Global Linear Regression Model Research Articles

Estimated dissolved oxygen in a river located in the south of Rio Grande do Sul

The present study aimed to estimate the dissolved oxygen concentrations in the Piratini River Watershed (in Brazilian Portuguese: BHRP), located in the northwest region of the State of Rio Grande do Sul. DO is a crucial parameter in assessing water quality, as it is essential for the maintenance of aquatic ecosystems. To estimate this, global linear regression models were applied, statistical tools capable of evaluating the effect that one or more variables exert on another. The analyzed data come from collections carried out by Environmental Protection Foundation (FEPAM) between the years 2005 and 2013, period that, due to the rigorous protocol followed and sampling periodicity, revealed a robust and consistent data set. For the selection of parameters, we sought those that had the smallest sampling failures in the historical data series, namely: Thermotolerant Coliforms, Total Phosphorus, Ammoniacal Nitrogen and Total Nitrogen, Turbidity, Total Solids and Hydrogenionic Potential. After applying the linear regressions, the best global models generated were able to explain 91.90% of the variations in dissolved oxygen in the BHRP, once all the analyzed parameters were incorporated, consolidating the applicability of the method. It is also conclude that, with the exception of Total Phosphorus, all training periods are within the limits tolerated by legislation.

White Matter Metabolite Ratios Predict Cognitive Outcome in Pediatric Traumatic Brain Injury.

The prognostic ability of global white matter and gray matter metabolite ratios following pediatric traumatic brain injury (TBI) and their relationship to 12-month neuropsychological assessments of intelligence quotient (IQ), attention, and memory is presented. Three-dimensional proton magnetic resonance spectroscopic imaging (MRSI) in pediatric subjects with complicated mild (cMild), moderate, and severe TBI was acquired acutely (6-18 days) and 12 months post-injury and compared to age-matched typically developing adolescents. A global linear regression model, co-registering MRSI metabolite maps with 3D high-resolution magnetic resonance images, was used to identify longitudinal white matter and gray matter metabolite ratio changes. Acutely, gray matter NAA/Cr, white matter NAA/Cr, and white matter NAA/Cho ratios were significantly lower in TBI groups compared to controls. Gray matter NAA/Cho was reduced only in the severe TBI group. At 12 months, all metabolite ratios normalized to control levels in each of the TBI groups. Acute gray matter and white matter NAA ratios were significantly correlated to 12-month assessments of IQ, attention, and memory. These findings suggest that whole brain gray matter and white matter metabolite ratios reflect longitudinal changes in neuronal metabolism following TBI, which can be used to predict neuropsychological outcomes in pediatric subjects.

Temporal heterogeneous effects of land-use on dockless bike-sharing usage under transit-oriented development context: The case of Beijing

The transit-oriented development (TOD) is an effective planning strategy for achieving the Sustainable Development Goals (SDGs) in transportation by incentivizing active transport. As an active transport with high flexibility and affordability, the use of dockless bike-sharing (DBS) services was affected by many identified factors. However, few studies have examined this effect from a temporally heterogeneous perspective. Here, this study aims to test if there are heterogeneous effects of station-level land-use on the use of DBS across time and to explore how and to what extent station-level land-use affects the use of DBS dynamically. Taking Beijing as a case study, this study developed the Global Linear Regression models and the Time-Varying Coefficients panel data models to estimate the heterogeneity. The results prove the existence of heterogeneity on both working days and non-working days. The daily DBS trips were significantly correlated to the density, diversity, and most types of land-use; the effect of density maintained a high level and varies with hours, and the effect of diversity showed an insignificant effect on hourly DBS trips. Besides, public management and service facilities were critical in influencing DBS usage during the daily off-peak hours, while residences always significantly and positively correlated to the use of DBS before morning peak hours and after evening peak hours. Further, the study suggests an active transfer of DBS facilities in the businesses and commercial areas to surrounding areas during non-working days. The findings guide the improvement of the service efficiency of DBS systems, and integrate DBS management strategies into TOD practice toward SDGs.

Prediction of Soil Heavy Metal Distribution Using Geographically Weighted Regression Kriging.

