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Our proposed method focuses on offline friends who are acquainted with a target user in the real world. We use Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to distinguish between online friends of a target user who are also offline friends and online friends who are acquainted with the target user only in cyberspace. By utilizing the probability of friendship as a function of the geographical distance between users’ home locations, our proposed method achieves an estimation accuracy 1.7 percentage point better than that of a conventional method. We have also been able to verify the effectiveness of an optimization method that narrows down candidate areas for a target user’s home location.

Purpose: The aim of this study was to determine the influence of utilization of online past examination papers on academic performance of IT students at Jomo Kenyatta University.Methodology: Descriptive survey research design was used in this study. Respondents were the 105 undergraduate students in Information Technology Department and 2 librarians in Jomo Kenyatta University of Agriculture and Technology-Eldoret Campus. They were sampled using stratified sampling, while Head of library and the library staff in charge of e-resources in JKUAT Eldoret Campus were purposively included in the study. Primary data was collected from students using closed-ended questionnaires, while interview guide was used on librarians. To ensure validity and reliability, pre-testing of research instruments was done on 20 undergraduate students of Mount Kenya university-Eldoret Campus in the department of Information Technology. D Descriptive statistics such as mean, percentage, frequencies and standard deviation, SPSS (version 22) was used in analysing data. Univariate and multiple regression analysis were used to test the hypothesis and overall model respectively. The results were presented using tables and explanations.Results: There is a positive and significant relationship between online past examination papers and academic performance of IT undergraduate students of Jomo Kenyatta University of Agriculture and Technology - Eldoret Campus. The study had a normal mean of 4.7 and standard deviation of 0.58. The online past examination papers had R estimation of 0.715. The P estimation of constant was significant (.000), consequently R square value was used. The R square estimation of 0.711 inferred that online past assessment papers anticipated 71.1% of the fluctuation in the academic performance. It had a beta of 0.504 at p

An interpretable convolutional neural network (CNN) including attribute estimation for image classification is presented in this paper. Although CNNs perform highly accurate image classification, the reason for the classification results obtained by the neural networks is not clear. In order to provide interpretation of CNNs, the proposed method estimates attributes, which explain elements of objects, in an intermediate layer of the network. This enables improvement of the interpretability of CNNs, and it is the main contribution of this paper. Furthermore, the proposed method uses the estimated attributes for image classification in order to enhance its accuracy. Consequently, the proposed method not only provides interpretation of CNNs but also realizes improvement in the performance of image classification.

The generation of waste by the paper industry has attracted great attention over the last decades among other reasons because the demand for recycled waste paper has considerably increased. As the paper industry is closely intertwined to the rest of industries in the production system, its activity exerts both a direct and indirect influence on the volume of waste generated by its supplier industries. The purpose of this study is to shed some light on the evolution of the volume of waste generated by the suppliers of the Spanish paper industry over the period 2005–2010 using an Economic Input–Output Life Cycle Assessment Model. In particular we focus on the evolution of the volume of waste generated by firms of the own paper industry. We employ data from different waste surveys conducted by the Spanish National Statistics Institute and input–output tables extracted from the World Input–Output Database. The results obtained show that the waste generated by suppliers amounted to 1250 thousand tonnes in 2010, an important volume if we take into account that the waste generated by the paper industry in 2010 amounted to 1739 thousand tonnes. The analysis of the evolution of the waste generated by suppliers reveals that there is a high degree of concentration, both in terms of industries and in terms of waste categories. In addition, the decrease in the volume of waste generated by supplier firms within the own paper industry reflects not only the growing importance of recycled paper as raw material for paper-making but also the important investments in technology made by this industry in Spain.

To review the highest impact studies published from the UK Biobank and assess their contributions to "precision medicine." We reviewed 140 of 689 studies published between 2008 and May 2019 from the UK Biobank deemed to be high impact by citations, alternative metric data, or publication in a high impact journal. We classified studies according to whether they (1) were largely methods papers, (2) largely replicated prior findings or associations, (3) generated novel findings or associations, (4) developed risk prediction models that did not yield clinically significant improvements in risk estimation over prior models or (5) developed models that produced significant improvements in individualized risk assessment, targeted screening, or targeted treatment. This final category represents "precision medicine." We classified 15 articles as category 1, 33 as category 2, 85 as category 3, six as category 4, and one as category 5. In this assessment of the first 7 years of the UK Biobank and first 4 years of genetic data availability, the majority of high impact UK Biobank studies either replicated known associations or generated novel associations without clinically relevant improvements in risk prediction, screening, or treatment. This information may be useful for designers of other cohort studies in terms of input to design and follow-up to facilitate precision medicine research.

