Application of Deep Learning Long Short-Term Memory in Energy Demand Forecasting

The smart metering infrastructure has changed how electricity is measured in both residential and industrial application. The large amount of data collected by smart meter per day provides a huge potential for analytics to support the operation of a smart grid, an example of which is energy demand forecasting. Short term energy forecasting can be used by utilities to assess if any forecasted peak energy demand would have an adverse effect on the power system transmission and distribution infrastructure. It can also help in load scheduling and demand side management. Many techniques have been proposed to forecast time series including Support Vector Machine, Artificial Neural Network and Deep Learning. In this work we use Long Short Term Memory architecture to forecast 3-day ahead energy demand across each month in the year. The results show that 3-day ahead demand can be accurately forecasted with a Mean Absolute Percentage Error of 3.15%. In addition to that, the paper proposes way to quantify the time as a feature to be used in the training phase which is shown to affect the network performance

Nitrogen-Functionalized Graphene Nanoflakes (GNFs:N): Tunable Photoluminescence and Electronic Structures

This study investigates the strong photoluminescence (PL) and X-ray excited optical luminescence observed in nitrogen-functionalized 2D graphene nanoflakes (GNFs:N), which arise from the significantly enhanced density of states in the region of {\pi} states and the gap between {\pi} and {\pi}* states. The increase in the number of the sp2 clusters in the form of pyridine-like N-C, graphite-N-like, and the C=O bonding and the resonant energy transfer from the N and O atoms to the sp2 clusters were found to be responsible for the blue shift and the enhancement of the main PL emission feature. The enhanced PL is strongly related to the induced changes of the electronic structures and bonding properties, which were revealed by the X-ray absorption near-edge structure, X-ray emission spectroscopy, and resonance inelastic X-ray scattering. The study demonstrates that PL emission can be tailored through appropriate tuning of the nitrogen and oxygen contents in GNFs and pave the way for new optoelectronic devices.Comment: 8 pages, 6 figures (including toc figure

Cross Tropopause Transport of Water by Mid-Latitude Deep Convective Storms: A Review

Recent observational and numerical modeling studies of the mechanisms which transport moisture to the stratosphere by deep convective storms at mid-latitudes are reviewed. Observational evidence of the cross-tropopause transport of moisture by thunderstorms includes satellite, aircraft and ground-based data. The primary satellite evidence is taken from both conventional satellite of thunderstorm images and CloudSat vertical cloud cross-section images. The conventional satellite images show cirrus plumes above the anvil tops of some of the convective storms where the anvils are already at the tropopause level. The CloudSat image shows an indication of penetration of cirrus plume into the stratosphere. The aircraft observations consist of earlier observations of the ¡§jumping cirrus¡¨ phenomenon reported by Fujita and recent detection of ice particles in the stratospheric air associated with deep convective storms. The ground-based observations are video camera records of the jumping cirrus phenomenon occurring at the top of thunderstorm cells. Numerical model studies of the penetrative deep convective storms were performed utilizing a three-dimensional cloud dynamical model to simulate a typical severe storm which occurred in the US Midwest region on 2 August 1981. Model results indicate two physical mechanisms that cause water to be injected into the stratosphere from the storm: (1) the jumping cirrus mechanism which is caused by the gravity wave breaking at the cloud top, and (2) an instability caused by turbulent mixing in the outer shell of the overshooting dome. Implications of the penetrative convection on global processes and a brief future outlook are discussed

Superconductivity and Pseudogap in Quasi-Two-Dimensional Metals around the Antiferromagnetic Quantum Critical Point

Spin fluctuations (SF) and SF-mediated superconductivity (SC) in quasi-two-dimensional metals around the antiferrromagnetic (AF) quantum critical point (QCP) are investigated by using the self-consistent renormalization theory for SF and the strong coupling theory for SC. We introduce a parameter y0 as a measure for the distance from the AFQCP which is approximately proportional to (x-xc), x being the electron (e) or hole (h) doping concentration to the half-filled band and xc being the value at the AFQCP. We present phase diagrams in the T-y0 plane including contour maps of the AF correlation length and AF and SC transition temperatures TN and Tc, respectively. The Tc curve is dome-shaped with a maximum at around the AFQCP. The calculated one-electron spectral density shows a pseudogap in the high-density-of-states region near (pi,0) below around a certain temperature T* and gives a contour map at the Fermi energy reminiscent of the Fermi arc. These results are discussed in comparison with e- and h-doped high-Tc cuprates.Comment: 5 pages, 3 figure

Which biomarker predicts benefit from EGFR-TKI treatment for patients with lung cancer?

