scholarly journals Correlation between Spectral Characteristics and Physicochemical Parameters of Soda-Saline Soils in Different States

2019 ◽  
Vol 11 (4) ◽  
pp. 388 ◽  
Author(s):  
Xiaojie Li ◽  
Jianhua Ren ◽  
Kai Zhao ◽  
Zhengwei Liang
Keyword(s):  
Physicochemical Parameters ◽  
Regression Models ◽  
Spectral Characteristics ◽  
Electronic Conductivity ◽  
Salt Content ◽  
Surface States ◽  
Absolute Error ◽  
Saline Soils ◽  
Spectral Features ◽  
Linear Regression Models

The spectral features of soils are a comprehensive representation of their physicochemical parameters, surface states, and internal structures. To date, spectral measurements have been mostly performed for powdered soils and smooth aggregate soils, but rarely for cracked soils; a common state of soda-saline soils. In this study, we measured the spectral features of 57 saline soil samples in powdered, aggregate, and cracked states for comparison. We then explored in depth the factors governing soil spectral features to build up simple and multiple linear regression models between the spectral features and physicochemical parameters (salt content, Na+, pH, and electronic conductivity (EC)) of saline soils in different states. We randomly selected 40 samples to construct the models, and used the remaining 17 samples for validation. Our results indicated that the regression models worked more effectively in predicting physicochemical parameters for cracked soils than for other soils. Subsequently, the crack ratio (CR) was introduced into the regression models to modify the spectra of soils in powdered and aggregate states. The accuracy of prediction was improved, evidenced by a 2–11% decrease in the parameters mean absolute error (MAE).

scholarly journals Perceptual and Acoustic Characteristics of Speech Clarity with and Without a Facemask

2021 ◽  
Author(s):  
Duy Duong Nguyen ◽  
Antonia Chacon ◽  
Christopher Payten ◽  
Rebecca Black ◽  
Meet Sheth ◽  
...  
Keyword(s):  
High Frequency ◽  
Regression Models ◽  
Spectral Characteristics ◽  
Mask Condition ◽  
Linear Regression Models ◽  
Root Mean Square Amplitude ◽  
Acoustic Characteristics ◽  
Care Workers ◽  
Surgical Masks ◽  
Speech Clarity

Abstract Some studies have found that the speech of speakers wearing facemasks has reduced intelligibility. Although it has been found that facemasks attenuated high-frequency energy, no study has examined the effects of masks on spectral characteristics of vowels or voiceless fricative consonants. The present study investigated auditory perceptual rating of speech clarity and acoustic-phonetic measures of vowels and voiceless fricative consonant production in 16 health care workers who produced standardized voice tasks without and with wearing either a standard surgical mask or a KN95 mask. Voice samples were perceptually rated for speech clarity and were acoustically analysed for root-mean-square amplitude (ARMS), spectral moments of two voiceless fricatives /s/ and /ʃ/, and ARMS and amplitude of the first three formants (A1, A2, and A3). Speech produced whilst wearing either a surgical or KN95 mask was significantly less clear than without a mask, with KN95 showing greater impact than surgical masks. In both fricatives, ARMS was lower in the surgical mask and KN95 mask conditions compared to the non-mask condition. None of the amplitude measures of vowels were affected by facemasks. Linear regression models indicated that perception of speech produced by mask users was mainly affected by modification of voiceless fricative consonant characteristics.

scholarly journals Environment-wide association study (EWAS) on cardiometabolic traits: A systematic assessment of the association of lifestyle variables on a longitudinal setting

2021 ◽  
Author(s):  
Alaitz Poveda ◽  
Yan Chen ◽  
Hugo Pomares-Millan ◽  
Azra Kurbasic ◽  
Chirag J Patel ◽  
...  
Keyword(s):  
Physical Activity ◽  
Mixed Models ◽  
Regression Models ◽  
Leisure Time ◽  
Leisure Time Physical Activity ◽  
Salt Content ◽  
Linear Regression Models ◽  
Association Analyses ◽  
Systematic Assessment ◽  
Cardiometabolic Traits