How best to estimate the accuracy of a predictive rule has been a longstanding question in statistics. Approaches to this task range from simple methods like Mallow’s Cp to algorithmic techniques l...

OBJECTIVE To evaluate reporting of items indicative of bias and weak study design. STUDY DESIGN Literature survey. POPULATION Papers published in Veterinary Anaesthesia and Analgesia. METHODS Reporting of randomization, blinding, sample size estimation and data exclusion were compared for papers published separated by a 10 year interval. A reporting rate of more than 95% was considered ideal. The availability of data supporting results in a publicly accessible repository was also assessed. Selected papers were randomized and identifiers removed for review, with data from 59 (57 in 2009, two in 2008) and 56 (52 in 2019, four in 2018) papers analyzed. Items were categorized for completeness of reporting using a previously published operationalized checklist. Two reviewers reviewed all papers independently. RESULTS Full reporting of randomization increased over time from 13.6% to 85.7% [95% confidence interval (CI), 57.8-86.6%; p < 0.0001], as did sample size estimation (from 0% to 20%; 95% CI, 7.6-32.4%; p = 0.002). Reporting of blinding (49.2% and 50.0%; 95% CI, -18.3% to 20.0%; p = 1.0) and exclusions of samples/animals (39.0% and 50.0%; 95% CI, -8.8% to 30.8%; p = 0.3) did not change significantly. Data availability was low (2008/2009, zero papers; 2018/2019, two papers). None of the items studied exceeded the predetermined ideal reporting rate. CONCLUSIONS AND CLINICAL RELEVANCE These results indicate that reporting quality remains low, with a risk of bias.

This dissertation includes three chapters that cover broad topics in economics. The first chapter explores how the US Government's Capital Purchase Program, a large capital injection to local and regional banks through a stock purchase agreement, impacted local establishment dynamics such as entry, exit, employment expansion, and employment contraction following the 2008 Financial Crisis. The Capital Purchase Program dispersed over \$200 billion dollars to banks hoping to prevent failure and ease tightened lending conditions. I estimate the direct effects of a county having a bank receive Capital Purchase Program funds on local business dynamics in the seven years following treatment, as well as spillover effects as entrepreneurs and business in neighboring regions travel to gain access to credit. Estimates show the CPP had no effect on establishment entry and exit, nor employment expansion and contraction. This paper establishes that the business-lending aims of the CPP were not realized in the communities and regions that received funds, and casts further doubt on meaningful pass through of CPP funds to desirable local economic activity.The second chapter develops a joint hypothesis centered Wald test over fixed effects in large N small T panel data models with symmetric serial correlation within cross sectional observations. The enables joint hypothesis tests over inconsistently estimated fixed effects, such as the traditional varying intercept model as well as models with individual specific slope coefficients. I establish two different set of assumptions where feasible tests exist. The first assumption requires that individual errors follow a stationary $\ARp$ process. Under this assumption all second and fourth cross product moments can be consistently estimated while allowing for individual specific hypothesis and covariates to vary across individuals and time with individual specific slopes. The second feasible test requires individuals to have coefficient slopes that are shared among all individuals in a known grouping structure under the null. This set of assumptions enables estimation of a completely unconstrained variance-covariance matrix and higher cross product moments for individuals. Examples of these tests arise in wanting to establish latent panel structure, such as unobserved grouping of individuals, wanting to compare different models of teacher or firm value added against each other, or testing whether or not fixed effects can be approximated by Mundlak-Chamberlain devices.Finally, the third paper estimates how messages displaced on Dynamic Message Boards, large signs either adjacent to or displayed above roads, impact near to sign accidents. In this research, I look at the traffic-related messages such as ``drive sober,'' ``x deaths on roads this year,'' and ``click it or ticket,'' displayed on major highways, on reported near-to-sign traffic accidents. This provides estimates of the impact of different types of nudges on road safety behavior. To estimate the causal effect of these nudges, we build a new high-frequency panel data set using the information on the time and location of messages, crashes, overall traffic levels, and weather conditions using the data of the state of Vermont over a three year time period. I estimate models that control for endogeneity of displayed messages, or allow for spillover effects from neighboring messages.

