Subset Regression Analysis Research Articles

0649 Ultrasound Assessment of Tongue Movement as a Predictor of Response to Hypoglossal Nerve Stimulation (HGNS)

Abstract Introduction HGNS is an approved therapy for obstructive sleep apnea (OSA). Initial setting of HGNS voltage is based on observation of anterior tongue movement, which may not reflect opening of the retroglossal airway. We developed an ultrasonographic (US) technique to assess tongue movement with HGNS. We correlated US measures of tongue movement at the initial HGNS voltage setting with the AHI determined by PSG/HSAT on HGNS therapy. Methods Eleven subjects implanted with INSPIRE™ (HGNS) were enrolled at least one month post-implantation. Initial HGNS voltage was determined while awake and semi-recumbent and set to achieve visualized anterior tongue protrusion at a tolerable stimulation voltage. A curvilinear probe (5-2MHz) was placed longitudinally in the submental region at the midline with the indicator pointed anteriorly. Hyoid bone excursion (HBE) with stimulation was used as a marker of base of tongue movement. PSG or HSAT was performed to determine AHI with HGNS. Responders were defined as those with a reduction in AHI ≥50% and an AHI <20 events/hr. Results N=11, 6M, 5F, Age=66.5 ±18.4 years, BMI=27.9±2.7 kg/m2. Pre-treatment AHI=38.8±13.4/hr, T-90%=10.5±16.7%. Mean HBE in responders=1.02±0.17cm vs 0.76±0.20cm in non-responders (p=0.006). Best subsets regression analysis performed using post-treatment AHI as the dependent variable and age, BMI, baseline AHI, HBE and HGNS voltage as independent variables showed that HBE (coef. -29.1, p=0.038) and BMI (coef. 2.6, p=0.018) were independent predictors of response. Conclusion We demonstrated that ultrasound assessment of HBE during HGNS may be a useful tool to predict response to therapy and guide HGNS settings. HBE, rather than voltage, predicted post-treatment AHI. Support None.

Clay Mineral Alteration in Oil and Gas Fields: Integrated Analyses of Surface Expression, Soil Spectra, and X-Ray Diffraction Data

Subsurface hydrocarbon occurrences can be detected by clay mineral (CM) alteration at the surface as a consequence of hydrocarbon migration. This study analyzed CM alteration in an oil and gas (O&G) field in the West Tugu field, located in the northwest Java Basin of Indonesia. Landsat 8 OLI data acquired on 25 September 2015 and soil spectral reflectance (SSR) data recorded using analytical spectral devices (ASD) in March 2017 were processed using clay indices to analyze CM alteration. Soil samples from a field survey of the same area were also analyzed using X-ray diffraction (XRD) to identify their CM compositions. CM distribution was empirically modeled by integrating the SSR bands re-sampled to Landsat 8 OLI (SSRL) bands and the CM composition using the best subset regression method and regression analyses. The resulting model was applied to Landsat 8 OLI images to map the surface CM alteration. The results show that CM alteration observed in the O&G field is an indicator of hydrocarbon microseepage at the surface. These results can be used as a technique to identify prospective regions that contain hydrocarbons and differentiate from those that are barren, and could be useful for increasing the rate of drilling success.

Computation of Evapotranspiration with Artificial Intelligence for Precision Water Resource Management

Accurate estimation of reference evapotranspiration (ETo) provides useful information for water resource management and sustainable agriculture. This study estimates ETo with recurrent neural networks (RNNs), namely long short-term memory (LSTM) and bidirectional LSTM. Four representative meteorological sites (North Cape, Summerside, Harrington, and Saint Peters) were selected across Prince Edward Island (PEI), Canada to form a PEI dataset from mean values of the four sites’ climatic variables for capturing climatic variability from all parts of the province. Based on subset regression analysis, the highest contributing climatic variables, namely maximum air temperature and relative humidity, were selected as input variables for RNNs’ training (2011–2015) and testing (2016–2017) runs. The results suggested that the LSTM and bidirectional LSTM are suitable methods to accurately (R2 > 0.90) estimate ETo for all sites except Harrington. Testing period (2016–2017) root mean square errors were recorded in range of 0.38–0.58 mm/day for all sites. No major differences were observed in accuracy of LSTM and bidirectional LSTM. Another objective of this study was to highlight the potential gap between ETO and rainfall for assessing agriculture sustainability in Prince Edward Island. Analyses of the data highlighted that the cumulative ETo surpassed the cumulative rainfall potentially affecting yield of major crops in the island. Therefore, agriculture sustainability requires viable options such as supplemental irrigation to replenish the crop water requirements as and when needed.