Both soil heavy metals and the influencing factors are related to spatial location and are spatially heterogeneous. However, the global linear regression model assumes the regression coefficients to be spatially stationary throughout the study region and is unable to account for the spatially varying relationships between soil heavy metals and influencing factors. Thus, the objectives of this study were to estimate the spatial distribution of soil heavy metals using a geographically weighted regression kriging (GWRK) approach, and compare the GWRK results with those obtained from ordinary kriging (OK) and regression kriging (RK). A dataset of soil lead (Pb) concentrations in Daye city, China, that was sampled in 2019 was used. According to the results of spatial smoothness, variability, and interpolation accuracy, GWRK was the best method and could provide the most reasonable spatial distribution pattern and the highest spatial interpolation accuracy in comparison with OK and RK.

LAND-USE and land-cover change processes in Pampa biome and relation with environmental and socioeconomic data

Pampa biome in the last years has gone through a process of change in land use, chiefly due to the conversion of grassland vegetation for agriculture of grains and silviculture. The main objective of this work is to analyze processes of Land-Use and Land-Cover (LUCC) in the Brazilian Pampa Biome, mapped from Multitemporal data of MODIS sensor, including the main processes of landscape transformation. The period studied was 2000 and 2014, and MODIS-EVI images and night DMSP-OLS images were used for generation of land use and cover maps, through decision tree classification. IBGE census sectors’ limits were used. To investigate the processes of landscape transformation of Pampa Biome, environmental variables were used including geomorphometric data, landscape metrics and climate data and socioeconomic variables. Local (GWR) and global linear regression models were used in addition to procedures for spatial clustering (SKATER algorithm). Reduction of around 25% of grassland class in 15-year interval was verified, from 10,252,740 ha to 7,676,208 ha. On the other hand, agriculture areas like Soybean class obtained 145.56% increase in their total area, from 855,087 ha in 2000, to 2,099,837 ha in 2014. Silviculture class also presented increase of over 167% of its total area. The main factors in the global regression model that negatively contributed to grassland degradation process are: population density, height against the closest drainage (HAND Model) and degradation patches in the grassland. Factors that positively contributed are: population residing in domiciles, average of number of residents in domiciles, Soybean expansion patches and distance from Soybean expansion process. It was concluded that orbital data along with geoprocessing techniques provided tools for monitoring changes in land use and cover.

Modelling changes in forest attributes driven by human activities at different spatial scales in the subtropical Atlantic Forest

Human activities are the main drivers of biotic homogenization, thus affecting ecosystem functions. In this study, we aimed to investigate the relationship among anthropogenic disturbances and forest attributes. Moreover, we sought to identify direct and indirect effects of topographic heterogeneity (TH), more specifically of standard deviation of altitude, on anthropogenic disturbances and forest attributes, respectively, through a path analysis. We used data gathered on 186 systematically distributed sample plots located in the Brazilian subtropical Evergreen Rainforest. We selected 14 predictor variables related to anthropogenic disturbances aiming to model seven forest attributes related to species diversity, composition, and structure. The aboveground biomass, rarefied species richness, proportion of standing dead trees, and proportion of individuals of pioneer species in the regeneration layer were better predicted by global linear regression models. The proportions of individuals of pioneer species in the canopy layer and of threatened species in the canopy and regeneration layers were better explained by local geographically weighted regression models. Human activities at different spatial scales may lead to disturbances (e.g., edge effects and habitat fragmentation), thus driving changes in forest attributes. The land use amidst forest remnants was related to biomass production and biotic homogenization. Logging and road networks may imperil the maintenance of threatened species. The path analysis showed that TH indirectly affects species richness via pasture area. Furthermore, topography appeared to act as a barrier for the expansion of certain human activities over the landscape. Our findings evoked the need for implementation of adaptative conservation strategies.

