The Birmingham Urban Climate Laboratory: an open meteorological test bed and challenges of the smart city

Existing urban meteorological networks have an important role to play as test beds for inexpensive and more sustainable measurement techniques that are now becoming possible in our increasingly smart cities. The Birmingham Urban Climate Laboratory (BUCL) is a near-real-time, high-resolution urban meteorological network (UMN) of automatic weather stations and inexpensive, nonstandard air temperature sensors. The network has recently been implemented with an initial focus on monitoring urban heat, infrastructure, and health applications. A number of UMNs exist worldwide; however, BUCL is novel in its density, the low-cost nature of the sensors, and the use of proprietary Wi-Fi networks. This paper provides an overview of the logistical aspects of implementing a UMN test bed at such a density, including selecting appropriate urban sites; testing and calibrating low-cost, nonstandard equipment; implementing strict quality-assurance/quality-control mechanisms (including metadata); and utilizing preexisting Wi-Fi networks to transmit data. Also included are visualizations of data collected by the network, including data from the July 2013 U.K. heatwave as well as highlighting potential applications. The paper is an open invitation to use the facility as a test bed for evaluating models and/or other nonstandard observation techniques such as those generated via crowdsourcing techniques.

Providing extrinsic reward for test performance undermines long-term memory acquisition

Based on numerous studies showing that testing studied material can improve long-term retention more than restudying the same material, it is often suggested that the number of tests in education should be increased to enhance knowledge acquisition. However, testing in real-life educational settings often entails a high degree of extrinsic motivation of learners due to the common practice of placing important consequences on the outcome of a test. Such an effect on the motivation of learners may undermine the beneficial effects of testing on long-term memory because it has been shown that extrinsic motivation can reduce the quality of learning. To examine this issue, participants learned foreign language vocabulary words, followed by an immediate test in which one-third of the words were tested and one-third restudied. To manipulate extrinsic motivation during immediate testing, participants received either monetary reward contingent on test performance or no reward. After 1 week, memory for all words was tested. In the immediate test, reward reduced correct recall and increased commission errors, indicating that reward reduced the number of items that can benefit from successful retrieval. The results in the delayed test revealed that reward additionally reduced the gain received from successful retrieval because memory for initially successfully retrieved words was lower in the reward condition. However, testing was still more effective than restudying under reward conditions because reward undermined long-term memory for concurrently restudied material as well. These findings indicate that providing performance–contingent reward in a test can undermine long-term knowledge acquisition.

Wheat seed embryo excision enables the creation of axenic seedlings and Koch’s postulates testing of putative bacterial endophytes

Early establishment of endophytes can play a role in pathogen suppression and improve seedling development. One route for establishment of endophytes in seedlings is transmission of bacteria from the parent plant to the seedling via the seed. In wheat seeds, it is not clear whether this transmission route exists, and the identities and location of bacteria within wheat seeds are unknown. We identified bacteria in the wheat (Triticum aestivum) cv. Hereward seed environment using embryo excision to determine the location of the bacterial load. Axenic wheat seedlings obtained with this method were subsequently used to screen a putative endophyte bacterial isolate library for endophytic competency. This absence of bacteria recovered from seeds indicated low bacterial abundance and/or the presence of inhibitors. Diversity of readily culturable bacteria in seeds was low with 8 genera identified, dominated by Erwinia and Paenibacillus. We propose that anatomical restrictions in wheat limit embryo associated vertical transmission, and that bacterial load is carried in the seed coat, crease tissue and endosperm. This finding facilitates the creation of axenic wheat plants to test competency of putative endophytes and also provides a platform for endophyte competition, plant growth, and gene expression studies without an indigenous bacterial background.

Consensus Statement: Chromosomal Microarray Is a First-Tier Clinical Diagnostic Test for Individuals with Developmental Disabilities or Congenital Anomalies

Chromosomal microarray (CMA) is increasingly utilized for genetic testing of individuals with unexplained developmental delay/intellectual disability (DD/ID), autism spectrum disorders (ASD), or multiple congenital anomalies (MCA). Performing CMA and G-banded karyotyping on every patient substantially increases the total cost of genetic testing. The International Standard Cytogenomic Array (ISCA) Consortium held two international workshops and conducted a literature review of 33 studies, including 21,698 patients tested by CMA. We provide an evidence-based summary of clinical cytogenetic testing comparing CMA to G-banded karyotyping with respect to technical advantages and limitations, diagnostic yield for various types of chromosomal aberrations, and issues that affect test interpretation. CMA offers a much higher diagnostic yield (15%-20%) for genetic testing of individuals with unexplained DD/ID, ASD, or MCA than a G-banded karyotype (similar to 3%, excluding Down syndrome and other recognizable chromosomal syndromes), primarily because of its higher sensitivity for submicroscopic deletions and duplications. Truly balanced rearrangements and low-level mosaicism are generally not detectable by arrays, but these are relatively infrequent causes of abnormal phenotypes in this population (<1%). Available evidence strongly supports the use of CMA in place of G-banded karyotyping as the first-tier cytogenetic diagnostic test for patients with DD/ID, ASD, or MCA. G-banded karyotype analysis should be reserved for patients with obvious chromosomal syndromes (e.g., Down syndrome), a family history of chromosomal rearrangement, or a history of multiple miscarriages.