Climate-change impacts on electricity demands at a metropolitan scale: A case study of Guangzhou, China

This study was to quantify the effects of climate change on total electricity consumption (TEC) and residential electricity consumption (REC) at a regional scale, with a case study in Guangzhou, China. The Mann-Kendall test was used to explore the tendency of climate change. The best subset regression analysis was undertaken to develop electricity consumption models, as represented by a number of socioeconomic and climatic variables. The levels of electricity consumption and their variabilities (percentage changes) in 2016 to 2035 (the 2030s), 2046 to 2065 (the 2050s), and 2076 to 2095 (the 2080s) were then calculated under 20 scenario combinations, which were driven by five Shared Socio-economic Pathways (SSPs) and four Representation Concentration Pathways (RCPs). The results revealed that Guangzhou had a significant warming tendency till the end of the 21st century, with an increasing rate of 0.15 – 0.47 °C/decade (1986–2099) under four RCPs. With such a warming trend, the increased demand for cooling would lead to the raised electricity consumption. Furthermore, total electricity consumption would be more sensitive to climatic warming than residential electricity consumption. With a raised temperature of 1 °C, total electricity consumption would increase by 2.7%, and the residential one would increase by 0.9%. In addition, the projected impacts of climate change on electricity consumption would depend on the emissions of greenhouse gases. In other words, electricity consumption would vary significantly under four RCPs, with the impacts being increased gradually from RCP2.6 to RCP8.5. In the 2080s, total electricity consumption would be 161 TWh under RCP2.6, while the residential one would be 44 TWh. In comparison, under RCP8.5, total electricity consumption would be 171 TWh, while the residential one would be 45 TWh. Under global warming, total electricity consumption would increase by 3.2%–10.4% by 2080s, compared with the baseline period from 1986 to 2005; for residential electricity consumption, the relevant increases would be 1.1%–3.5%.

Validity of adopting a Health Assessment Questionnaire Disability Index less than 0.5 as a target in elderly rheumatoid arthritis patients

The effect of age on the Health Assessment Questionnaire Disability Index (HAQ-DI) scores of rheumatoid arthritis (RA) patients and the validity of adopting HAQ-DI < 0.5 as the target for functional remission and comprehensive disease control (CDC) under a treat-to-target (T2T) treatment strategy were investigated. A total of 441 RA patients with > 3-year treatment under T2T were evaluated. The relationships between the HAQ-DI score at follow-up (HAQ) and 28-joint Disease Activity Score with C-reactive protein, Sharp/van der Heijde Score, age at follow-up, and HAQ-DI at baseline were statistically evaluated with best subset regression analysis in groups separated according to age and the EULAR response. CDC status was evaluated with a chi-square test. The HAQ score significantly correlated with all indices in the group ≥ 65years old (G-O) and in the group with good or moderate EULAR responses (p < 0.01). No significant correlation was observed in the group < 65years old (G-Y) or in the group with no EULAR response. The CDC ratio was not significantly different between the age groups, whereas the HAQ failure ratio was significantly greater in G-O than in G-Y (p < 0.01). No significant difference was found between the EULAR response groups. The HAQ score is influenced by age in patients > 65years. T2T is appropriate for attaining good disease activity control but does not always lead to functional remission in these patients. The HAQ score < 0.5 is not an appropriate target for functional remission according to the CDC criteria for elderly patients.Key Points• ADL in elderly RA patient aged ≥ 65years declines corresponding to his/her aging.• Functional remission for elderly RA patients is not the same as that for young RA patients.• The HAQ score < 0.5 in elderly RA patient is not an appropriate target for CDC.

