Motivation Differences between Youth Single-Sport, Multi-Sport, and Single-Sport Specialized Athletes in the Western United States

The motivation for youth sport involvement may differ for single-sport (non-specialists), multi-sport, and single-sport specialized athletes. To investigate differences between adolescent single-sport athletes (NSSA), multi-sport athletes (MSA), and single-sport specialized athletes (SSSA) on measures of sport enjoyment and motivation. A secondary aim was to compare these variables between age groups. Adolescent sport participants in 6th, 7th, and 8th grade from the Western United States (n=306, age=13.0±1.0 yrs) completing the Sources of Enjoyment in Youth Sport Questionnaire (SEYSQ) that assesses sport enjoyment in the subscales of self-referenced competency (SRC), other-referenced competency and recognition (ORCR), effort expenditure (EE), competitive excitement (CE), affiliation with peers (AP), and positive parental involvement (PPI). The participants also reported their age, gender, grade, years of sport participation and sport status (NSSA, MSA, SSSA). SSSA reported significantly more enjoyment in all subscales except PPI when compared to NSSA (p<0.05; ES 0.4 – 0.99). MSA and SSSA showed significant differences in the subscales of SRC and EE; however, effect size was small (0.37 and 0.33, respectively). Overall scores for intrinsic and extrinsic motivation were significantly lower among NSSA compared to MSA and SSSA (p<0.01) with no differences between MSA and SSSA (p>0.05). Results revealed no significant differences in the SEYSQ’s subscales for age (p>0.05). Within the parameters of this study, adolescents that specialize in a single sport or those who compete in multiple sport both rely on intrinsic and extrinsic sources of enjoyment for motivation in very similar ways; whereas adolescent NSSA are less motivated and experience less enjoyment from sport participation compared to MSA and SSSA. Keywords: adolescent, enjoyment, SEYSQ, motivation, sport psychology

Alert Optimization of the PLUM Earthquake Early Warning Algorithm for the Western United States

ABSTRACT We determine an optimal alerting configuration for the propagation of local undamped motion (PLUM) earthquake early warning (EEW) algorithm for use by the U.S. ShakeAlert system covering California, Oregon, and Washington. All EEW systems should balance the primary goal of providing timely alerts for impactful or potentially damaging shaking while limiting alerts for shaking that is too low to be of concern (precautionary alerts). The PLUM EEW algorithm forward predicts observed ground motions to nearby sites within a defined radius without accounting for attenuation, avoiding the earthquake source parameter estimation step of most EEW algorithms. PLUM was originally developed in Japan where the alert regions and ground motions for which alerts are issued differ from those implemented by ShakeAlert. We compare predicted ground motions from PLUM to ShakeMap-reported ground motions for a set of 22 U.S. West Coast earthquakes of magnitude 4.4–7.2 and evaluate available warning times. We examine a range of prediction radii (20–100 km), thresholds used to issue an alert (alert threshold), and levels of impactful or potentially damaging shaking (target threshold). We find optimal performance when the alert threshold is close to the target threshold, although higher target ground motions benefit from somewhat lower alert thresholds to ensure timely alerts. We also find that performance, measured as the cost reduction that a user can achieve, depends on the user’s tolerance for precautionary alerts. Users with a low target threshold and high tolerance for precautionary alerts achieve optimal performance when larger prediction radii (60–100 km) are used. In contrast, users with high target thresholds and low tolerance for precautionary alerts achieve better performance for smaller prediction radii (30–60 km). Therefore, setting the PLUM prediction radius to 60 km balances the needs of many users and provides warning times of up to ∼20 s.

Global Comprehensive Outlook of Hantavirus Contagion on Humans: A Review

Hantaviruses are rodent viruses that have been identified as etiologic agents of 2 diseases of humans: hemorrhagic fever with renal syndrome (HFRS) and nephropathiaepidemica (NE) in the Old World and Hantavirus pulmonary syndrome (HPS) in the New World. Orthohantavirus is a genus of sin- gle-stranded, enveloped, negative-sense RNA viruses in the family Hantaviridae of the order Bunyavi- rales. The important reservoir of Hantaviruses is rodents. Each virus serotype has its unique rodent host species and is transmitted to human beings with the aid of aerosolized virus, which is shed in urine, fae- ces and saliva and hardly by a bite of the contaminated host. Andes virus is the only Hantavirus identified to be transmitted from human-to-human and its major signs and symptoms include fever, headache, mus- cle aches, lungs filled with fluid etc. In the early 1993, this viral syndrome appeared in the Four Cor- ner location in the south western United States. The only accepted therapeutics for this virus is Ribavirin. Recently, serological examinations to identify Hantavirus antibodies have become most popular for in- vestigation among humans and rodent reservoirs.

Pattern-based downscaling of snowpack variability in the western United States

The decline in snowpack across the western United States is one of the most pressing threats posed by climate change to regional economies and livelihoods. Earth system models are important tools for exploring past and future snowpack variability, yet their coarse spatial resolutions distort local topography and bias spatial patterns of accumulation and ablation. Here, we explore pattern-based statistical downscaling for spatially-continuous interannual snowpack estimates. We find that a few leading patterns capture the majority of snowpack variability across the western US in observations, reanalyses, and free-running simulations. Pattern-based downscaling methods yield accurate, high resolution maps that correct mean and variance biases in domain-wide simulated snowpack. Methods that use large-scale patterns as both predictors and predictands perform better than those that do not and all are superior to an interpolation-based “delta change” approach. These findings suggest that pattern-based methods are appropriate for downscaling interannual snowpack variability and that using physically meaningful large-scale patterns is more important than the details of any particular downscaling method.