Development of Auto Scaling Method for 3D Rock Fragmentation Measurement System

Fragmentation Distribution is one of the important aspects of mining operations as it affects productivities on the majority of Mine-to-Mill operations. Nevertheless the significance of fragmentation management, the mining industry has relied on 2D image based fragmentation measurement system which poses many downsides. To overcome the drawbacks of current 2D fragmentation measurement system, 3D Rock Fragmentation Measurement System has been proposed with using 3D photogrammetry technologies. One of the common difficulty of fragmentation measurement system is scaling of the object, which is an essential component to secure the accuracy of particle size distribution. In this study, the actual scales and size information of objects have been obtained by measuring the acceleration when moving between the photographing points and giving the information of the distance obtained from the acceleration. The developed system would be equipped with the 3D Rock Fragmentation Measurement System

Design and Analysis of IPACT-based Bandwidth Allocation for Delay-Guarantee in OFDMA-PON

To guarantee delay performances for timesensitive services in an orthogonal frequency-division multiple access passive optical network (OFDMA-PON), we propose a two-dimension (i.e., subcarriers and time) upstream bandwidth allocation method based on interleaved polling with adaptive cycle time (IPACT). We first analyze its delay performance in terms of cycle time, i.e., the length of a polling cycle. Then, by setting the maximum polling cycle so as to guarantee timely transmissions for time-sensitive services, we identify the requirements, i.e., maximum bandwidth allocation, maximum number of allowed optical network units (ONUs), and optimum number of subcarriers, for upstream bandwidth allocation with delay guarantees. The proposed scheme is evaluated both numerically and via simulation

Application of the Savitzky-Golay Filter to Land Cover Classification Using Temporal MODIS Vegetation Indices

In this study, the Savitzky-Golay filter was applied to smooth observed unnatural variations in the temporal profiles of the Normalized Difference Vegetation Index (NDVI} and the Enhanced Vegetation Index {EVI} time series from the MODerate Resolution Imaging Spectroradiometer (MODIS}. We computed two sets of land cover classifications based 011 the NDVI and EVI time series before and after applying the Savitzky-Golay filter. The resulting classification from the filtered versions of the vegetation indices showed a substantial improvement in accuracy when compared to the classifications from the unfiltered versions. The classification by the EVIsg had the highest K (0.72} for all classes compared to those of the EVI (0.67}, NDVI (0.63}, and NDV/sg (0.62). Therefore, we conclude that the EVIsg is best suited for land cover classification compared to the other data sets in this study

Cholesterol Secosterol Aldehydes Induce Amyloidogenesis and Dysfunction of Wild-Type Tumor Protein p53

SummaryEpidemiologic and clinical evidence points to an increased risk for cancer when coupled with chronic inflammation. However, the molecular mechanisms that underpin this interrelationship remain largely unresolved. Herein we show that the inflammation-derived cholesterol 5,6-secosterol aldehydes, atheronal-A (KA) and -B (ALD), but not the polyunsaturated fatty acid (PUFA)-derived aldehydes 4-hydroxynonenal (HNE) and 4-hydroxyhexenal (HHE), induce misfolding of wild-type p53 into an amyloidogenic form that binds thioflavin T and Congo red dyes but cannot bind to a consensus DNA sequence. Treatment of lung carcinoma cells with KA and ALD leads to a loss of function of extracted p53, as determined by the analysis of extracted nuclear protein and in activation of p21. Our results uncover a plausible chemical link between inflammation and cancer and expand the already pivotal role of p53 dysfunction and cancer risk

Training Spiking Neural Networks Using Lessons From Deep Learning

The brain is the perfect place to look for inspiration to develop more efficient neural networks. The inner workings of our synapses and neurons provide a glimpse at what the future of deep learning might look like. This paper serves as a tutorial and perspective showing how to apply the lessons learnt from several decades of research in deep learning, gradient descent, backpropagation and neuroscience to biologically plausible spiking neural neural networks. We also explore the delicate interplay between encoding data as spikes and the learning process; the challenges and solutions of applying gradient-based learning to spiking neural networks; the subtle link between temporal backpropagation and spike timing dependent plasticity, and how deep learning might move towards biologically plausible online learning. Some ideas are well accepted and commonly used amongst the neuromorphic engineering community, while others are presented or justified for the first time here. A series of companion interactive tutorials complementary to this paper using our Python package, snnTorch, are also made available: https://snntorch.readthedocs.io/en/latest/tutorials/index.htm

Pharmacological and Toxicological Properties of the Potent Oral γ-Secretase Modulator BPN-15606.

Alzheimer's disease (AD) is characterized neuropathologically by an abundance of 1) neuritic plaques, which are primarily composed of a fibrillar 42-amino-acid amyloid-β peptide (Aβ), as well as 2) neurofibrillary tangles composed of aggregates of hyperphosporylated tau. Elevations in the concentrations of the Aβ42 peptide in the brain, as a result of either increased production or decreased clearance, are postulated to initiate and drive the AD pathologic process. We initially introduced a novel class of bridged aromatics referred tγ-secretase modulatoro as γ-secretase modulators that inhibited the production of the Aβ42 peptide and to a lesser degree the Aβ40 peptide while concomitantly increasing the production of the carboxyl-truncated Aβ38 and Aβ37 peptides. These modulators potently lower Aβ42 levels without inhibiting the γ-secretase-mediated proteolysis of Notch or causing accumulation of carboxyl-terminal fragments of APP. In this study, we report a large number of pharmacological studies and early assessment of toxicology characterizing a highly potent γ-secretase modulator (GSM), (S)-N-(1-(4-fluorophenyl)ethyl)-6-(6-methoxy-5-(4-methyl-1H-imidazol-1-yl)pyridin-2-yl)-4-methylpyridazin-3-amine (BPN-15606). BPN-15606 displayed the ability to significantly lower Aβ42 levels in the central nervous system of rats and mice at doses as low as 5-10 mg/kg, significantly reduce Aβ neuritic plaque load in an AD transgenic mouse model, and significantly reduce levels of insoluble Aβ42 and pThr181 tau in a three-dimensional human neural cell culture model. Results from repeat-dose toxicity studies in rats and dose escalation/repeat-dose toxicity studies in nonhuman primates have designated this GSM for 28-day Investigational New Drug-enabling good laboratory practice studies and positioned it as a candidate for human clinical trials

In politics, caricatures can become facts, and that is bad for everyone

The distortion of facts is nothing new to politics and election campaigns. But, with the rise of the internet and 24-hour news cycle, rumors and conspiracy theories can now spread easier than ever through social networks to reach potential voters. Michael Cacciatore and co-authors look at two examples from the 2008 presidential election campaign to better understand how unsubstantiated rumors can become facts in voters’ minds. They find that values, including political ideology and evangelical Christian status, were primarily responsible for propelling misperceptions about President Barack Obama’s faith, while media use played a more important role in driving the misperception that Sarah Palin, and not Saturday Night Live’s Tina Fey, was responsible for the “I can see Russia from my house” quote. The latter finding lends some credibility to the so-called “lamestream media” effect often espoused by prominent Republican figures

Gravitational Wave Spectrum in Inflation with Nonclassical States

The initial quantum state during inflation may evolve to a highly squeezed quantum state due to the amplification of the time-dependent parameter, $\omega_{phys}(k/a)$, which may be the modified dispersion relation in trans-Planckian physics. This squeezed quantum state is a nonclassical state that has no counterpart in the classical theory. We have considered the nonclassical states such as squeezed, squeezed coherent, and squeezed thermal states, and calculated the power spectrum of the gravitational wave perturbation when the mode leaves the horizon.Comment: 21 page