Statistical analysis of proficiency testing results under elliptical distributions

The use of inter-laboratory test comparisons to determine the performance of individual laboratories for specific tests (or for calibration) [ISO/IEC Guide 43-1, 1997. Proficiency testing by interlaboratory comparisons - Part 1: Development and operation of proficiency testing schemes] is called Proficiency Testing (PT). In this paper we propose the use of the generalized likelihood ratio test to compare the performance of the group of laboratories for specific tests relative to the assigned value and illustrate the procedure considering an actual data from the PT program in the area of volume. The proposed test extends the test criteria in use allowing to test for the consistency of the group of laboratories. Moreover, the class of elliptical distributions are considered for the obtained measurements. (C) 2008 Elsevier B.V. All rights reserved.

Hypotheses Testing on a Multivariate Null Intercept Errors-in-Variables Model

Considering the Wald, score, and likelihood ratio asymptotic test statistics, we analyze a multivariate null intercept errors-in-variables regression model, where the explanatory and the response variables are subject to measurement errors, and a possible structure of dependency between the measurements taken within the same individual are incorporated, representing a longitudinal structure. This model was proposed by Aoki et al. (2003b) and analyzed under the bayesian approach. In this article, considering the classical approach, we analyze asymptotic test statistics and present a simulation study to compare the behavior of the three test statistics for different sample sizes, parameter values and nominal levels of the test. Also, closed form expressions for the score function and the Fisher information matrix are presented. We consider two real numerical illustrations, the odontological data set from Hadgu and Koch (1999), and a quality control data set.

Comparing finite state machine test coverage criteria

To plan testing activities, testers face the challenge of determining a strategy, including a test coverage criterion that offers an acceptable compromise between the available resources and test goals. Known theoretical properties of coverage criteria do not always help and, thus, empirical data are needed. The results of an experimental evaluation of several coverage criteria for finite state machines (FSMs) are presented, namely, state and transition coverage; initialisation fault and transition fault coverage. The first two criteria focus on FSM structure, whereas the other two on potential faults in FSM implementations. The authors elaborate a comparison approach that includes random generation of FSM, construction of an adequate test suite and test minimisation for each criterion to ensure that tests are obtained in a uniform way. The last step uses an improved greedy algorithm.

Fault Coverage-Driven Incremental Test Generation

In this paper, we consider a classical problem of complete test generation for deterministic finite-state machines (FSMs) in a more general setting. The first generalization is that the number of states in implementation FSMs can even be smaller than that of the specification FSM. Previous work deals only with the case when the implementation FSMs are allowed to have the same number of states as the specification FSM. This generalization provides more options to the test designer: when traditional methods trigger a test explosion for large specification machines, tests with a lower, but yet guaranteed, fault coverage can still be generated. The second generalization is that tests can be generated starting with a user-defined test suite, by incrementally extending it until the desired fault coverage is achieved. Solving the generalized test derivation problem, we formulate sufficient conditions for test suite completeness weaker than the existing ones and use them to elaborate an algorithm that can be used both for extending user-defined test suites to achieve the desired fault coverage and for test generation. We present the experimental results that indicate that the proposed algorithm allows obtaining a trade-off between the length and fault coverage of test suites.

Improving data perturbation testing techniques for Web services

The widespread use of service-oriented architectures (SOAs) and Web services in commercial software requires the adoption of development techniques to ensure the quality of Web services. Testing techniques and tools concern quality and play a critical role in accomplishing quality of SOA based systems. Existing techniques and tools for traditional systems are not appropriate to these new systems, making the development of Web services testing techniques and tools required. This article presents new testing techniques to automatically generate a set of test cases and data for Web services. The techniques presented here explore data perturbation of Web services messages upon data types, integrity and consistency. To support these techniques, a tool (GenAutoWS) was developed and applied to real problems. (C) 2010 Elsevier Inc. All rights reserved.