Mineral carbon MinC(%) from Rock-Eval analysis as a reliable and cost-effective measurement of carbonate contents in shale source and reservoir rocks

The abundance of carbonate minerals in rocks is an important element for petroleum geochemists, geologists and engineers to consider during hydrocarbon exploration and production. Carbonate minerals can be related to the depositional environments of the source rocks and hence the type of organic matter that can be subsequently modified through diagenetic processes. The total carbonate content in shales can also be used to deduce their fracability as reservoir rock for shale gas/oil extraction. At present, mineralogical analysis by X-ray diffraction (XRD) is the most widely used technique in the oil and gas-related petrophysical and geochemical laboratories for identifying and quantifying carbonate minerals in rock samples. In contrast, the use of the total mineral carbon content parameter MinC (%) from Rock-Eval analysis has been limited despite (1) the parameter's effectiveness as demonstrated through instrument and methodology development; and (2) the presence of a large volume of Rock-Eval results due to its widespread use in the oil and gas industry.In this study, we acquired XRD and Rock-Eval analytical results on over nine hundred rock samples ranging from Ordovician to Cretaceous age from four petroleum sedimentary basins in Canada. Least-squares regression analyses produce empirical equations with R2 values greater than 0.92 for the correlations between XRD total carbonate contents and the Rock-Eval MinC (%) values of six different suites of samples. This indicates that the MinC (%) values generated by Rock-Eval 6 and other instruments of similar capability can be used with confidence for estimating the amounts of total carbonates in sedimentary rock samples. While the correlation between the two types of carbonate content measurements is geology-specific, a global linear regression model (R2 of 0.97) based on the large combined data set has been proposed for approximating the total carbonate abundances based on Rock-Eval MinC (%) values.

On the censored cost-effectiveness analysis using copula information

BackgroundInformation and theory beyond copula concepts are essential to understand the dependence relationship between several marginal covariates distributions. In a therapeutic trial data scheme, most of the time, censoring occurs. That could lead to a biased interpretation of the dependence relationship between marginal distributions. Furthermore, it could result in a biased inference of the joint probability distribution function. A particular case is the cost-effectiveness analysis (CEA), which has shown its utility in many medico-economic studies and where censoring often occurs.MethodsThis paper discusses a copula-based modeling of the joint density and an estimation method of the costs, and quality adjusted life years (QALY) in a cost-effectiveness analysis in case of censoring. This method is not based on any linearity assumption on the inferred variables, but on a punctual estimation obtained from the marginal distributions together with their dependence link.ResultsOur results show that the proposed methodology keeps only the bias resulting statistical inference and don’t have anymore a bias based on a unverified linearity assumption. An acupuncture study for chronic headache in primary care was used to show the applicability of the method and the obtained ICER keeps in the confidence interval of the standard regression methodology.ConclusionFor the cost-effectiveness literature, such a technique without any linearity assumption is a progress since it does not need the specification of a global linear regression model. Hence, the estimation of the a marginal distributions for each therapeutic arm, the concordance measures between these populations and the right copulas families is now sufficient to process to the whole CEA.

Non-invasive glucose monitoring in patients with Type 1 diabetes: A Multisensor system combining sensors for dielectric and optical characterisation of skin

In vivo variations of blood glucose (BG) are affecting the biophysical characteristics (e.g. dielectric and optical) of skin and underlying tissue (SAUT) at various frequencies. However, the skin impedance spectra for instance can also be affected by other factors, perturbing the glucose related information, factors such as temperature, skin moisture and sweat, blood perfusion as well as body movements affecting the sensor-skin contact. In order to be able to correct for such perturbing factors, a Multisensor system was developed including sensors to measure the identified factors. To evaluate the quality of glucose monitoring, the Multisensor was applied in 10 patients with Type 1 diabetes. Glucose was administered orally to induce hyperglycaemic excursions at two different study visits. For analysis of the sensor signals, a global multiple linear regression model was derived. The respective coefficients of the variables were determined from the sensor signals of this first study visit ( R 2 = 0.74, MARD = 18.0% − mean absolute relative difference). The identical set of modelling coefficients of the first study visit was re-applied to the test data of the second study visit to evaluate the predictive power of the model ( R 2 = 0.68, MARD = 27.3%). It appears as if the Multisensor together with the global linear regression model applied, allows for tracking glucose changes non-invasively in patients with diabetes without requiring new model coefficients for each visit. Confirmation of these findings in a larger study group and under less experimentally controlled conditions is required for understanding whether a global parameterisation routine is feasible.