Different Patterns of Pulse Spectrum Between Survivors and Non-survivors During Progressive Hemorrhage in Rats

2006 ◽  
Vol 34 (04) ◽  
pp. 575-589 ◽  
Author(s):  
Yu Hsin Chang ◽  
Jui Shan Lin ◽  
Jaung Geng Lin ◽  
Yue Der Lin ◽  
Tsai Chung Li ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Blood Volume ◽  
Linear Regression Model ◽  
Pulse Spectrum ◽  
Acute Hemorrhage ◽  
Arterial Blood ◽  
Study Results ◽  
Significant Difference ◽  
Harmonic Proportion

Previous work from our laboratory has demonstrated that the percentage differences of 2nd (C2) and 3rd (C3) pulse harmonics related to Kidney and Spleen were both increased toward another steady state in rats after acute hemorrhage. Therefore, it is suggested that changes in pulse spectra might represent the ability of animals to survive a model of progressive hemorrhage. In this study, the difference of the pulse spectra patterns between survivors and non-survivors after progressive hemorrhage (by loss of 5%, 10% or 20% of the estimated blood volume) in anesthetized rats is determined. Seven rats, dead within 2 hours after a loss of 20% of the estimated blood volume hemorrhage, were defined as 'non-survivors'. The other eleven rats, more than 2 hours after hemorrhage, were defined as 'survivors'. Pulse waves of arterial blood pressure before and after the hemorrhage were measured in parallel to the pulse spectrum analysis. Data among different phases were analyzed using one-way analysis of variance (ANOVA) with Duncan's test for pairwise comparisons. Differences between survivor and non-survivor groups at each phase were analyzed using Student's t-test. A mixed-effects linear regression model was applied to evaluate the relationship in harmonics, which significantly differed between the two groups. The study results showed that in rats, during progressive hemorrhage, the percentage differences of 2nd harmonic proportion increased significantly; however, the result failed to show any significant difference between survivors and non-survivors. After the third blood withdrawal process, the percentage differences of 3rd harmonic proportion increased more significantly in the survivors. In addition, the percentage differences of 1st harmonic proportion related to the Liver for the survivor group was significantly lower than that of the non-survivors. After analysis with the mixed linear regression model, C3 and C1 demonstrated a linear regression relationship, and there existed significant differences between survivors and non-survivors. These results suggest that C3 might play an important role in physiology regarding surviving capability after progressive hemorrhage.

scholarly journals Cognitive processes affect the gait of subjects with Parkinson’s and Alzheimer’s disease in dual tasks

2015 ◽  
Vol 64 (2) ◽  
pp. 154-159
Author(s):  
Gustavo Christofoletti ◽  
Lílian Assunção Felippe ◽  
Paulo de Tarso Müller ◽  
Fernanda Beinotti ◽  
Guilherme Borges
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Linear Regression ◽  
Regression Model ◽  
Cognitive Processes ◽  
Dual Task ◽  
Linear Regression Model ◽  
Cognitive Functions ◽  
Dual Tasks ◽  
Significant Difference

Objective To investigate the relation between gait parameters and cognitive impairments in subjects with Parkinson’s disease (PD) and Alzheimer’s disease (AD) during the performance of dual tasks. Methods This was a cross-sectional study involving 126 subjects divided into three groups: Parkinson group (n = 43), Alzheimer group (n = 38), and control group (n = 45). The subjects were evaluated using the Timed Up and Go test administered with motor and cognitive distracters. Gait analyses consisted of cadence and speed measurements, with cognitive functions being assessed by the Brief Cognitive Screening Battery and the Clock Drawing Test. Statistical procedures included mixed-design analyses of variance to observe the gait patterns between groups and tasks and the linear regression model to investigate the influence of cognitive functions in this process. A 5% significant level was adopted. Results Regarding the subjects’ speed, the data show a significant difference between group vs task interaction (p = 0.009), with worse performance of subjects with PD in motor dual task and of subjects with AD in cognitive dual task. With respect to cadence, no statistical differences was seen between group vs task interaction (p = 0.105), showing low interference of the clinical conditions on such parameter. The linear regression model showed that up to 45.79%, of the variance in gait can be explained by the interference of cognitive processes. Conclusion Dual task activities affect gait pattern in subjects with PD and AD. Differences between groups reflect peculiarities of each disease and show a direct interference of cognitive processes on complex tasks.

scholarly journals Is COVID-19 a Real Incentive for Flu Vaccination? Let the Numbers Speak for Themselves

Vaccines ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 276
Author(s):  
Marcello Di Pumpo ◽  
Giuseppe Vetrugno ◽  
Domenico Pascucci ◽  
Elettra Carini ◽  
Viria Beccia ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Teaching Hospital ◽  
Vaccination Coverage ◽  
Linear Regression Model ◽  
Healthcare Workers ◽  
Surveys And Questionnaires ◽  
Flu Vaccination ◽  
Significant Difference ◽  
Seasonal Flu

