Integrating Project-based Activity in Improving the Science Academic Performance of Learners in the New Normal

The objective of this paper was to determine the efficacy of Project-based learning in increasing the academic performance of learners in remote learning amidst the pandemic. There were eight (8) Project-based instructional activities across six (6) most essential learning competencies in Earth Science were included in the Grade 9 Unit three Earth Science. The computed t value of 5.08 is greater than the t critical value of 2.0049 at level of significance of 0.05, the statistical decision is to reject the null hypothesis. There is a significant difference between the pre-test and post-test of the respondents who were subjected to project-based activity. Result implied that there is enough evidence to support the claim that integrating project-based activity can effectively improve the academic performance of the respondents. Based on the analysis gathered by the researcher, the use of project-based activity in on-line teaching is highly effective in improving the academic performance of students in Earth Science. Therefore, I, the researcher, recommends the use of project-based method of teaching in the on-line distance learning.

Analysis of distribution laws of transformer oil indicators in 110-330 kV transformers

Introduction. Ensuring the operational reliability of power transformers is an urgent task for the power industry in Ukraine and for most foreign countries. One of the ways to solve this problem is the correction of maximum permissible values of insulation parameters. However, such a correction is fundamentally impossible without an analysis of the laws of distribution of diagnostic indicators in the equipment with different states. The purpose of the research is to analyse the laws of distribution of the quality indicators of transformer oil with different states in 110 and 330 kV transformers. Novelty. It was found that both 330 kV autotransformers and 110 kV transformers have the displacements between the mathematical expectations of the distribution density of usable oil indicators. It caused by different service life of the analysed transformers and different values of load factors. This indicates the need to consider the influence of these factors when correcting the maximum permissible values of oil indicators. Also, the presence of displacement between the distribution densities of some indicators of usable oil in 110 kV transformers and 330 kV autotransformers has been revealed. It indicates a different intensity of oxidation reactions in transformers with different voltage class. In order to reduce the heterogeneity of initial data the procedure of statistical processing of in-service test results has been proposed as a method. This procedure combines the use of a priori information about the service life of equipment and values of load factors with the elements of statistical hypothesis testing. The results of the analysis of the distribution laws of transformer oil indicators with different states have shown that for both usable and unusable oil the values of oil indicators obey the Weibull distribution. Values of the shape and scale parameters for each of the obtained indices arrays have been obtained, as well as calculated and critical values of the goodness-of-fit criteria. Practical value. Obtained values of the distribution law parameters of the transformer oil indicators with different states, considering the service life and operating conditions allow to perform the correction of the maximum permissible values of the indicators using the statistical decision-making methods.

Developing Strategies for Onchocerciasis Elimination Mapping and Surveillance Through The Diagnostic Network Optimization Approach

BackgroundOnchocerciasis (river blindness) is a filarial disease targeted for elimination of transmission. However, challenges exist to the implementation of effective diagnostic and surveillance strategies at various stages of elimination programs. To address these challenges, we used a network data analytics approach to identify optimal diagnostic scenarios for onchocerciasis elimination mapping (OEM).MethodsThe diagnostic network optimization (DNO) method was used to model the implementation of the old Ov16 rapid diagnostic test (RDT) and of new RDTs in development for OEM under different testing strategy scenarios with varying testing locations, test performance and disease prevalence. Environmental suitability scores (ESS) based on machine learning algorithms were developed to identify areas at risk of transmission and used to select sites for OEM in Bandundu region in the Democratic Republic of Congo (DRC) and Uige province in Angola. Test sensitivity and specificity ranges were obtained from the literature for the existing RDT, and from characteristics defined in the target product profile for the new RDTs. Sourcing and transportation policies were defined, and costing information was obtained from onchocerciasis programs. Various scenarios were created to test various state configurations. The actual demand scenarios represented the disease prevalence at IUs according to the ESS, while the counterfactual scenarios (conducted only in the DRC) are based on adapted prevalence estimates to generate prevalence close to the statistical decision thresholds (5% and 2%), to account for variability in field observations. The number of correctly classified implementation units (IUs) per scenario were estimated and key cost drivers were identified.ResultsIn both Bandundu and Uige, the sites selected based on ESS had high predicted onchocerciasis prevalence >10%. Thus, in the actual demand scenarios in both Bandundu and Uige, the old Ov16 RDT correctly classified all 13 and 11 IUs, respectively, as requiring CDTi. In the counterfactual scenarios in Bandundu, the new RDTs with higher specificity correctly classified IUs more cost effectively. The new RDT with highest specificity (99.8%) correctly classified all 13 IUs. However, very high specificity (e.g., 99.8%) when coupled with imperfect sensitivity, can result in many false negative results (missing decisions to start MDA) at the 5% statistical decision threshold (the decision rule to start MDA). This effect can be negated by reducing the statistical decision threshold to 2%. Across all scenarios, the need for second stage sampling significantly drove program costs upwards. The best performing testing strategies with new RDTs were more expensive than testing with existing tests due to need for second stage sampling, but this was offset by the cost of incorrect classification of IUs.ConclusionThe new RDTs modelled added most value in areas with variable disease prevalence, with most benefit in IUs that are near the statistical decision thresholds. Based on the evaluations in this study, DNO could be used to guide the development of new RDTs based on defined sensitivities and specificities. While test sensitivity is a minor driver of whether an IU is identified as positive, higher specificities are essential. Further, these models could be used to explore the development and optimization of new tools for other neglected tropical diseases.

