The contribution associated with pests to international natrual enviroment deadwood decomposition

This paper investigates the association between consecutive ambient air pollution and Chronic Obstructive Pulmonary Disease (COPD) hospitalization in Chengdu China. The three-year (2015-2017) time series data for both ambient air pollutant concentrations and COPD hospitalizations in Chengdu are approved for the study. The big data statistic analysis shows that Air Quality Index (AQI) exceeded the lighted air polluted level in Chengdu region are mainly attributed to particulate matters (i.e., PM2.5 and PM10). The time series study for consecutive ambient air pollutant concentrations reveal that AQI, PM2.5, and PM10 are significantly positive correlated, especially when the number of consecutive polluted days is greater than nine days. The daily COPD hospitalizations for every 10 μg/m3 increase in PM2.5 and PM10 indicate that consecutive ambient air pollution can lead to an appearance of an elevation of COPD admissions, and also present that dynamic responses before and after the peak admission are different. Support Vector Regression (SVR) is then used to describe the dynamics of COPD hospitalizations to consecutive ambient air pollution. These findings will be further developed for region specific, hospital early notifications of COPD in responses to consecutive ambient air pollution.Unfractionated heparin (UFH) is commonly used in the intensive care unit (ICU) to prevent blood clotting. Recently, many researchers focus on the development of data- driven methods to solve UFH related problems, which usually involves time series analysis. The performance of data-driven methods depends on whether the inter-correlation of attributes (or variables) in the dataset is closely examined and addressed. This study performs attribute selection, optimal time delay and inter-attributes relations on ICU time series data, in order to provide insights of time series data for UFH related problems. Medical records of 3211 patients with 22 attributes extracted from MIMIC (Medical Information Mart for Intensive Care) III database are used for the experiment. Experimental result shows that some of commonly selected attributes in the literature are less sensitive to the variations of UFH injection. Furthermore, some attributes are inter-dependent, which can increase the complexity of data-driven models, implying that the number of attributes could be reduced. There are 9 attributes found highly related and fast responding in 22 commonly used attributes. This study shows strong potential to provide clinicians with information about sensitive attributes that can help determine the UFH injection policy in ICU.We developed a method of estimating impactors of cognitive function (ICF) - such as anxiety, sleep quality, and mood - using computational voice analysis. Clinically validated questionnaires (VQs) were used to score anxiety, sleep and mood while salient voice features were extracted to train regression models with deep neural networks. Experiments with 203 subjects showed promising results with significant concordance correlation coefficients (CCC) between actual VQ scores and the predicted scores (0.46 = anxiety, 0.50 = sleep quality, 0.45 = mood).A large amount of data including joint kinematics, joint kinetics, clinical and functional measurements constitutes the clinical gait analysis basis which is a process whereby quantitative gait information are collected to aid in clinical decision-making. Therefore, better understanding the relationship between the biomechanical and clinical data for the knee osteoarthritis (OA) patient is for a relevant importance. It's the purpose of this paper, which aims to analyze and visualize the correlation structure between biomechanical characteristics and clinical symptoms, and thus to provide an additional knowledge from the coupling of these parameters that will be useful for the pathology assessment of knee-joint disease in the end-staged knee OA patients. We perform two multivariate statistical approaches, first, a Canonical Correlation Analysis (CCA) to assess the multivariate association and, second, a graphical- based representation of the multivariate correlation to better understand the association between these multivariate data. Results show the usefulness of using such multivariate approaches to highlight association and specific correlation structure between the features and to extract meaningful information.This paper proposes the fusion of data from unobtrusive sensing solutions for the recognition and classification of activities in home environments. AM580 supplier The ability to recognize and classify activities can help in the objective monitoring of health and wellness trends in ageing adults. While the use of video and stereo cameras for monitoring activities provides an adequate insight, the privacy of users is not fully protected (i.e., users can easily be recognized from the images). Another concern is that widely used wearable sensors, such as accelerometers, have some disadvantages, such as limited battery life, adoption issues and wearability. This study investigates the use of low-cost thermal sensing solutions capable of generating distinct thermal blobs with timestamps to recognize the activities of study participants. More than 11,000 thermal blobs were recorded from 10 healthy participants with two thermal sensors placed in a laboratory kitchen (i) one mounted on the ceiling, and (ii) the other positioned on a mini tripod stand in the corner of the room. Furthermore, data from the ceiling thermal sensor were fused with data gleaned from the lateral thermal sensor. Contact sensors were used at each stage as the gold standard for timestamp approximation during data acquisition, which allowed the attainment of (i) the time at which each activity took place, (ii) the type of activity performed, and (iii) the location of each participant. Experimental results demonstrated successful cluster-based activity recognition and classification with an average regression co-efficient of 0.95 for tested clusters and features. Also, an average accuracy of 95% was obtained for data mining models such as k-nearest neighbor, logistic regression, neural network and random forest on Evaluation Test.Clinical Relevance-This study presents an unobtrusive (i.e., privacy-friendly) solution for activity recognition and classification, for the purposes of profiling trends in health and wellbeing.