Quantifying statistical uncertainty in the attribution of human influence on severe weather

Event attribution in the context of climate change seeks to understand the role of anthropogenic greenhouse gas emissions on extreme weather events, either specific events or classes of events. A common approach to event attribution uses climate model output under factual (real-world) and counterfactual (world that might have been without anthropogenic greenhouse gas emissions) scenarios to estimate the probabilities of the event of interest under the two scenarios. Event attribution is then quantified by the ratio of the two probabilities. While this approach has been applied many times in the last 15 years, the statistical techniques used to estimate the risk ratio based on climate model ensembles have not drawn on the full set of methods available in the statistical literature and have in some cases used and interpreted the bootstrap method in non-standard ways. We present a precise frequentist statistical framework for quantifying the effect of sampling uncertainty on estimation of the risk ratio, propose the use of statistical methods that are new to event attribution, and evaluate a variety of methods using statistical simulations. We conclude that existing statistical methods not yet in use for event attribution have several advantages over the widely-used bootstrap, including better statistical performance in repeated samples and robustness to small estimated probabilities. Software for using the methods is available through the climextRemes package available for R or Python. While we focus on frequentist statistical methods, Bayesian methods are likely to be particularly useful when considering sources of uncertainty beyond sampling uncertainty.Comment: 41 pages, 11 figures, 1 tabl

Quantifying the effect of interannual ocean variability on the attribution of extreme climate events to human influence

In recent years, the climate change research community has become highly interested in describing the anthropogenic influence on extreme weather events, commonly termed "event attribution." Limitations in the observational record and in computational resources motivate the use of uncoupled, atmosphere/land-only climate models with prescribed ocean conditions run over a short period, leading up to and including an event of interest. In this approach, large ensembles of high-resolution simulations can be generated under factual observed conditions and counterfactual conditions that might have been observed in the absence of human interference; these can be used to estimate the change in probability of the given event due to anthropogenic influence. However, using a prescribed ocean state ignores the possibility that estimates of attributable risk might be a function of the ocean state. Thus, the uncertainty in attributable risk is likely underestimated, implying an over-confidence in anthropogenic influence. In this work, we estimate the year-to-year variability in calculations of the anthropogenic contribution to extreme weather based on large ensembles of atmospheric model simulations. Our results both quantify the magnitude of year-to-year variability and categorize the degree to which conclusions of attributable risk are qualitatively affected. The methodology is illustrated by exploring extreme temperature and precipitation events for the northwest coast of South America and northern-central Siberia; we also provides results for regions around the globe. While it remains preferable to perform a full multi-year analysis, the results presented here can serve as an indication of where and when attribution researchers should be concerned about the use of atmosphere-only simulations

Injuries in Collegiate Women’s Volleyball: A Four-Year Retrospective Analysis

A four-year retrospective analysis of injury data was conducted on a collegiate (NCAA Division I) women’s volleyball team. Twenty athletes (Year 1: age = 19.4 ± 0.9 y, height = 175.2 ± 5.1 cm, body mass = 70.5 ± 10.2 kg; Year 2: age = 20.1 ± 1.0 y, height = 175.7 ± 4.7 cm, body mass = 69.5 ± 10.1 kg; Year 3: age = 20.1 ± 1.4 y, height = 173.8 ± 6.3 cm, body mass = 69.9 ± 10.8 kg; Year 4: age = 19.5 ± 1.4 y, height = 174.4 ± 8.6 cm, body mass = 72.7 ± 10.8 kg) participated in this study, accounting for 1483 total training exposures. Injury was defined as any damage to a body part, incurred during volleyball or strength and conditioning-related activities, which interfered with training and/or competition. Injury rate was normalized to the number of athletes and exposure and expressed as injuries per 1000 exposures. A total of 133 injuries were recorded. The most common injury was to the knee (left = 7.5%, right = 12.0%). Injuries occurred most often in volleyball practice (75.2%), followed by competition (20.3%), and strength and conditioning-related activities (4.5%). Non-contact injuries (upper body = 26.3%, lower body = 53.4%) were more common than contact injuries (upper-body = 13.5%, lower-body = 6.8%). An examination of injury rates relative to the training year revealed patterns in injury occurrence. Specifically, spikes in injury rate were consistently observed during periods of increased training volume that were preceded by breaks in organized training, such as the early pre-season and off-season training periods

