Forecasting Design Day Demand Using Extremal Quantile Regression

Extreme events occur rarely, making them difficult to predict. Extreme cold events strain natural gas systems to their limits. Natural gas distribution companies need to be prepared to satisfy demand on any given day that is at or warmer than an extreme cold threshold. The hypothetical day with temperature at this threshold is called the Design Day. To guarantee Design Day demand is satisfied, distribution companies need to determine the demand that is unlikely to be exceeded on the Design Day. We approach determining this demand as an extremal quantile regression problem. We review current methods for extremal quantile regression. We implement a quantile forecast to estimate the demand that has a minimal chance of being exceeded on the design day. We show extremal quantile regression to be more reliable than direct quantile estimation. We discuss the difficult task of evaluating a probabilistic forecast on rare events. Probabilistic forecasting is a quickly growing research topic in the field of energy forecasting. Our paper contributes to this field in three ways. First, we forecast quantiles during extreme cold events where data is sparse. Second, we forecast extremely high quantiles that have a very low probability of being exceeded. Finally, we provide a real world scenario on which to apply these techniques

Age grading \u3cem\u3eAn. gambiae\u3c/em\u3e and \u3cem\u3eAn. arabiensis\u3c/em\u3e using near infrared spectra and artificial neural networks

Background Near infrared spectroscopy (NIRS) is currently complementing techniques to age-grade mosquitoes. NIRS classifies lab-reared and semi-field raised mosquitoes into \u3c or ≥ 7 days old with an average accuracy of 80%, achieved by training a regression model using partial least squares (PLS) and interpreted as a binary classifier. Methods and findings We explore whether using an artificial neural network (ANN) analysis instead of PLS regression improves the current accuracy of NIRS models for age-grading malaria transmitting mosquitoes. We also explore if directly training a binary classifier instead of training a regression model and interpreting it as a binary classifier improves the accuracy. A total of 786 and 870 NIR spectra collected from laboratory reared An. gambiae and An. arabiensis, respectively, were used and pre-processed according to previously published protocols. The ANN regression model scored root mean squared error (RMSE) of 1.6 ± 0.2 for An. gambiae and 2.8 ± 0.2 for An. arabiensis; whereas the PLS regression model scored RMSE of 3.7 ± 0.2 for An. gambiae, and 4.5 ± 0.1 for An. arabiensis. When we interpreted regression models as binary classifiers, the accuracy of the ANN regression model was 93.7 ± 1.0% for An. gambiae, and 90.2 ± 1.7% for An. arabiensis; while PLS regression model scored the accuracy of 83.9 ± 2.3% for An. gambiae, and 80.3 ± 2.1% for An. arabiensis. We also find that a directly trained binary classifier yields higher age estimation accuracy than a regression model interpreted as a binary classifier. A directly trained ANN binary classifier scored an accuracy of 99.4 ± 1.0 for An. gambiae and 99.0 ± 0.6% for An. arabiensis; while a directly trained PLS binary classifier scored 93.6 ± 1.2% for An. gambiae and 88.7 ± 1.1% for An. arabiensis. We further tested the reproducibility of these results on different independent mosquito datasets. ANNs scored higher estimation accuracies than when the same age models are trained using PLS. Regardless of the model architecture, directly trained binary classifiers scored higher accuracies on classifying age of mosquitoes than regression models translated as binary classifiers. Conclusion We recommend training models to estimate age of An. arabiensis and An. gambiae using ANN model architectures (especially for datasets with at least 70 mosquitoes per age group) and direct training of binary classifier instead of training a regression model and interpreting it as a binary classifier

A helical-shape scintillating fiber trigger and tracker system for the DarkLight experiment and beyond