Subsets of patients with non-small cell lung cancer respond remarkably well to small molecule tyrosine kinase inhibitors (TKI) specific for epidermal growth factor receptor (EGFR) such as gefitinib or erlotinib. In 2004, it was found that EGFR mutations occurring in the kinase domain are strongly associated with EGFR-TKI sensitivity. However, subsequent studies revealed that this relationship was not perfect and various predictive markers have been reported. These include EGFR gene copy numbers, status of ligands for EGFR, changes in other HER family genes or molecules downstream to EGFR including KRAS or AKT. In this review, we would like to review current knowledge of predictive factors for EGFR-TKI. As all but one phase III trials failed to show a survival advantage of the treatment arm involving EGFR-TKIs, it is necessary to select patients by these biomarkers in future clinical trials. Through these efforts, it would be possible to individualise EGFR-TKI treatment for patients suffering from lung cancer

Siglecg Limits the Size of B1a B Cell Lineage by Down-Regulating NFκB Activation

BACKGROUND: B1 B cells are believed to be a unique lineage with a distinct developmental pathway, function and activation requirement. How this lineage is genetically determined remained largely obscure. METHODS AND PRINCIPAL FINDINGS: Using the Siglecg-deficient mice with a knockin of green-fluorescent protein encoding sequence, we show here that, although the Siglecg gene is broadly expressed at high levels in all stages and/or lineages of B cells tested and at lower levels in other lineages, its deletion selectively expanded the B1a B cell lineages, including the frequency of the B1 cell progenitor in the bone marrow and the number of B1a cells in the peritoneal cavity, by postnatal expansion. The expansion of B1a B cells in the peritoneal correlated with enhanced activation of NFkappaB and was ablated by an IKK inhibitor. CONCLUSION AND SIGNIFICANCE: Our data revealed a critical role for Siglec G-NFkappaB pathway in regulating B1a B cell lineage. These data lead to a novel model of B1a lineage development that explains a large array of genetic data in this field

Quasiparticle Inelastic Lifetime from Paramagnons in Disordered Superconductors

The paramagnon contribution to the quasiparticle inelastic scattering rate in disordered superconductors is presented. Using Anderson's exact eigenstate formalism, it is shown that the scattering rate is Stoner enhanced and is further enhanced by the disorder relative to the clean case in a manner similar to the disorder enhancement of the long-range Coulomb contribution. The results are discussed in connection with the possibility of conventional or unconventional superconductivity in the borocarbides. The results are compared to recent tunneling experiments on LuNi$_{2}$B$_{2}$C.Comment: 5 pages, no figure

Celebrating Cercignani's conjecture for the Boltzmann equation

Cercignani's conjecture assumes a linear inequality between the entropy and entropy production functionals for Boltzmann's nonlinear integral operator in rarefied gas dynamics. Related to the field of logarithmic Sobolev inequalities and spectral gap inequalities, this issue has been at the core of the renewal of the mathematical theory of convergence to thermodynamical equilibrium for rarefied gases over the past decade. In this review paper, we survey the various positive and negative results which were obtained since the conjecture was proposed in the 1980s.Comment: This paper is dedicated to the memory of the late Carlo Cercignani, powerful mind and great scientist, one of the founders of the modern theory of the Boltzmann equation. 24 pages. V2: correction of some typos and one ref. adde

The Therapeutic Implications of Plasticity of the Cancer Stem Cell Phenotype

The cancer stem cell hypothesis suggests that tumors contain a small population of cancer cells that have the ability to undergo symmetric self-renewing cell division. In tumors that follow this model, cancer stem cells produce various kinds of specified precursors that divide a limited number of times before terminally differentiating or undergoing apoptosis. As cells within the tumor mature, they become progressively more restricted in the cell types to which they can give rise. However, in some tumor types, the presence of certain extra- or intracellular signals can induce committed cancer progenitors to revert to a multipotential cancer stem cell state. In this paper, we design a novel mathematical model to investigate the dynamics of tumor progression in such situations, and study the implications of a reversible cancer stem cell phenotype for therapeutic interventions. We find that higher levels of dedifferentiation substantially reduce the effectiveness of therapy directed at cancer stem cells by leading to higher rates of resistance. We conclude that plasticity of the cancer stem cell phenotype is an important determinant of the prognosis of tumors. This model represents the first mathematical investigation of this tumor trait and contributes to a quantitative understanding of cancer