Sequential statistical decision making in problems of data flows analysis

In the problems of data flows analysis, the problems of statistical decision making on parameters of observed data flows are important. For their solution it is proposed to use sequential statistical decision rules. The rules are constructed for three models of observation flows: sequence of independent homogeneous observations; sequence of observations forming a time series with a trend; sequence of dependent observations forming a homogeneous Markov chain. For each case the situation is considered, where the model describes the observed stochastic data with a distortion. "Outliers" ("contamination") are used as the admissible distortions that adequately describe the majority of situations appear in practice. For such situations the families of sequential decision rules are proposed, and robust decision rules are constructed that allow to reduce influence of distortion to the efficiency characteristics. The results of computer experiments are given to illustrate the constructed decision rules.

Investigating preferences in art collecting: the case of the François Pinault Collection

This article focuses on private art collections that play a relevant role on the art market while reducing its information asymmetry. Knowledge of how art consumers such as private art collectors show preferences for specific artworks may allow to identify collecting patterns based on the preference of some artworks’ signs. Understanding these patterns is essential for evaluating the impact of art collectors on the art market. The evolution of the art market shows complex consumption systems that shape the cognition and behavior of actors such as private art collectors. Consequently, to be a key art collector and to progress as such in today’s art world implies a constant reinterpretation about what it means to consume and to collect art. This paper explores the collection of one of the most important art collectors in the world, the French tycoon François Pinault. More precisely, his background as a key collector was examined, and a number of preferences toward particular signs which connote his collected artworks were identified. All the collected artworks were observed through a descriptive data analysis of the Pinault Collection’s exhibition catalogues, published from 2006 to 2015, enforced by the statistical decision tree classifier. Results show how the Pinault Collection is shaped by collecting preferences that can be described as collecting patterns. As a preeminent collector and owner of one of the two major auction houses in the world, Pinault’s consumption preferences and decisions may impact the art market, for instance through signals and by influencing other art market players or the artists’ careers.

How to Analyze the Diagnostic Performance of a New Test? Explained with Illustrations

Diagnostic tests are pivotal in modern medicine due to their applications in statistical decision-making regarding confirming or ruling out the presence of a disease in patients. In this regard, sensitivity and specificity are two most important and widely utilized components that measure the inherent validity of a diagnostic test for dichotomous outcomes against a gold standard test. Other diagnostic indices like positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, accuracy of a diagnostic test, and the effect of prevalence on various diagnostic indices have also been discussed. We have tried to present the performance of a classification model at all classification thresholds by reviewing the receiver operating characteristic (ROC) curve and the depiction of the tradeoff between sensitivity and (1–specificity) across a series of cutoff points when the diagnostic test is on a continuous scale. The area under the ROC (AUROC) and comparison of AUROCs of different tests have also been discussed. Reliability of a test is defined in terms of the repeatability of the test such that the test gives consistent results when repeated more than once on the same individual or material, under the same conditions. In this article, we have presented the calculation of kappa coefficient, which is the simplest way of finding the agreement between two observers by calculating the overall percentage of agreement. When the prevalence of disease in the population is low, prospective study becomes increasingly difficult to handle through the conventional design. Hence, we chose to describe three more designs along with the conventional one and presented the sensitivity and specificity calculations for those designs. We tried to offer some guidance in choosing the best possible design among these four designs, depending on a number of factors. The ultimate aim of this article is to provide the basic conceptual framework and interpretation of various diagnostic test indices, ROC analysis, comparison of diagnostic accuracy of different tests, and the reliability of a test so that the clinicians can use it effectively. Several R packages, as mentioned in this article, can prove handy during quantitative synthesis of clinical data related to diagnostic tests.

Measure Transportation and Statistical Decision Theory

Unlike the real line, the real space, in dimension d ≥ 2, is not canonically ordered. As a consequence, extending to a multivariate context fundamental univariate statistical tools such as quantiles, signs, and ranks is anything but obvious. Tentative definitions have been proposed in the literature but do not enjoy the basic properties (e.g., distribution-freeness of ranks, their independence with respect to the order statistic, their independence with respect to signs) they are expected to satisfy. Based on measure transportation ideas, new concepts of distribution and quantile functions, ranks, and signs have been proposed recently that, unlike previous attempts, do satisfy these properties. These ranks, signs, and quantiles have been used, quite successfully, in several inference problems and have triggered, in a short span of time, a number of applications: fully distribution-free testing for multiple-output regression, MANOVA, and VAR models; R-estimation for VARMA parameters; distribution-free testing for vector independence; multiple-output quantile regression; nonlinear independent component analysis; and so on. Expected final online publication date for the Annual Review of Statistics, Volume 9 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.