A new paradigm for medium-range severe weather forecasts: probabilistic random forest-based predictions

Historical observations of severe weather and simulated severe weather environments (i.e., features) from the Global Ensemble Forecast System v12 (GEFSv12) Reforecast Dataset (GEFS/R) are used in conjunction to train and test random forest (RF) machine learning (ML) models to probabilistically forecast severe weather out to days 4--8. RFs are trained with 9 years of the GEFS/R and severe weather reports to establish statistical relationships. Feature engineering is briefly explored to examine alternative methods for gathering features around observed events, including simplifying features using spatial averaging and increasing the GEFS/R ensemble size with time-lagging. Validated RF models are tested with ~1.5 years of real-time forecast output from the operational GEFSv12 ensemble and are evaluated alongside expert human-generated outlooks from the Storm Prediction Center (SPC). Both RF-based forecasts and SPC outlooks are skillful with respect to climatology at days 4 and 5 with degrading skill thereafter. The RF-based forecasts exhibit tendencies to underforecast severe weather events, but they tend to be well-calibrated at lower probability thresholds. Spatially averaging predictors during RF training allows for prior-day thermodynamic and kinematic environments to generate skillful forecasts, while time-lagging acts to expand the forecast areas, increasing resolution but decreasing objective skill. The results highlight the utility of ML-generated products to aid SPC forecast operations into the medium range

The Feasibility of a Using a Smart Button Mobile Health System to Self-Track Medication Adherence and Deliver Tailored Short Message Service Text Message Feedback

BACKGROUND: As many as 50% of people experience medication nonadherence, yet studies for detecting nonadherence and delivering real-time interventions to improve adherence are lacking. Mobile health (mHealth) technologies show promise to track and support medication adherence. OBJECTIVE: The study aimed to evaluate the feasibility and acceptability of using an mHealth system for medication adherence tracking and intervention delivery. The mHealth system comprises a smart button device to self-track medication taking, a companion smartphone app, a computer algorithm used to determine adherence and then deliver a standard or tailored SMS (short message service) text message on the basis of timing of medication taking. Standard SMS text messages indicated that the smartphone app registered the button press, whereas tailored SMS text messages encouraged habit formation and systems thinking on the basis of the timing the medications were taken. METHODS: A convenience sample of 5 adults with chronic kidney disease (CKD), who were prescribed antihypertensive medication, participated in a 52-day longitudinal study. The study was conducted in 3 phases, with a standard SMS text message sent in phases 1 (study days 1-14) and 3 (study days 46-52) and tailored SMS text messages sent during phase 2 (study days 15-45) in response to participant medication self-tracking. Medication adherence was measured using: (1) the smart button and (2) electronic medication monitoring caps. Concordance between these 2 methods was evaluated using percentage of measurements made on the same day and occurring within ±5 min of one another. Acceptability was evaluated using qualitative feedback from participants. RESULTS: A total of 5 patients with CKD, stages 1-4, were enrolled in the study, with the majority being men (60%), white (80%), and Hispanic/Latino (40%) of middle age (52.6 years, SD 22.49; range 20-70). The mHealth system was successfully initiated in the clinic setting for all enrolled participants. Of the expected 260 data points, 36.5% (n=95) were recorded with the smart button and 76.2% (n=198) with electronic monitoring. Concordant events (n=94), in which events were recorded with both the smart button and electronic monitoring, occurred 47% of the time and 58% of these events occurred within ±5 min of one another. Participant comments suggested SMS text messages were encouraging. CONCLUSIONS: It was feasible to recruit participants in the clinic setting for an mHealth study, and our system was successfully initiated for all enrolled participants. The smart button is an innovative way to self-report adherence data, including date and timing of medication taking, which were not previously available from measures that rely on recall of adherence. Although the selected smart button had poor concordance with electronic monitoring caps, participants were willing to use it to self-track medication adherence, and they found the mHealth system acceptable to use in most cases

Unobtrusive measurement of psychological constructs in organizational research

Measurement in organizational psychology is dominated by the use of approaches that require the cooperation of a respondent—namely, questionnaires and interviews. The goal of this article is to increase and improve the use of unobtrusivemeasures as a supplementalmeans toassess psychological constructs in organizational research. Specifically, we first illustrate themerit and necessity of utilizing unobtrusive measures. Next, we review the literature employing unobtrusive measures to assess psychological constructs and then discuss threats to validity associated with these approaches. Finally, we offer recommendations to enhance the effectiveness of unobtrusive measures in future research