Screening osteoporotic femoral neck without measuring bone mineral density with the use of tartrate resistant acid phosphatase-5b and serum-creatinine-to-cystatin C ratio in Japanese postmenopausal women

BackgroundScreening osteoporosis in Japanese postmenopausal women is an important subject for preventing bone fragility fracture (BFF). Measuring bone mineral density (BMD) is included in the criteria for osteoporosis diagnosis. However, it has not gained popularity. MethodsBMD of lumbar spine (LS) and femoral neck (FN) were measured, the serum creatinine-to-cystatin C ratio (Cr/CysC), tartrate-resistant acid phosphatase-5b (TRACP-5b), body mass index (BMI) were also simultaneously measured. These subjects had no potential secondary osteoporosis factors or concomitant drug administration for osteoporosis (pSubjects; primary subjects). Best subsets regression analysis (BeStR) was evaluated for determining the statistically significant factors, and multivariate regression analysis (MLR) was used to assess the correlation of these factors with BMD at each part. Relationship between osteoporotic criteria whether T score≤−2.5 and each factor was also evaluated at each part with binary logistic regression analysis (BLR). Cut-off index (COI) at each part was determined, and sensitivity and specificity were evaluated in pSubjects and in subjects with whom potential secondary osteoporosis factors were included (sSubjects). ResultsBMI and Cr/CysC had a significant influence on BMD at the LS with BeStR, whereas only BMI demonstrated significant correlation with BMD both with MLR and BLR, with 22.7 of COI and sensitivity and specificity of 48.7% and 94.7%, respectively, in pSubjects with 60 and 49.1% and 87.5%, respectively, in sSubjects with 83. At the FN, Cr/CysC, BMI, and TRACP-5b significantly influenced BMD, whereas all these factors demonstrated significant correlation with BMD. Cr/CysC and TRACP-5b demonstrated significant correlation with T score≤−2.5 in BLR. With COI that was made in combination with these factors and subjects’ age, sensitivity and specificity were 67.7% and 82.4%, respectively, in pSubjects and 55.8% and 82.8%, respectively, in sSubjects. ConclusionsThese results suggested that BMI, TRACP-5b, and Cr/CysC may be realistic surrogate markers for screening osteoporosis in Japanese postmenopausal women.

Meta-analysis of propped fracture conductivity data—fixed effects regression modeling

Previous empirical models of propped fracture conductivity are based either on data sourced from single investigations or on data not in the public domain. In this work, statistically rigorous models of propped fracture conductivity are developed using a database of fracture conductivity experiments reported in technical literature over the last 40 years.The database contains about 2700 data points. Propped fracture conductivity is the dependent variable and proppant type, mesh size, proppant concentration, formation hardness, closure stress, formation temperature, and polymer concentration are the independent variables. The full, original database was pared down to various subsets, each having complete information in relation to some subset of the independent variables. The number of independent variables included in each of the resulting databases varied from three to six. Seventy percent of the data was used to develop the models while thirty percent of the data was used to validate them.Fixed effect models were selected using all subsets regression analysis in three, four, and five experimental factors for each of two types of proppant. The five factor model appeared to be the most useful model for both proppant types from a predictive point of view. Five-factor model predictions also compared favorably with an existing propped fracture conductivity model and different case histories published in literature.This project provides engineers with access to a propped fracture conductivity database based on experiments reported over the past 40 years in technical literature. The models developed based on this database can be used to generate predictions of propped fracture conductivity for a variety of proppant characteristics and formation conditions. Also, the models presented here are based on data from experimental investigations in different laboratories, potentially reducing biases that may be present in single laboratory investigations.

Soil health characterization in smallholder agricultural catchments in India

Soil health (SH) of managed lands in India is affected by anthropogenic activities such as nutrient mining, excessive tillage, and monocropping, which reduce the productive capacity of soils. A comprehensive SH characterization was conducted in 27 catchments in six districts of Jharkhand, India. Each was stratified into four landscape positions: (i) uncultivated upland in tree vegetation, (ii) cultivated upland in garden or orchard use, and (iii) midland and (iv) lowland areas in rice-fallow fields, yielding 113 soil samples from 0 to 15 cm and 20 from 30 to 40 cm depths. Soil textural separates as well as 15 dynamic physical, biological, and chemical properties were assessed using the Comprehensive Assessment of Soil Health framework. Nutrient analyses indicate low to very low P and K values, but high micronutrient levels. A district level ANOVA shows effects of inherent soil factors on the indicators. The influence of tillage, nutrient extraction as well as landscape hydrology on soil health indicators was apparent, notably showing uncultivated soils with higher overall SH. Puddle tillage affected the surface and subsurface soil, the latter showing reduced water holding capacity and less favorable biological indicators. Multivariate analyses showed directional separation of biological and chemical indicators in the first two principal components. A Best Subsets Regression analysis revealed organic matter, soil respiration and active carbon as the most predictive in determining overall SH scores (R2adj = 0.87). In conclusion, a comprehensive soil health assessment using a spatial framework in Jharkhand, India identified multiple SH constraints related to farmer management, associated organic matter dynamics, and natural factors.