Seasonal flu vaccination is one of the most important strategies for preventing influenza. The attitude towards flu vaccination in light of the COVID-19 pandemic has so far been studied in the literature mostly with the help of surveys and questionnaires. Whether a person chooses to be vaccinated or not during the COVID-19 pandemic, however, speaks louder than any declaration of intention. In our teaching hospital, we registered a statistically significant increase in flu vaccination coverage across all professional categories between the 2019/2020 and the 2020/2021 campaign (24.19% vs. 54.56%, p < 0.0001). A linear regression model, based on data from four previous campaigns, predicted for the 2020/2021 campaign a total flu vaccination coverage of 30.35%. A coverage of 54.46% was, instead, observed, with a statistically significant difference from the predicted value (p < 0.0001). The COVID-19 pandemic can, therefore, be considered as an incentive that significantly and dramatically increased adherence to flu vaccination among our healthcare workers.

scholarly journals Pulmonary nodules volumetry. Variability in Results After Manual Correction of Contours

2019 ◽  
Vol 100 (2) ◽  
pp. 74-81
Author(s):  
E. V. Rozengauz ◽  
D. V. Nesterov ◽  
Z. A. Al’derov ◽  
N. M. Korablin
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Pulmonary Nodules ◽  
Manual Segmentation ◽  
Adjacent Structures ◽  
Significant Difference ◽  
Vascular Structures ◽  
The Impact ◽  
Manual Correction

Objective. To study variability of volumetric of the pulmonary nodules volumetry after manual correction of their contours.Material and methods. Twenty-seven nodules uncircumscribed from the vascular structures and pleura were selected. A linear regression model was used to investigate the impact of the size of a nodule, the area of its contact with the adjacent structures on variability in results.Results. The linear regression model based on contact area and nodule size can correctly predict volumetry variability.Conclusion. Even after manual segmentation volumetry remain suitable method for size assessment of lung nodules. Segmentation should be made with the same person because of significant difference of interobserver and intraobserver variabilities.

scholarly journals Soft Computing Predictions of Relative Energy Dissipation for Vertical Drops Equipped with a Horizontal Screen

2020 ◽  
Author(s):  
Reza Norouzi ◽  
Rasoul Daneshfaraz ◽  
John Abraham ◽  
Parveen Sihag
Keyword(s):  
Energy Dissipation ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Absolute Error ◽  
Sensitivity Study ◽  
Relative Energy ◽  
Study Results ◽  
Input Variables

Drops are the most important and most common energy dissipator in irrigation networks and erodible canals and consequently, their performance must be well understood. This study was designed to evaluate the capability of Artificial Intelligence (AI) methods including ANN, ANFIS, GRNN, SVM, GP, MLR, and LR to predict the relative energy dissipation (∆E/E0) in vertical drops equipped with a horizontal screen. For this study, 108 experiments were carried out to investigate energy dissipation with variable discharge, varying drop height, and porosity of the horizontal screens. Parameters yc/h, yd/yc, and p are considered as input variables and ∆E/E0 is the output variable. The efficiency of models was compared using Taylor's diagram, Box Plot of the applied error distribution, correlation coefficient (CC), mean absolute error (MAE) and root-mean-square error (RMSE). Results indicate that the performance of the ANFIS_gbellmf based model with CC value of 0.9953, RMSE value of 0.0069 and MAE value of 0.0042 was superior to other applied models. Also, the linear regression model with CC=0.9933, RMSE=0.0083, and MAE= 0.0067performs better than the multiple linear regression model in this study. Results of a sensitivity study suggest that yc/h is the most effective parameter for predicting ∆E/E0.

Physico-Chemical Analysis of Groundwater and Effect of Climatic Change in Angul-Talcher Area

2018 ◽  
Vol 6 (12) ◽  
pp. 26
Author(s):  
Aliva Bera ◽  
D.P. Satapathy
Keyword(s):  
Water Resources ◽  
Linear Regression ◽  
Regression Model ◽  
Climatic Change ◽  
Linear Regression Model ◽  
Ann Model ◽  
Quality Of Water ◽  
Physico Chemical ◽  
Simulation Based

In this paper, the linear regression model using ANN and the linear regression model using MS Excel were developed to estimate the physico-chemical concentrations in groundwater using pH, EC, TDS, TH, HCO3 as input parameters and Ca, Mg and K as output parameters. A comparison was made which indicated that ANN model had the better ability to estimate the physic-chemical concentrations in groundwater. An analytical survey along with simulation based tests for finding the climatic change and its effect on agriculture and water bodies in Angul-Talcher area is done. The various seasonal parameters such as pH, BOD, COD, TDS,TSS along with heavy elements like Pb, Cd, Zn, Cu, Fe, Mn concentration in water resources has been analyzed. For past 30 years rainfall data has been analyzed and water quality index values has been studied to find normal and abnormal quality of water resources and matlab based simulation has been done for performance analysis. All results has been analyzed and it is found that the condition is stable. 

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

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