A Guide to Solar Power Forecasting using ARMA Models

We describe a simple and succinct methodology to develop hourly auto-regressive moving average (ARMA) models to forecast power output from a photovoltaic solar generator. We illustrate how to build an ARMA model, to use statistical tests to validate it, and construct hourly samples. The resulting model inherits nice properties for embedding it into more sophisticated operation and planning models, while at the same time showing relatively good accuracy. Additionally, it represents a good forecasting tool for sample generation for stochastic energy optimization models

Plasmodium yoelii 17XL infection up-regulates RANTES, CCR1, CCR3 and CCR5 expression, and induces ultrastructural changes in the cerebellum

BACKGROUND: Malaria afflicts 300–500 million people causing over 1 million deaths globally per year. The immunopathogenesis of malaria is mediated partly by co mplex cellular and immunomodulator interactions involving co-regulators such as cytokines and adhesion molecules. However, the role of chemokines and their receptors in malaria immunopathology remains unclear. RANTES (Regulated on Activation Normal T-Cell Expressed and Secreted) is a chemokine involved in the generation of inflammatory infiltrates. Recent studies indicate that the degradation of cell-cell junctions, blood-brain barrier dysfunction, recruitment of leukocytes and Plasmodium-infected erythrocytes into and occlusion of microvessels relevant to malaria pathogenesis are associated with RANTES expression. Additionally, activated lymphocytes, platelets and endothelial cells release large quantities of RANTES, thus suggesting a unique role for RANTES in the generation and maintenance of the malaria-induced inflammatory response. The hypothesis of this study is that RANTES and its corresponding receptors (CCR1, CCR3 and CCR5) modulate malaria immunopathogenesis. A murine malaria model was utilized to evaluate the role of this chemokine and its receptors in malaria. METHODS: The alterations in immunomodulator gene expression in brains of Plasmodium yoelii 17XL-infected mice was analysed using cDNA microarray screening, followed by a temporal comparison of mRNA and protein expression of RANTES and its corresponding receptors by qRT-PCR and Western blot analysis, respectively. Plasma RANTES levels was determined by ELISA and ultrastructural studies of brain sections from infected and uninfected mice was conducted. RESULTS: RANTES (p < 0.002), CCR1 (p < 0.036), CCR3 (p < 0.033), and CCR5 (p < 0.026) mRNA were significantly upregulated at peak parasitaemia and remained high thereafter in the experimental mouse model. RANTES protein in the brain of infected mice was upregulated (p < 0.034) compared with controls. RANTES plasma levels were significantly upregulated; two to three fold in infected mice compared with controls (p < 0.026). Some d istal microvascular endothelium in infected cerebellum appeared degraded, but remained intact in controls. CONCLUSION: The upregulation of RANTES, CCR1, CCR3, and CCR5 mRNA, and RANTES protein mediate inflammation and cellular degradation in the cerebellum during P. yoelii 17XL malaria

Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge

Background: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season. Methods: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). Results: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. Conclusion: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts. © 2016 The Author(s)

Detector signal characterization with a Bayesian network in XENONnT

We developed a detector signal characterization model based on a Bayesian network trained on the waveform attributes generated by a dual-phase xenon time projection chamber. By performing inference on the model, we produced a quantitative metric of signal characterization and demonstrate that this metric can be used to determine whether a detector signal is sourced from a scintillation or an ionization process. We describe the method and its performance on electronic-recoil (ER) data taken during the first science run of the XENONnT dark matter experiment. We demonstrate the first use of a Bayesian network in a waveform-based analysis of detector signals. This method resulted in a 3% increase in ER event-selection efficiency with a simultaneously effective rejection of events outside of the region of interest. The findings of this analysis are consistent with the previous analysis from XENONnT, namely a background-only fit of the ER data