Machine learning model to predict an adult learner’s decision to continue ESOL course

This study investigated the ability of the demographic and the affective variables to predict the adult learners’ decision to continue ESOL courser. 278 adult learners, enrolled on ESOL course at FLS institution in Istanbul, Turkey, participated in the study. The result showed that the continued or dropped out groups, demonstrated statistical differences in the demographic variable (the placement test score) with a magnitude of large effect size (.378). Additionally, the result showed the effect size in the perception of the affective variables (motivation, attitude, and anxiety), accounts for about 50% of the variation between the continuation and dropout groups. Following that, three machine learning models were proposed; all possible subset regression analysis was used to compare the three models. The adequate model, which fitted the demographic variable (the placement test score) and the affective variables (motivation, attitude, and anxiety), correctly predicted 83.3% of the adult learners’ decision to continue ESOL course. The model showed about 68% goodness-of-fit. The cultural implications of these findings are discussed, along with suggestions for future research.

Students Who Used the Library in Their First Year of University are More Likely to Graduate or Still be Enrolled After Four Years

A Review of:&#x0D; Soria, K. M., Fransen, J., &amp; Nackerud, S. (2017). The impact of academic library resources on undergraduates’ degree completion. College &amp; Research Libraries, 78(6), 812–823. https://doi.org/10.5860/crl.78.6.812&#x0D; Abstract&#x0D; Objective – The researchers sought to measure the effect of accessing library resources on academic retention and graduation after four years while accounting for external factors that may influence academic outcomes.&#x0D; Design – Quasi-experimental observational study.&#x0D; Setting – A large public university in the Midwestern United States of America.&#x0D; Subjects – 5,368 first-year, non-transfer undergraduates; an entire freshman class.&#x0D; Methods – Using already collected student and library records data, the researchers grouped the population into those that had accessed one of five library resources at least once (treatment) and those who had not (control). The five treatment variables studied were circulation use, electronic resource or website access, library computer workstation logins, enrollment in open registration or course-embedded library instruction, and use of two reference services (online chat and peer research consultations).&#x0D; The researchers then performed a series of propensity score matching and regression analyses to compare the treatment and control groups’ outcome measures—graduation or continued enrollment after four years. These statistical models controlled for ten covariate measures that included SAT scores, first generation status, on campus residency, college of enrollment (e.g., business, engineering, education, biological sciences, design, or food, agriculture, and natural sciences), and demographic profiles. The regressions included subset analyses of the treatment group to determine if some treatment variables were associated with better outcomes than others.&#x0D; Main Results – The researchers found that students in the treatment group (n = 4,415) were 1.441 times more likely to graduate and 1.389 times more likely to still be enrolled after four years than those in the control group (n = 953). Both results were statistically significant at p &lt; 0.01 and p &lt; 0.001 respectively. The subset regression analyses revealed that accessing an electronic resource at least once was associated with the best graduation odds at 1.924 times (p &lt; 0.001) and the best continued enrollment odds at 1.450 times (p &lt; 0.001). Students who had accessed computer workstations and either of the two reference services studied were no more likely to have graduated or still been enrolled after four years than those who had not (p &lt; 0.001 and p &lt; 0.05).&#x0D; Conclusion – Accessing library services during the first year of university is associated with improved academic outcomes after four years. More research is needed to accurately measure this impact for methodological reasons. Libraries should document contact with students as much as possible for later assessment.