The search for new physics beyond the Standard Model has interesting possibilities at low energies. For example, the recent 6.8$\sigma$ anomaly reported in the invariant mass of $e^+e^-$ pairs from $^8\text{Be}$ nuclear transitions and the discrepancy between predicted and measured values of muon g-2 give strong motivations for a protophobic fifth-force model. At low energies, the electromagnetic interaction is well understood and produces straightforward final states, making it an excellent probe of such models. However, to achieve the required precision, an experiment must address the substantially higher rate of electromagnetic backgrounds. In this paper, we present the results of simulation studies of a trigger system, motivated by the DarkLight experiment, using helical-shape scintillating fibers in a solenoidal magnetic field to veto electron-proton elastic scattering and the associated radiative processes. We also assess the performance of a tracking detector for lepton final states using scintillating fibers in the same setup

Measurement of the directional sensitivity of Dark Matter Time Projection Chamber detectors

The Dark Matter Time Projection Chamber (DMTPC) is a direction-sensitive detector designed to measure the direction of recoiling $^{19}$F and $^{12}$C nuclei in low-pressure CF$_4$ gas using optical and charge readout systems. In this paper, we employ measurements from two DMTPC detectors, with operating pressures of 30-60 torr, to develop and validate a model of the directional response and performance of such detectors as a function of recoil energy. Using our model as a benchmark, we formulate the necessary specifications for a scalable directional detector with sensitivity comparable to that of current-generation counting (non-directional) experiments, which measure only recoil energy. Assuming the performance of existing DMTPC detectors, as well as current limits on the spin-dependent WIMP-nucleus cross section, we find that a 10-20 kg scale direction-sensitive detector is capable of correlating the measured direction of nuclear recoils with the predicted direction of incident dark matter particles and providing decisive (3$\sigma$) confirmation that a candidate signal from a non-directional experiment was indeed induced by elastic scattering of dark matter particles off of target nuclei.Comment: 13 pages, 10 figures. Accepted for publication in Phys. Rev. D. Added color figures, switched to more compact layout, and fixed some reference

Heat flow at the spreading centers of the Guaymas Basin, Gulf of California

Fifty-four new heat flow measurements in the central troughs of the Guaymas basin support the hypothesis that they are sites of active intrusion. In the northern trough a distinct pattern of hydrothermal cooling is revealed, with venting along the western boundary fault of the trough. In the southern trough an analogous pattern is apparently superimposed upon a conductive cooling anomaly associated with a recent central intrusion. The discharge of thermal waters occurs along the boundary faults and through other faults associated with a possible horst block located in the north central floor of the southern trough. The heat flow patterns suggest that the intrusions are episodic and do not occur simultaneously along the length (15–40 km) of a spreading segment. A review of all available heat flow measurements for the Guaymas basin suggests that most of the recharge for a pervasive regional hydrothermal system is limited to the central depressions, with perhaps some contribution from pore water. The discharge of thermal waters occurs predominantly in the central depressions and possibly along the boundary transform faults and fracture zones. The regions of the basin more than a few kilometers in distance from the spreading axis, although presumably underlain by a hydrothermal system, are probably not the location of numerous vents or recharge zones

An autoencoder and artificial neural network-based method to estimate parity status of wild mosquitoes from near-infrared spectra