We enjoyed reading Professor Efron’s (Brad) paper just as much as we enjoyed listening to his June 2019 lecture in Leiden. One of the core values underlying statistical research is in how it enable...

The new pandemic disease caused by COVID-19 virus is the crucial event over the world in the beginning of 2020. Studies on corona viruses have been however carried since several decades ago, with recent research papers published on weekly basis. We demonstrate a simple approach to explore CORD-19 dataset to provide a high level overview of important semantic changes that occurred over time. Our method aims to support better understanding of large domain-specific collections of scholarly publications that span long time periods and could be regarded as complementary to frequency-based analysis.

In this study, the role of turbulence-chemistry interaction in diesel spray auto-ignition, flame stabilization and end of injection phenomena is investigated under engine relevant “Spray A” conditions. A recently developed diesel spray combustion modeling approach, Conditional Source-term Estimation (CSE-FGM), is coupled with Reynolds-averaged Navier-Stokes simulation (RANS) framework to study the details of spray combustion. The detailed chemistry mechanism is included through the Flamelet Generated Manifold (FGM) method. Both unsteady and steady flamelet solutions are included in the manifold to account for the auto-ignition process and the subsequent flame propagation in a diesel spray. Conditionally averaged chemical source terms are closed by the conditional scalars obtained in the CSE routine. Both non-reacting and reacting spray jets are computed over a wide range of Engine Combustion Network (ECN) diesel. “Spray A” conditions. The reacting spray results are compared with simulations using a homogeneous reactor combustion model and a flamelet combustion model with the same chemical mechanism. The present study represents the first application of CSE for a diesel spray. The non-reacting liquid/vapour penetration, the mean and RMS mixture fraction, the reactive region, the flame lift-off and the ignition delay show a good agreement with literature data from an optically accessible combustion vessel over a wide range of tested conditions. The CSE-FGM model also shows a better capability in predicting the end-of-injection events in diesel spray combustion. Overall, the CSE-FGM model is shown to capture the experimental trends well, both quantitatively and qualitatively.

In this article, we discuss how we can advance our knowledge and estimation of the distribution of temperature across urban regions by going beyond the commonly known urban heat islands (UHI) phenomena, which identify the temperature difference between a city and its rural surrounding. Transit heat island (THI) focuses more on the transit routes, which are among the hottest intra-urban land covers. We also discuss new efficient and effective means of collecting data with enough spatio-temporal resolution to model and analyze THI with various smart city applications in mind.

I review my scientific career in terms of eight areas and 16 papers. The first two areas are associated with childhood. The other six are associated with residual maximum likelihood (REML), canonical transformation, inbreeding in selected populations, average information residual maximum likelihood (AIREML), the computer program ASReml and sampling-based estimation.

The data presented here are manually encoded characteristics of research papers in the area of Energy-Economy Nexus (empirical investigation of Granger causality between energy consumption and economic growth) that describe the methods, samples, and other details related to the individual estimations done in the examined empirical papers. Data cover papers indexed by Scopus, published in economic journals, written in English, after year 2000. In addition, papers were manually filtered to only those that deal with Energy-Economy Nexus investigation and have at least 10 citations at (at the time of query – November 2015). This data are to be used to conduct meta-analysis – associated dataset was used in Hajko [1] . Early version of the dataset was used for multinomial logit estimation in Master thesis by Kocianova [2] .

Academic papers cite other literature for various purposes: such as sharing research motivations, comparing with previous methods or achieved accuracies, and so on. At some annual conference, high impact papers have been selected and honored after about 10 years from the publication to demonstrate both the emphasis research field and great contributions. This paper has proposed the estimation method of high impact papers using characteristics of those citing purposes. To extract the cited structure of scientific literature, we analyze the paper structure based on purpose and identify the citation points in the paper. For evaluating the proposed method, 17 years long proceedings on VLDB (Very Large Data Bases) Conference and "10 Year Award" papers are applied. We believe that the research becomes a foothold for estimating papers that may have a significant impact on the academic field in the future.

