Bovine tuberculosis (bTB) is prevalent among livestock and wildlife in many countries including New Zealand (NZ), a country which aims to eradicate bTB by 2055. This study evaluates predictions related to the numbers of livestock herds with bTB in NZ from 2012 to 2021 inclusive using both statistical and mechanistic (causal) modelling. Additionally, this study made predictions for the numbers of infected herds between 2022 and 2059. This study introduces a new graphical method representing the causal criteria of strength of association, such as R2, and the consistency of predictions, such as mean squared error. Mechanistic modelling predictions were, on average, more frequently (3 of 4) unbiased than statistical modelling predictions (1 of 4). Additionally, power model predictions were, on average, more frequently (3 of 4) unbiased than exponential model predictions (1 of 4). The mechanistic power model, along with annual updating, had the highest R2 and the lowest mean squared error of predictions. It also exhibited the closest approximation to unbiased predictions. Notably, significantly biased predictions were all underestimates. Based on the mechanistic power model, the biological eradication of bTB from New Zealand is predicted to occur after 2055. Disease eradication planning will benefit from annual updating of future predictions.

This paper proposes a methodology to define and quantify the precision uncertainties in aerothermodynamic cycle model comparisons. The total uncertainty depends on biases and random errors commonly found in such comparisons. These biases and random errors are classified and discussed based on observations found in the literature. The biases account for effects such as differences in model inputs, the configurations being simulated, and thermodynamic packages. Random errors consider the effects on the physics modeling and numerical methods used in cycle models. The methodology is applied to a comparison of two cycle models, designated as the model subject to comparison and reference model, respectively. The former is the so-called Aerothermodynamic Generic Cycle Model developed in-house at the Laboratory of Applied Research in Active Control, Avionics and AeroServoElasticity (LARCASE); the latter is an equivalent model programmed in the Numerical Propulsion System Simulation (NPSS). The proposed methodology is intended to quantify the bias and random errors effects on different cycle parameters of interest, such as thrust, specific fuel consumption, among others. Each bias and random errors are determined by deliberately preventing the effects from other biases and random errors. The methodology presented in this paper can be extended to other cycle model comparisons. Moreover, the uncertainty figures derived in this work are recommended to be used in other model comparisons when no better reference is available.

Determination of sample size (the number of replications) is a key step in the design of an observational study or randomized experiment. Statistical procedures for this purpose are readily available. Their treatment in textbooks is often somewhat marginal, however, and frequently the focus is on just one particular method of inference (significance test, confidence interval). Here, we provide a unified review of approaches and explain their close interrelationships, emphasizing that all approaches rely on the standard error of the quantity of interest, most often a pairwise difference of two means. The focus is on methods that are easy to compute, even without a computer. Our main recommendation based on standard errors is summarized as what we call the 1-2-3 rule for a difference of two treatment means.

Location and navigation services based on global navigation satellite systems (GNSS) are needed for real-time high-precision positioning applications in relevant economic sectors, such as precision agriculture, transport, civil engineering or mapping. Real-time navigation users of GNSS networks have significantly increased all around the world, since the 1990s, and usage has exceeded initial expectations. Therefore, if the evolution of GNSS network users is monitored, the dynamics of market segments can be studied. The implementation of this hypothesis requires the treatment of big volumes of navigation data over several years and the continuous monitoring of customers. This paper is focused on the management of massive connection of GNSS users in an efficient way, in order to obtain analysis and statistics. Big data architecture and data analyses based on data mining algorithms have been implemented as the best way to approach the hypothesis. Results demonstrate the dynamic of users of different market segments, the increasing demand over the years and, specifically, conclusions are obtained about the trends, year-on-year correlation and business volume recovering after economic crisis periods.

Atom probe tomography (APT) is a technique that has expanded significantly in terms of adoption, dataset size, and quality during the past 15 years. The sophistication used to ensure ultimate analysis precision has not kept pace. The earliest APT datasets were small enough that deadtime and background considerations for processing mass spectrum peaks were secondary. Today, datasets can reach beyond a billion atoms so that high precision data processing procedures and corrections need to be considered to attain reliable accuracy at the parts-per-million level. This paper considers options for mass spectrum ranging, deadtime corrections, and error propagation as applied to an extrinsic-silicon standard specimen to attain agreement for silicon isotopic fraction measurements across multiple instruments, instrument types, and acquisition conditions. Precision consistent with those predicted by counting statistics is attained showing agreement in silicon isotope fraction measurements across multiple instruments, instrument platforms, and analysis conditions.

This article explores how Qin Dynasty bureaucrats attained accuracy and precision in producing and designing measuring containers. One of the salient achievements of the Qin empire was the so-called unification of measurement systems. Yet measurement systems and the technological methods employed to achieve accuracy and precision in ancient China have scarcely been explored in English-language scholarship. I will examine the material features of the containers and reconstruct the production methods with which the clay models, molds, and cores of the containers were prepared before casting. I also investigate the inscriptions on the containers to determine whether they were cast or engraved. In so doing, I supply the field of Qin history with additional solid evidence about how accuracy and precision were defined in the Qin empire.

The use of local knowledge observations to generate empirical wildlife resource exploitation data in data-poor, capacity-limited settings is increasing. Yet, there are few studies quantitatively examining their relationship with those made by researchers or natural resource managers. We present a case study comparing intra-annual patterns in effort and mobulid ray (Mobula spp.) catches derived from local knowledge and fisheries landings data at identical spatiotemporal scales in Zanzibar (Tanzania). The Bland–Altman approach to method comparison was used to quantify agreement, bias and precision between methods. Observations from the local knowledge of fishers and those led by researchers showed significant evidence of agreement, demonstrating the potential for local knowledge to act as a proxy, or complement, for researcher-led methods in assessing intra-annual patterns of wildlife resource exploitation. However, there was evidence of bias and low precision between methods, undermining any assumptions of equivalency. Our results underline the importance of considering bias and precision between methods as opposed to simply assessing agreement, as is commonplace in the literature. This case study demonstrates the value of rigorous method comparison in informing the appropriate use of outputs from different knowledge sources, thus facilitating the sustainable management of wildlife resources and the livelihoods of those reliant upon them.

We undertook a strengths, weaknesses, opportunities, and threats (SWOT) analysis of Northern Hemisphere tree-ring datasets included in IntCal20 in order to evaluate their strategic fit with the demands of archaeological users. Case studies on wiggle-matching single tree rings from timbers in historic buildings and Bayesian modeling of series of results on archaeological samples from Neolithic long barrows in central-southern England exemplify the archaeological implications that arise when using IntCal20. The SWOT analysis provides an opportunity to think strategically about future radiocarbon (14C) calibration so as to maximize the utility of 14C dating in archaeology and safeguard its reputation in the discipline.