The effects of low protein during gestation on mouse pancreatic development and beta cell regeneration

Beta cells are partially replaced in neonatal rodents after deletion with streptozotocin (STZ). Exposure of pregnant rats to a low protein (LP) diet impairs endocrine pancreas development in the offspring, leading to glucose intolerance in adulthood. Our objective was to determine whether protein restriction has a similar effect on the offspring in mice, and if this alters the capacity for beta cell regeneration after STZ. Pregnant Balb/c mice were fed a control (C) (20% protein) or an isocaloric LP (8% protein) diet during gestation. Pups were given 35 mg/kg STZ (or vehicle) from d 1 to 5 for each dietary treatment. Histologic analysis showed that C-fed offspring had largely replaced beta cell mass (BCM) after STZ by d 30, but this was not sustained over time. Female LP-fed offspring showed an initial increase in BCM by d 14 but developed glucose intolerance by d 130. In contrast, male LP offspring showed no changes in BCM or glucose tolerance. However, LP exposure limited the capacity for recovery of BCM in both genders after STZ treatment. Copyright © 2010 International Pediatric Research Foundation, Inc

Phosphodiesterase 5 Inhibition Improves β-Cell Function in Metabolic Syndrome

OBJECTIVE: This study tested the hypothesis that phosphodiesterase 5 inhibition alone or in combination with ACE inhibition improves glucose homeostasis and fibrinolysis in individuals with metabolic syndrome. RESEARCH DESIGN AND METHODS: Insulin sensitivity, beta-cell function, and fibrinolytic parameters were measured in 18 adults with metabolic syndrome on 4 separate days after a randomized, crossover, double-blind, 3-week treatment with placebo, ramipril (10 mg/day), tadalafil (10 mg o.d.), and ramipril plus tadalafil. RESULTS: Ramipril decreased systolic and diastolic blood pressure, ACE activity, and angiotensin II and increased plasma renin activity. Ramipril did not affect insulin sensitivity or beta-cell function. In contrast, tadalafil improved beta-cell function (P = 0.01). This effect was observed in women (331.9 +/- 209.3 vs. 154.4 +/- 48.0 32 micro x mmol(-1) x l(-1), respectively, for tadalafil treatment vs. placebo; P = 0.01) but not in men. There was no effect of any treatment on fibrinolysis. CONCLUSIONS Phosphodiesterase 5 inhibition may represent a novel strategy for improving beta-cell function in metabolic syndrome

Effects of a Severe Cold Event on the Subtropical, Estuarine-Dependent Common Snook, Centropomus undecimalis

The effects of infrequent disturbance events on marine fishes are often difficult to determine, due largely to lack of sufficient pre- and post-disturbance event data. In January 2010, subtropical southwestern Florida (USA) experienced extreme cold for 13 days, which caused extensive mortality of many fish species. The effect of this severe cold event on common snook (Centropomus undecimalis), an economically important gamefish, was assessed using three years (2007-2009) of pre-event and one year (2010) of post-event data from a tag-recapture program conducted over 28 km of Gulf of Mexico barrier islands of Florida. All metrics pointed to a significant effect of the severe cold event: post-disturbance apparent survival of marked fish was 96-97% lower than pre-disturbance, and post-disturbance common snook abundance was 75.57% and 41.88% less than in 2008 and 2009, the two years immediately pre-event. Although severe cold events have impacted subtropical Florida in the past, these events are infrequent (the previous recorded event was \u3e30 years prior), and documentation of the impacts on common snook have not previously been published

Distances to Populous Clusters in the LMC via the K-Band Luminosity of the Red Clump

We present results from a study of the distances and distribution of a sample of intermediate-age clusters in the Large Magellanic Cloud. Using deep near-infrared photometry obtained with ISPI on the CTIO 4m, we have measured the apparent K-band magnitude of the core helium burning red clump stars in 17 LMC clusters. We combine cluster ages and metallicities with the work of Grocholski & Sarajedini to predict each cluster's absolute K-band red clump magnitude, and thereby calculate absolute cluster distances. An analysis of these data shows that the cluster distribution is in good agreement with the thick, inclined disk geometry of the LMC, as defined by its field stars. We also find that the old globular clusters follow the same distribution, suggesting that the LMC's disk formed at about the same time as the globular clusters, ~ 13 Gyr ago. Finally, we have used our cluster distances in conjunction with the disk geometry to calculate the distance to the LMC center, for which we find (m-M)o = 18.40 +/- 0.04_{ran} +/- 0.08_{sys}, or Do = 47.9 +/- 0.9 +/- 1.8 kpc.Comment: 31 pages including 5 figures and 7 tables. Accepted for publication in the August 2007 issue of A