A modified signed likelihood ratio test in elliptical structural models

In this paper we deal with the issue of performing accurate testing inference on a scalar parameter of interest in structural errors-in-variables models. The error terms are allowed to follow a multivariate distribution in the class of the elliptical distributions, which has the multivariate normal distribution as special case. We derive a modified signed likelihood ratio statistic that follows a standard normal distribution with a high degree of accuracy. Our Monte Carlo results show that the modified test is much less size distorted than its unmodified counterpart. An application is presented.

Improved testing inference in mixed linear models

Mixed linear models are commonly used in repeated measures studies. They account for the dependence amongst observations obtained from the same experimental unit. Often, the number of observations is small, and it is thus important to use inference strategies that incorporate small sample corrections. In this paper, we develop modified versions of the likelihood ratio test for fixed effects inference in mixed linear models. In particular, we derive a Bartlett correction to such a test, and also to a test obtained from a modified profile likelihood function. Our results generalize those in [Zucker, D.M., Lieberman, O., Manor, O., 2000. Improved small sample inference in the mixed linear model: Bartlett correction and adjusted likelihood. Journal of the Royal Statistical Society B, 62,827-838] by allowing the parameter of interest to be vector-valued. Additionally, our Bartlett corrections allow for random effects nonlinear covariance matrix structure. We report simulation results which show that the proposed tests display superior finite sample behavior relative to the standard likelihood ratio test. An application is also presented and discussed. (C) 2008 Elsevier B.V. All rights reserved.

Testing hypotheses in the Birnbaum-Saunders distribution under type-II censored samples

The two-parameter Birnbaum-Saunders distribution has been used successfully to model fatigue failure times. Although censoring is typical in reliability and survival studies, little work has been published on the analysis of censored data for this distribution. In this paper, we address the issue of performing testing inference on the two parameters of the Birnbaum-Saunders distribution under type-II right censored samples. The likelihood ratio statistic and a recently proposed statistic, the gradient statistic, provide a convenient framework for statistical inference in such a case, since they do not require to obtain, estimate or invert an information matrix, which is an advantage in problems involving censored data. An extensive Monte Carlo simulation study is carried out in order to investigate and compare the finite sample performance of the likelihood ratio and the gradient tests. Our numerical results show evidence that the gradient test should be preferred. Further, we also consider the generalized Birnbaum-Saunders distribution under type-II right censored samples and present some Monte Carlo simulations for testing the parameters in this class of models using the likelihood ratio and gradient tests. Three empirical applications are presented. (C) 2011 Elsevier B.V. All rights reserved.

Methods for Equivalence and Noninferiority Testing

Classical hypothesis testing focuses on testing whether treatments have differential effects on outcome. However, sometimes clinicians may be more interested in determining whether treatments are equivalent or whether one has noninferior outcomes. We review the hypotheses for these noninferiority and equivalence research questions, consider power and sample size issues, and discuss how to perform such a test for both binary and survival outcomes. The methods are illustrated on 2 recent studies in hematopoietic cell transplantation.

Hypothesis testing in an errors-in-variables model with heteroscedastic measurement errors

In many epidemiological studies it is common to resort to regression models relating incidence of a disease and its risk factors. The main goal of this paper is to consider inference on such models with error-prone observations and variances of the measurement errors changing across observations. We suppose that the observations follow a bivariate normal distribution and the measurement errors are normally distributed. Aggregate data allow the estimation of the error variances. Maximum likelihood estimates are computed numerically via the EM algorithm. Consistent estimation of the asymptotic variance of the maximum likelihood estimators is also discussed. Test statistics are proposed for testing hypotheses of interest. Further, we implement a simple graphical device that enables an assessment of the model`s goodness of fit. Results of simulations concerning the properties of the test statistics are reported. The approach is illustrated with data from the WHO MONICA Project on cardiovascular disease. Copyright (C) 2008 John Wiley & Sons, Ltd.

Residual-based test for Nonlinear Cointegration with application in PPPs

Nested by linear cointegration first provided in Granger (1981), the definition of nonlinear cointegration is presented in this paper. Sequentially, a nonlinear cointegrated economic system is introduced. What we mainly study is testing no nonlinear cointegration against nonlinear cointegration by residual-based test, which is ready for detecting stochastic trend in nonlinear autoregression models. We construct cointegrating regression along with smooth transition components from smooth transition autoregression model. Some properties are analyzed and discussed during the estimation procedure for cointegrating regression, including description of transition variable. Autoregression of order one is considered as the model of estimated residuals for residual-based test, from which the teststatistic is obtained. Critical values and asymptotic distribution of the test statistic that we request for different cointegrating regressions with different sample sizes are derived based on Monte Carlo simulation. The proposed theoretical methods and models are illustrated by an empirical example, comparing the results with linear cointegration application in Hamilton (1994). It is concluded that there exists nonlinear cointegration in our system in the final results.