Time Varying Spatio-temporal Covariance Models

In this paper, we introduce valid parametric covariance models for univariate and multivariate spatio-temporal random fields. In contrast to the traditional models, we allow the model parameters to vary over time. Since variables in applications usually exhibit seasonality or changes in dependency structures, the allowance of varying parameters would be beneficial in terms of improving model flexibility. Conditions in constructing valid covariance models and discussions on practical implementation will be provided. As an application, a set of air pollution data observed from a monitoring network will be modeled. It is found that the time varying model performs better in prediction compared with the traditional models. © 2015 Elsevier Ltd.postprin

Seemingly unrelated intervention time series models for effectiveness evaluation of large scale environmental remediation

Large scale environmental remediation projects applied to sea water always involve large amount of capital investments. Rigorous effectiveness evaluations of such projects are, therefore, necessary and essential for policy review and future planning. This study aims at investigating effectiveness of environmental remediation using three different Seemingly Unrelated Regression (SUR) time series models with intervention effects, including Model (1) assuming no correlation within and across variables, Model (2) assuming no correlation across variable but allowing correlations within variable across different sites, and Model (3) allowing all possible correlations among variables (i.e., an unrestricted model). The results suggested that the unrestricted SUR model is the most reliable one, consistently having smallest variations of the estimated model parameters. We discussed our results with reference to marine water quality management in Hong Kong while bringing managerial issues into consideration. © 2013 Elsevier Ltd

Actinomycosis: An often forgotten diagnosis

We report a case of actinomycosis presenting as a chest wall mass in a 35 year-old man. Thoracic actinomycosis poses a diagnostic challenge to clinicians not only because it is uncommon and often forgotten, but also because culture of the causative microbes is technically difficult. The classic microscopic appearance of this Gram-positive bacteria associated with surrounding sulfur granules often forms the basis of diagnosis.link_to_subscribed_fulltex

A Hybrid Convolutional Neural Network for Complex Leaves Identification

The classification of leaves has gained popularity through the years, and a great variety of algorithms has been created to target these tasks, among those is the Deep Learning approach, which simplicity of learning from raw imputed data makes this task easy to target. However, not all methods are into the complex leaves classification task. In this work we propose a different approach in the way the leaf’s pictures are used to train the models, this is done by using the front and back face of a leaf as one element of the dataset. These pairs will be inputted into two shared convolutional layers, making the models to learn from a complete leaf. The results obtained in this work overpassed the accuracy obtained in related works. For this, we created a new complex leaves dataset, that consists of 6 different kinds of peach varieties, the dataset is available in this link (https://drive.google.com/drive/folders/1rWCr9DrknoK0HKFhNRavCVgZ5UKjU3hi)