AbstractThe present study aims to assess the over-time association of ∼300 lifestyle exposures with nine cardiometabolic traits with the ultimate aim of identifying exposures/exposure groups that could inform lifestyle interventions aiming at controlling cardiometabolic diseases. The analyses were undertaken in a longitudinal sample comprising >31000 adults living in northern Sweden. Linear mixed models were used to assess the average associations of lifestyle exposures and linear regression models were used to test association with 10-year change of the cardiometabolic traits. ‘Physical activity’ and ‘General Health’ were the exposure categories containing the highest number of ‘tentative signals’ in analyses assessing the average association of lifestyle variables, while ‘Tobacco use’ was the top-category for the 10-year change association analyses. Thirteen modifiable variables showed a consistent average association among the majority of cardiometabolic traits. These variables belonged to four main groups: i) Smoking, ii) Diet (secoisolariciresinol intake and brewed coffee), iii) Leisure time physical activity and iv) a group of variables more specific to the Swedish lifestyle (snuff status, hunting/fishing during leisure time and boiled coffee). Interestingly, sweet drinks, fish intake and salt content, all lifestyle exposures frequently mentioned in public health recommendations were not broadly associated with the analysed cardiometabolic traits.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

Linear Regression Models for Interval-Valued Data using Log-transformation

2020 ◽  
Author(s):  
Nykolas Mayko Maia Barbosa ◽  
João Paulo Pordeus Gomes ◽  
César Lincoln Cavalcante Mattos ◽  
Diêgo Farias Oliveira
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Log Transformation ◽  
Interval Valued

THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Manufacturing Firms ◽  
Single Step ◽  
Model Parameters ◽  
Linear Regression Models ◽  
Operating Income ◽  
Predictive Content ◽  
Multiple Step

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

scholarly journals Relationships between test positivity rate, total laboratory confirmed cases of malaria, and malaria incidence in high burden settings of Uganda: an ecological analysis

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Jaffer Okiring ◽  
Adrienne Epstein ◽  
Jane F. Namuganga ◽  
Victor Kamya ◽  
Asadu Sserwanga ◽  
...  
Keyword(s):  
Regression Models ◽  
Malaria Incidence ◽  
Temporal Changes ◽  
Linear Regression Models ◽  
High Burden ◽  
Exponential Regression ◽  
Malaria Morbidity ◽  
Laboratory Test Results ◽  
Combining Data ◽  
Catchment Areas

Abstract Background Malaria surveillance is critical for monitoring changes in malaria morbidity over time. National Malaria Control Programmes often rely on surrogate measures of malaria incidence, including the test positivity rate (TPR) and total laboratory confirmed cases of malaria (TCM), to monitor trends in malaria morbidity. However, there are limited data on the accuracy of TPR and TCM for predicting temporal changes in malaria incidence, especially in high burden settings. Methods This study leveraged data from 5 malaria reference centres (MRCs) located in high burden settings over a 15-month period from November 2018 through January 2020 as part of an enhanced health facility-based surveillance system established in Uganda. Individual level data were collected from all outpatients including demographics, laboratory test results, and village of residence. Estimates of malaria incidence were derived from catchment areas around the MRCs. Temporal relationships between monthly aggregate measures of TPR and TCM relative to estimates of malaria incidence were examined using linear and exponential regression models. Results A total of 149,739 outpatient visits to the 5 MRCs were recorded. Overall, malaria was suspected in 73.4% of visits, 99.1% of patients with suspected malaria received a diagnostic test, and 69.7% of those tested for malaria were positive. Temporal correlations between monthly measures of TPR and malaria incidence using linear and exponential regression models were relatively poor, with small changes in TPR frequently associated with large changes in malaria incidence. Linear regression models of temporal changes in TCM provided the most parsimonious and accurate predictor of changes in malaria incidence, with adjusted R2 values ranging from 0.81 to 0.98 across the 5 MRCs. However, the slope of the regression lines indicating the change in malaria incidence per unit change in TCM varied from 0.57 to 2.13 across the 5 MRCs, and when combining data across all 5 sites, the R2 value reduced to 0.38. Conclusions In high malaria burden areas of Uganda, site-specific temporal changes in TCM had a strong linear relationship with malaria incidence and were a more useful metric than TPR. However, caution should be taken when comparing changes in TCM across sites.

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