A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines

Information in neural networks is represented as weighted connections, or synapses, between neurons. This poses a problem as the primary computational bottleneck for neural networks is the vector-matrix multiply when inputs are multiplied by the neural network weights. Conventional processing architectures are not well suited for simulating neural networks, often requiring large amounts of energy and time. Additionally, synapses in biological neural networks are not binary connections, but exhibit a nonlinear response function as neurotransmitters are emitted and diffuse between neurons. Inspired by neuroscience principles, we present a digital neuromorphic architecture, the Spiking Temporal Processing Unit (STPU), capable of modeling arbitrary complex synaptic response functions without requiring additional hardware components. We consider the paradigm of spiking neurons with temporally coded information as opposed to non-spiking rate coded neurons used in most neural networks. In this paradigm we examine liquid state machines applied to speech recognition and show how a liquid state machine with temporal dynamics maps onto the STPU-demonstrating the flexibility and efficiency of the STPU for instantiating neural algorithms.Comment: 8 pages, 4 Figures, Preprint of 2017 IJCN

U.S. SPACE FORCE (USSF) ACQUISITION OCCUPATIONAL COMPETENCY INTEGRATION INTO A TALENT OPERATIONS PLATFORM

The idea of using competencies as a vehicle for effective talent management has been an idea explored by many organizations. Recently all service components across the Department of Defense (DOD) have begun a revolution within talent management, particularly with job placement. The DOD’s newest component, the United States Space Force (USSF), actively seeks to implement a competency-based process as dictated by the Guardian Ideal. This capstone report provides USSF with recommendations on effectively integrating a scalable competency-driven system into a talent operations platform that manages Guardian talent during assignment placement. The team evaluated civilian and governmental talent operations systems and processes through interviews with relevant talent management personnel within the DOD and industry. This qualitative analysis fueled the team’s development of a simulation model to identify the effects of competency integration on the system and its interaction with external variables. Throughout the research, the team confirmed that all services desire the effective integration of competencies but lack the implementation of accountable competencies by a validation method. The team recommends Space Force develop a way to validate and input competency assessments by implementing the competency framework within a software system in terms of a scoring algorithm to provide a clear picture for Guardians and Commanders to determine the best fit for vacant billets.Space Force Talent Management Office (ETMO)Major, United States ArmyCaptain, United States ArmyCaptain, United States ArmyCaptain, United States ArmyCaptain, United States ArmyApproved for public release. Distribution is unlimited

Forecast dataset associated with “From Random Forests to Flood Forecasts: A Research to Operations Success Story”

Gridded forecasts from the Colorado State University-Machine Learning Probabilities (CSU-MLP) system for excessive rainfall prediction over the continental United States. The dataset includes probabilistic forecasts for days 1, 2, and 3 from the 2017, 2019, and 2020 versions of the CSU-MLP forecast system. For the day 2 and 3 forecasts, daily forecasts are included from 19 June 2018 through 15 October 2020; for day-1 forecasts a period from 15 March 2019 through 15 October 2020 is used.Because excessive rainfall is poorly defined and difficult to forecast, there is a need for tools for Weather Prediction Center (WPC) forecasters to use when generating Excessive Rainfall Outlooks (EROs), which are issued for the contiguous United States at lead times of 1--3 days. To address this need, a probabilistic forecast system for excessive rainfall, known as the Colorado State University-Machine Learning Probabilities (CSU-MLP) system, was developed based on ensemble reforecasts, precipitation observations, and machine learning algorithms, specifically random forests. The CSU-MLP forecasts were designed to emulate the EROs, with the goal being a tool that forecasters can use as a ``first guess'' in the ERO forecast process. Resulting from close collaboration between CSU and WPC and evaluation at the Flash Flood and Intense Rainfall experiment, iterative improvements were made to the forecast system and it was transitioned into operational use at WPC. Quantitative evaluation shows that the CSU-MLP forecasts are skillful and reliable, and they are now being used as a part of the WPC forecast process. This project represents an example of a successful research-to-operations transition, and highlights the potential for machine learning and other post-processing techniques to improve operational predictions.This research and operational transition was supported by NOAA Joint Technology Transfer Initiative grants NA16OAR4590238 and NA18OAR4590378