The usage of psychological networks that conceptualize psychological behavior as a complex interplay of psychological and other components has gained increasing popularity in various fields of psychology. While prior publications have tackled the topics of estimating and interpreting such networks, little work has been conducted to check how accurate (i.e., prone to sampling variation) networks are estimated, and how stable (i.e., interpretation remains similar with less observations) inferences from the network structure (such as centrality indices) are. In this tutorial paper, we aim to introduce the reader to this field and tackle the problem of accuracy under sampling variation. We first introduce the current state-of-the-art of network estimation. Second, we provide a rationale why researchers should investigate the accuracy of psychological networks. Third, we describe how bootstrap routines can be used to (A) assess the accuracy of estimated network connections, (B) investigate the stability of centrality indices, and (C) test whether network connections and centrality estimates for different variables differ from each other. We introduce two novel statistical methods: for (B) the correlation stability coefficient, and for (C) the bootstrapped difference test for edge-weights and centrality indices. We conducted and present simulation studies to assess the performance of both methods. Finally, we developed the free R-package bootnet that allows for estimating psychological networks in a generalized framework in addition to the proposed bootstrap methods. We showcase bootnet in a tutorial, accompanied by R syntax, in which we analyze a dataset of 359 women with posttraumatic stress disorder available online., Comment: Accepted for publication in Behavior Research Methods

IntroductionDecreasing participation levels in health surveys pose a threat to the validity of estimates intended to be representative of their target population. If participants and non-participants differ systematically, the results may be biased. The application of traditional non-response adjustment methods, such as weighting, can fail to correct for such biases, as estimates are typically based on the sociodemographic information available. Therefore, a dedicated methodology to infer on non-participants offers advancement by employing survey data linked to administrative health records, with reference to data on the general population. We aim to validate such a methodology in a register-based setting, where individual-level data on participants and non-participants are available, taking alcohol consumption estimation as the exemplar focus.Methods and analysisWe made use of the selected sample of the Health 2000 survey conducted in Finland and a separate register-based sample of the contemporaneous population, with follow-up until 2012. Finland has nationally representative administrative and health registers available for individual-level record linkage to the Health 2000 survey participants and invited non-participants, and the population sample. By comparing the population sample and the participants, synthetic observations representing the non-participants may be generated, as per the developed methodology. We can compare the distribution of the synthetic non-participants with the true distribution from the register data. Multiple imputation was then used to estimate alcohol consumption based on both the actual and synthetic data for non-participants, and the estimates can be compared to evaluate the methodology’s performance.Ethics and disseminationEthical approval and access to the Health 2000 survey data and data from administrative and health registers have been given by the Health 2000 Scientific Advisory Board, Statistics Finland and the National Institute for Health and Welfare. The outputs will include two publications in public health and statistical methodology journals and conference presentations.

In controlling uncertain processes, it is decisive to utilize information provided by measurements in order to estimate parameters and states. Nonlinear Model Predictive Control (NMPC) is a popular method to implement feedback control and deal with uncertainties. Conventional NMPC or nominal control, however, sometimes does not provide enough information for system estimation, leading to unsatisfactory performance. Dual control attempts to strike a balance between the two goals of enhancing system estimation and optimizing the nominal objective function. In this paper, we analyze the performance of these strategies through the interplay between the performance control task and the information gain task in connection with Optimal Experimental Design. Examples illustrate the conflict and agreement between the two tasks and explain why in some cases nominal control performs well. It is also observed that measurement noise provides excitation helping to improve the quality of estimates.

The measurement uncertainty characterizes the dispersion of the quantity values being attributed to the measurand and there are different approaches for uncertainty estimation. This study illustrates the application of the GUM (bottom-up) approach to estimate the measurement results uncertainty for the quantitative determination of Al, Ba, Fe, Mg, Mn, Pb, Sr and Zn from document paper samples using ICP-MS. The measurement uncertainty estimation was done based on identifying, quantifying and combining all the associated sources of uncertainty separately. Certain typical steps were followed: specifying the measurand; identifying the major sources of uncertainty; quantifying the uncertainty components; combining the significant uncertainty components; determining the extended combined standard uncertainty; reviewing the estimates and reporting the measurement uncertainty. For the eight mentioned trace elements the combined standard uncertainties and the expanded uncertainties were determined. The relative measurement uncertainty values lay between 7.7% and 13.6%. In all the five paper samples for each of the eight elements homogenous uncertainty values were obtained. In order to emphasize the uncertainty sources contributions, the percent contribution of the uncertainty components to the combined relative standard uncertainty were graphically represented for the elements determined by ICP-MS in paper samples. The previously validated method proved to be suitable for the intended purpose and when the uncertainty of the measurement results is estimated, it becomes a significant tool for characterizing the elemental composition of the document paper samples. Moreover, the applied approach for the uncertainty estimation enables improving the data quality and decision making.