Design and Implementation of the Andromeda Proof Assistant

Andromeda is an LCF-style proof assistant where the user builds derivable judgments by writing code in a meta-level programming language AML. The only trusted component of Andromeda is a minimalist nucleus (an implementation of the inference rules of an object-level type theory), which controls construction and decomposition of type-theoretic judgments. Since the nucleus does not perform complex tasks like equality checking beyond syntactic equality, this responsibility is delegated to the user, who implements one or more equality checking procedures in the meta-language. The AML interpreter requests witnesses of equality from user code using the mechanism of algebraic operations and handlers. Dynamic checks in the nucleus guarantee that no invalid object-level derivations can be constructed. To demonstrate the flexibility of this system structure, we implemented a nucleus consisting of dependent type theory with equality reflection. Equality reflection provides a very high level of expressiveness, as it allows the user to add new judgmental equalities, but it also destroys desirable meta-theoretic properties of type theory (such as decidability and strong normalization). The power of effects and handlers in AML is demonstrated by a standard library that provides default algorithms for equality checking, computation of normal forms, and implicit argument filling. Users can extend these new algorithms by providing local "hints" or by completely replacing these algorithms for particular developments. We demonstrate the resulting system by showing how to axiomatize and compute with natural numbers, by axiomatizing the untyped lambda-calculus, and by implementing a simple automated system for managing a universe of types

Electrical Modification of Combustion and the Affect of Electrode Geometry on the Field Produced

There has been extensive work to show how electric fields can influence combustion. However, many different set ups are used. This work shows how different set ups produce different field strengths and that the field is not always uniformly distributed. The field strength is modelled using Ansys Maxwell. The type of material used is discussed and the set up of apparatus. It is recommended to use parallel plates for experimentation. Parallel plates produce the most uniform field this allow's it's influence to be directly investigated and related to the field strength

KINETIC ASYMMETRY AND CENTER OF MASS DISPLACEMENT DURING JUMPS

The purpose of this study was to determine the role of kinetic asymmetry on center of mass displacement in both mediolateral (COMd ML) and anteroposterior directions (COMd AP). Seventeen collegiate baseball players underwent weight distribution (WtD), and unloaded and loaded static (SJ) and countermovement jumps (CMJ). Concurrent kinetic and kinematic data were collected during the evaluation. Independent samples t tests were run to evaluate differences in COMd between the most and least asymmetrical athletes. WtD was not able to differentiate between values of COMd AP, but did for loaded conditions of COMd ML. Peak force and rate of force development (RFD) asymmetry appear to influence COMd, and RFD asymmetry appears to show the most differentiability between groups in terms of COMd ML. Kinetic asymmetry may result in undesirable displacement of athletes’ COM during jumping

Affinity and dose of TCR engagement yield proportional enhancer and gene activity in CD4+ T cells.

Affinity and dose of T cell receptor (TCR) interaction with antigens govern the magnitude of CD4+ T cell responses, but questions remain regarding the quantitative translation of TCR engagement into downstream signals. We find that while the response of mouse CD4+ T cells to antigenic stimulation is bimodal, activated cells exhibit analog responses proportional to signal strength. Gene expression output reflects TCR signal strength, providing a signature of T cell activation. Expression changes rely on a pre-established enhancer landscape and quantitative acetylation at AP-1 binding sites. Finally, we show that graded expression of activation genes depends on ERK pathway activation, suggesting that an ERK-AP-1 axis plays an important role in translating TCR signal strength into proportional activation of enhancers and genes essential for T cell function

LEG DYNAMIC STRENGTH PREDICTORS OF A PRE-PLANNED CHANGE OF DIRECTION TASK IN NCAA DIVISION I SOCCER PLAYERS

The purpose of this investigation was to evaluate the relationships between two types of vertical jumps and change of direction (COD) test in collegiate soccer players (n=24). 5-5 COD test was utilized to measure soccer athletes’ COD ability. 3m acceleration (3mAcc), Total time (TT) and Partial time (PT) were measured by two sets of timing gates. Countermovement jump (CMJ) and static jump (SJ) with 2 different loading conditionings (0kg and 20kg) were employed to evaluate athletes’ leg dynamic strength. Strong statistically significant relationships were found between COD test variables (r =0.71 to 0.90), and between vertical jump variables with PT and TT (r = -0.41 to -0.81). These results suggest that leg dynamic strength is vital for NCAA Division I soccer players’ COD performance and SJ 0kg jump height can be used to predict for COD performance