After mating, female mosquitoes need animal blood to develop their eggs. In the process of acquiring blood, they may acquire pathogens, which may cause different diseases in humans such as malaria, zika, dengue, and chikungunya. Therefore, knowing the parity status of mosquitoes is useful in control and evaluation of infectious diseases transmitted by mosquitoes, where parous mosquitoes are assumed to be potentially infectious. Ovary dissections, which are currently used to determine the parity status of mosquitoes, are very tedious and limited to few experts. An alternative to ovary dissections is near-infrared spectroscopy (NIRS), which can estimate the age in days and the infectious state of laboratory and semi-field reared mosquitoes with accuracies between 80 and 99%. No study has tested the accuracy of NIRS for estimating the parity status of wild mosquitoes. In this study, we train an artificial neural network (ANN) models on NIR spectra to estimate the parity status of wild mosquitoes. We use four different datasets: An. arabiensis collected from Minepa, Tanzania (Minepa-ARA); An. gambiae s.s collected from Muleba, Tanzania (Muleba-GA); An. gambiae s.s collected from Burkina Faso (Burkina-GA); and An.gambiae s.s from Muleba and Burkina Faso combined (Muleba-Burkina-GA). We train ANN models on datasets with spectra preprocessed according to previous protocols. We then use autoencoders to reduce the spectra feature dimensions from 1851 to 10 and re-train the ANN models. Before the autoencoder was applied, ANN models estimated parity status of mosquitoes in Minepa-ARA, Muleba-GA, Burkina-GA and Muleba-Burkina-GA with out-of-sample accuracies of 81.9±2.8 (N = 274), 68.7±4.8 (N = 43), 80.3±2.0 (N = 48), and 75.7±2.5 (N = 91), respectively. With the autoencoder, ANN models tested on out-of-sample data achieved 97.1±2.2% (N = 274), 89.8 ± 1.7% (N = 43), 93.3±1.2% (N = 48), and 92.7±1.8% (N = 91) accuracies for Minepa-ARA, Muleba-GA, Burkina-GA, and Muleba-Burkina-GA, respectively. These results show that a combination of an autoencoder and an ANN trained on NIR spectra to estimate the parity status of wild mosquitoes yields models that can be used as an alternative tool to estimate parity status of wild mosquitoes, especially since NIRS is a high-throughput, reagent-free, and simple-to-use technique compared to ovary dissections

Polarized localization of phosphatidylserine in the endothelium regulates Kir2.1

Lipid regulation of ion channels is largely explored using in silico modeling with minimal experimentation in intact tissue; thus, the functional consequences of these predicted lipid-channel interactions within native cellular environments remain elusive. The goal of this study is to investigate how lipid regulation of endothelial Kir2.1 - an inwardly rectifying potassium channel that regulates membrane hyperpolarization - contributes to vasodilation in resistance arteries. First, we show that phosphatidylserine (PS) localizes to a specific subpopulation of myoendothelial junctions (MEJs), crucial signaling microdomains that regulate vasodilation in resistance arteries, and in silico data have implied that PS may compete with phosphatidylinositol 4,5-bisphosphate (PIP2) binding on Kir2.1. We found that Kir2.1-MEJs also contained PS, possibly indicating an interaction where PS regulates Kir2.1. Electrophysiology experiments on HEK cells demonstrate that PS blocks PIP2 activation of Kir2.1 and that addition of exogenous PS blocks PIP2-mediated Kir2.1 vasodilation in resistance arteries. Using a mouse model lacking canonical MEJs in resistance arteries (Elnfl/fl/Cdh5-Cre), PS localization in endothelium was disrupted and PIP2 activation of Kir2.1 was significantly increased. Taken together, our data suggest that PS enrichment to MEJs inhibits PIP2-mediated activation of Kir2.1 to tightly regulate changes in arterial diameter, and they demonstrate that the intracellular lipid localization within the endothelium is an important determinant of vascular function

Building a diverse workforce and thinkforce to reduce health disparities

The Research Centers in Minority Institutions (RCMI) Program was congressionally man-dated in 1985 to build research capacity at institutions that currently and historically recruit, train, and award doctorate degrees in the health professions and health-related sciences, primarily to individuals from underrepresented and minority populations. RCMI grantees share similar infrastructure needs and institutional goals. Of particular importance is the professional development of multidisciplinary teams of academic and community scholars (the “workforce”) and the harnessing of the heterogeneity of thought (the “thinkforce”) to reduce health disparities. The purpose of this report is to summarize the presentations and discussion at the RCMI Investigator Development Core (IDC) Workshop, held in conjunction with the RCMI Program National Conference in Bethesda, Maryland, in December 2019. The RCMI IDC Directors provided information about their professional development activities and Pilot Projects Programs and discussed barriers identified by new and early-stage investigators that limit effective career development, as well as potential solutions to overcome such obstacles. This report also proposes potential alignments of professional development activities, targeted goals and common metrics to track productivity and success

A noninvasive method for measuring the velocity of diffuse hydrothermal flow by tracking moving refractive index anomalies

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95358/1/ggge1802.pd