Outlier analysis of sloshing impact loads on liquid ship cargo

This paper introduces an outlier analysis which can improve the convergence of the statistical analysis results of sloshing model test data. The paper classify possible outliers in the sloshing model test into three categories and present a treatment method for each outlier. The developed outlier analysis is adapted to the model test results for the cargo of the liquefied-natural-gas (LNG) carrier in operation. The results of the present new method are compared with those of the conventional procedure, particularly focusing on long-term sloshing prediction. Through this study, the effectiveness of the present method is observed, and it is found that the present method provides is robust and reliable results in the application of experimental data for load prediction.

A Spatial Analysis of Robbery Rate in the City of Detroit using Exploratory Data Analysis Approach

Many U.S. cities have experienced rising crime rates in recent years. Crime has inherent geographic quality and tend to concentrate in certain places within the city. To prioritize public safety and crime prevention strategies, it is important to identify where crime is occurring and with what severity. Using spatial statistics including the average nearest neighbour index, Moran’s I, Getis-Ord Gi* statistic, and Anselin Cluster and Outlier Analysis, this study investigates robbery locations within the city of Detroit over 5-year period, 2016 to 2020 to identify hot spots, cold spots and spatial patterns across two different spatial scale – block group and census tracts. The study seeks to understand the effect of data aggregation on each spatial scale on the outcome of the analysis to determine the most optimum spatial scale to study robbery rates. The study concludes that, spatial analysis at small scale like block group level is most informative. Policy implications and areas for further research are provided.

Genetic stability of Aedes aegypti populations following invasion by wMel Wolbachia

Background Wolbachia wMel is the most commonly used strain in rear and release strategies for Aedes aegypti mosquitoes that aim to inhibit the transmission of arboviruses such as dengue, Zika, Chikungunya and yellow fever. However, the long-term establishment of wMel in natural Ae. aegypti populations raises concerns that interactions between Wolbachia wMel and Ae. aegypti may lead to changes in the host genome, which could affect useful attributes of Wolbachia that allow it to invade and suppress disease transmission. Results We applied an evolve-and-resequence approach to study genome-wide genetic changes in Ae. aegypti from the Cairns region, Australia, where Wolbachia wMel was first introduced more than 10 years ago. Mosquito samples were collected at three different time points in Gordonvale, Australia, covering the phase before (2010) and after (2013 and 2018) Wolbachia releases. An additional three locations where Wolbachia replacement happened at different times across the last decade were also sampled in 2018. We found that the genomes of mosquito populations mostly remained stable after Wolbachia release, with population differences tending to reflect the geographic location of the populations rather than Wolbachia infection status. However, outlier analysis suggests that Wolbachia may have had an influence on some genes related to immune response, development, recognition and behavior. Conclusions Ae. aegypti populations remained geographically distinct after Wolbachia wMel releases in North Australia despite their Wolbachia infection status. At some specific genomic loci, we found signs of selection associated with Wolbachia, suggesting potential evolutionary impacts can happen in the future and further monitoring is warranted.

Crime Analysis and Prediction Using Data Mining and Machine Learning Techniques

With the increase in crime rates across the world, it has become important for the Government and crime handling agencies to control the situation as it has put every person in distress. This paper is an attempt to systematically analyze and identify the crime trends across the years, the inter-state relations based on crime rates and categories through the data available, which will help in predicting the crime trends in future and will be instrumental for the Government to take informed actions and improve the country’s situation. This paper applies various data mining techniques in order to analyze the crime records in India. The results of analysis have been compared for various algorithms in the domain of Association Rule Mining, Clustering, Outlier Analysis, Regression and Classification. The paper also attempts to predict the future occurrences of crimes using classification and regression algorithms which use data mining techniques . Keywords: Crime Analysis, Data Mining, Association Rule Mining, Clustering, outlier Analysis, Classification, Regression

Multivariate Outlier Detection in Precipitation Series by Using Two-Dimensional Correlation

The accuracy of descriptive statistics might be influenced by the existence of outliers in data sets. An observation which might not be considered as an outlier in the univariate case might be a multivariate outlier. Therefore, determination of outliers might make multivariate analysis more robust by providing an opportunity for making required corrections before modelling studies. This paper presents the implementation of the two-dimensional correlation method in the determination of multivariate outliers among the observations of six precipitation stations in Turkey. The two-dimensional correlation method considers the averages of the parts of the whole series instead of the average of the whole series and enables determination of the location of the outlier in the compared series. The obtained results point out that an outlier analysis for hydrologic variables should consider the two-directional behavior and the presented two-dimensional correlation method proves to be a strong alternative to be used in outlier and irregularity detection studies even with a limited number of available data. The 2DCorr software used in the study is freely provided as a supplementary material.

