent anticancer efficacies. Herein a polymeric construction of Metformin, PolyMetformin (PolyMet) is successfully synthesized through conjugation of linear polyethylenimine (PEI) with dicyandiamide. The delocalization of cationic charges in the biguanide groups of PolyMet reduces the toxicity of PEI both in vitro and in vivo. Furthermore, the polycationic properties of PolyMet permits capture of siRNA into a core-membrane structured lipid-polycation-hyaluronic acid (LPH) nanoparticle for systemic gene delivery. Advances herein permit LPH-PolyMet nanoparticles to facilitate VEGF siRNA delivery for VEGF knockdown in a human lung cancer xenograft, leading to enhanced tumour suppressive efficacy. Even in the absence of RNAi, LPH-PolyMet nanoparticles act similarly to Metformin and induce antitumour efficacy through activation of the AMPK and inhibition of the mTOR. In essence, PolyMet successfully combines the intrinsic anticancer efficacy of Metformin with the capacity to carry siRNA to enhance the therapeutic activity of an anticancer gene therapy