First Dark Matter Search with Nuclear Recoils from the XENONnT Experiment

We report on the first search for nuclear recoils from dark matter in the form of weakly interacting massive particles (WIMPs) with the XENONnT experiment, which is based on a two-phase time projection chamber with a sensitive liquid xenon mass of 5.9 ton. During the (1.09±0.03)  ton yr exposure used for this search, the intrinsic $^{85}$Kr and $^{222}$Rn concentrations in the liquid target are reduced to unprecedentedly low levels, giving an electronic recoil background rate of (15.8±1.3)  events/ton yr keV in the region of interest. A blind analysis of nuclear recoil events with energies between 3.3 and 60.5 keV finds no significant excess. This leads to a minimum upper limit on the spin-independent WIMP-nucleon cross section of 2.58×10$^{47}$  cm$^2$ for a WIMP mass of 28  GeV/c$^2$ at 90% confidence level. Limits for spin-dependent interactions are also provided. Both the limit and the sensitivity for the full range of WIMP masses analyzed here improve on previous results obtained with the XENON1T experiment for the same exposure

Searching for Heavy Dark Matter near the Planck Mass with XENON1T

Multiple viable theoretical models predict heavy dark matter particles with a mass close to the Planck mass, a range relatively unexplored by current experimental measurements. We use 219.4 days of data collected with the XENON1T experiment to conduct a blind search for signals from multiply interacting massive particles (MIMPs). Their unique track signature allows a targeted analysis with only 0.05 expected background events from muons. Following unblinding, we observe no signal candidate events. This Letter places strong constraints on spin-independent interactions of dark matter particles with a mass between 1×10$^{12}$ and 2×10$^{17}$  GeV/c$^2$. In addition, we present the first exclusion limits on spin-dependent MIMP-neutron and MIMP-proton cross sections for dark matter particles with masses close to the Planck scale

Search for events in XENON1T associated with Gravitational Waves

We perform a blind search for particle signals in the XENON1T dark matter detector that occur close in time to gravitational wave signals in the LIGO and Virgo observatories. No particle signal is observed in the nuclear recoil, electronic recoil, CE$\nu$NS, and S2-only channels within $\pm$ 500 seconds of observations of the gravitational wave signals GW170104, GW170729, GW170817, GW170818, and GW170823. We use this null result to constrain mono-energetic neutrinos and Beyond Standard Model particles emitted in the closest coalescence GW170817, a binary neutron star merger. We set new upper limits on the fluence (time-integrated flux) of coincident neutrinos down to 17 keV at 90% confidence level. Furthermore, we constrain the product of coincident fluence and cross section of Beyond Standard Model particles to be less than $10^{-29}$ cm$^2$/cm$^2$ in the [5.5-210] keV energy range at 90% confidence level

Searching for Heavy Dark Matter near the Planck Mass with XENON1T

Multiple viable theoretical models predict heavy dark matter particles with a mass close to the Planck mass, a range relatively unexplored by current experimental measurements. We use 219.4 days of data collected with the XENON1T experiment to conduct a blind search for signals from Multiply-Interacting Massive Particles (MIMPs). Their unique track signature allows a targeted analysis with only 0.05 expected background events from muons. Following unblinding, we observe no signal candidate events. This work places strong constraints on spin-independent interactions of dark matter particles with a mass between 1$\times$10$^{12}\,$GeV/c$^2$ and 2$\times$10$^{17}\,$GeV/c$^2$. In addition, we present the first exclusion limits on spin-dependent MIMP-neutron and MIMP-proton cross-sections for dark matter particles with masses close to the Planck scale.Comment: 7 pages, 6 figure

Cosmogenic background simulations for the DARWIN observatory at different underground locations

Xenon dual-phase time projections chambers (TPCs) have proven to be a successful technology in studying physical phenomena that require low-background conditions. With 40t of liquid xenon (LXe) in the TPC baseline design, DARWIN will have a high sensitivity for the detection of particle dark matter, neutrinoless double beta decay ($0\nu\beta\beta$), and axion-like particles (ALPs). Although cosmic muons are a source of background that cannot be entirely eliminated, they may be greatly diminished by placing the detector deep underground. In this study, we used Monte Carlo simulations to model the cosmogenic background expected for the DARWIN observatory at four underground laboratories: Laboratori Nazionali del Gran Sasso (LNGS), Sanford Underground Research Facility (SURF), Laboratoire Souterrain de Modane (LSM) and SNOLAB. We determine the production rates of unstable xenon isotopes and tritium due to muon-included neutron fluxes and muon-induced spallation. These are expected to represent the dominant contributions to cosmogenic backgrounds and thus the most relevant for site selection