Combining outlier analysis algorithms to identify new physics at the LHC

The lack of evidence for new physics at the Large Hadron Collider so far has prompted the development of model-independent search techniques. In this study, we compare the anomaly scores of a variety of anomaly detection techniques: an isolation forest, a Gaussian mixture model, a static autoencoder, and a β-variational autoencoder (VAE), where we define the reconstruction loss of the latter as a weighted combination of regression and classification terms. We apply these algorithms to the 4-vectors of simulated LHC data, but also investigate the performance when the non-VAE algorithms are applied to the latent space variables created by the VAE. In addition, we assess the performance when the anomaly scores of these algorithms are combined in various ways. Using super- symmetric benchmark points, we find that the logical AND combination of the anomaly scores yielded from algorithms trained in the latent space of the VAE is the most effective discriminator of all methods tested.

Genetic Stability of Aedes aegypti Populations following Invasion by wMel Wolbachia

BackgroundWolbachia wMel is the most used strain in mosquito rear and release strategies that aim to inhibit the transmission of arboviruses such as dengue, Zika, Chikungunya and yellow fever. However, the long-term establishment of wMel in natural populations of the dengue mosquito Aedes aegypti raises concerns that interactions between Wolbachia wMel and Ae. aegypti may lead to changes in the host genome, which could affect useful attributes of Wolbachia that allow it to invade and suppress disease transmission. ResultsWe applied an evolve-and-resequence approach to study genome-wide genetic changes in Ae. aegypti from the Cairns region, Australia, where Wolbachia wMel was first introduced more than 10 years ago. Mosquito samples were collected at three different time points in Gordonvale, Australia, covering the phase before (2010) and after (2013 and 2018) Wolbachia releases. An additional three locations where Wolbachia replacement happened at different times across the last decade were also sampled in 2018. We found that the genomes of mosquito populations mostly remained stable after Wolbachia release, with population differences tending to reflect the geographic location of the populations rather than Wolbachia infection status. However, outlier analysis suggests that Wolbachia may have had an influence on some genes related to immune response, development, recognition and behavior. ConclusionsAe. aegypti populations remained geographically distinct after Wolbachia wMel releases in North Australia despite their Wolbachia infection status. At some specific genomic loci, we found signs of selection associated with Wolbachia, suggesting potential evolutionary impacts can happen in the future and further monitoring is warranted.

Genetic stability of Aedes aegypti populations following invasion by wMel Wolbachia

Background: Wolbachia wMel play a major role in mosquito rear and release strategies that aim to inhibit the transmission of arboviruses such as dengue, Zika, Chikungunya and yellow fever. However, the long-term establishment of wMel in natural populations of the dengue mosquito Aedes aegypti raises concerns that interactions between Wolbachia wMel and Ae. aegypti may lead to changes in the host genome, which could affect useful attributes of Wolbachia that allow it to invade and suppress disease transmission. Results: We applied an evolve-and-resequence approach to study genome-wide genetic changes in Ae. aegypti from the Cairns region, Australia, where Wolbachia wMel was first introduced more than 10 years ago. Mosquito samples were collected at three different time points in Gordonvale, Australia, covering the phase before (2010) and after (2013 and 2018) Wolbachia releases; an additional three locations where Wolbachia replacement happened at different times across the last decade were also sampled in 2018. We found that in general, the genome of mosquito populations mostly remained stable after Wolbachia release, with any population differences tending to reflect the geographic location of the populations rather than Wolbachia infection status. Whereas on the other hand, outlier analysis suggested that Wolbachia may have had an influence on some genes related to immune response, development, recognition and behaviour. Conclusions: In general, Ae. aegypti populations remained geographically distinct after Wolbachia releases in North Australia despite their Wolbachia infection status. Whereas at some specific genomic loci, we found signs of selection associated with Wolbachia, suggesting potential evolutionary impacts can happen in the future and further monitoring is warranted.