Determination of Gestational Age: A Perspective With the Bangladeshi Ethnicity

Determination of gestational age (GA) with precision is vital to the proper care of pregnant mothers. Our present study aimed at determining the gestational ages by the ultrasonic measurements of four standard fetal parameters namely biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), and femur length (FL) specifically focusing on the Bangladeshi ethnic population. The current study is cross-sectional but data were collected in a prospective nature. There were 229 Bangladeshi women who had usual singleton fetuses with the evidence of menstrual dates by sonography before 14 weeks. Fetal anatomical structures have been scanned and measured at the time of sonographic inspection. Multiple linear regression models of GA by the date of last menstrual period (LMP) and GA by ultrasonographic measures have been fitted on four fetal parameters for our analysis. In the present analysis, we have found that all the response variables i.e. gestational age by LMP and gestational age by ultrasonography (USG) clearly depend on the fetal parameters. The best subsets regression analysis shows that BPD, AC, and FL are the best predictors of GA by LMP (adj R 2 = 96.54). In terms of Mallow’s Cp and adj R 2 , it is found that all the fetal parameters BPD, AC, HC and FL are important predictors for GA by USG. It has been observed that multiple fetal parameters measured by ultrasonography can be useful to predict gestational age during second and third trimesters. Precise estimation of gestational age by this method is helpful to reduce pregnancy-related complicacy and maternal death in the developing countries including Bangladesh.

Extending AIC to best subset regression

The Akaike information criterion (AIC) is routinely used for model selection in best subset regression. The standard AIC, however, generally under-penalizes model complexity in the best subset regression setting, potentially leading to grossly overfit models. Recently, Zhang and Cavanaugh (Comput Stat 31(2):643–669, 2015) made significant progress towards addressing this problem by introducing an effective multistage model selection procedure. In this paper, we present a rigorous and coherent conceptual framework for extending AIC to best subset regression. A new model selection algorithm derived from our framework possesses well understood and desirable asymptotic properties and consistently outperforms the procedure of Zhang and Cavanaugh in simulation studies. It provides an effective tool for combating the pervasive overfitting that detrimentally impacts best subset regression analysis so that the selected models contain fewer irrelevant predictors and predict future observations more accurately.

Estimating seasonal aboveground biomass of a riparian pioneer plant community: An exploratory analysis by canopy structural data

The aboveground biomass (AGB) of vegetation is of central importance for ecosystem services by providing a measure of productivity. Models have been developed for estimating AGB via canopy structural variables in both fundamental and applied ecological studies. However, the potential of canopy structural variables for describing AGB dynamics throughout a growing season are still unclear. This study focuses on the AGB seasonal dynamics of a pioneer community, Cynodon dactylon (L.) Pers. (Bermuda grass), in a newly-formed riparian habitat at China’s Three Gorges Reservoir. The objectives are (1) to determine the most important structural variable for estimating AGB at different growing stages during the season, and (2) to develop a model that can estimate AGB at the different growing stages and using multiple structural variables. We sampled the C. dactylon community six times during the growing season from May to September 2016. Six variables were engaged in the analysis, including five canopy structural variables, i.e., canopy height (H), canopy cover (CC), leaf area index (LAI), the volume related variables VLAI (H×LAI) and VCC (H×CC), and one seasonal growth effect variable (SV). We conducted univariate linear regression analysis to determine the most important estimator of AGB and the best subset regression analysis were used to develop the AGB estimation model. The detected most important AGB estimator changed with different growing stages throughout a season. Canopy structural characteristics of the community are key factors for determining such changes. Cover was the most important variable for AGB estimation during the early growing season and VLAI was the most important variable in the mid and end of the growing season. The developed best multivariate models explained an additional 11% in AGB variance on average for the different growing stages compared with the univariate models using the most important estimators. SV was found to be useful in developing an acceptance general AGB estimation model appropriate for the entire growing season. The findings of this study are expected to provide knowledge for guiding sampling work and to assist with modeling AGB and understanding the AGB seasonal dynamics in the future.

PREDICTING PERFORMANCE IN DESIGN-BID-BUILD PROJECTS

Design-bid-build (DBB) is the most common delivery method for public and private construction projects in Spain, and is also widely used in the United States (U.S.). While the execution of DBB projects in Spain and the U.S. is similar in many ways, owners in Spain approach the procurement of the general contractor and subcontractors differently. There is a greater emphasis on selecting firms that provide the “best value” of cost, quality and expertise. Comparatively, U.S. owners leading a DBB delivery are more concerned with achieving the lowest first cost. Using data from 67 completed DBB projects in Spain and the U.S., a best subsets regression analysis was performed to develop five models, each predicting a key performance outcome of DBB projects: cost growth, schedule growth, unit cost, delivery speed and intensity. These models separated the effects of the country of origin from other explanatory variables that effect performance. These other explanatory variables include differences in the procurement process, payment terms, the initial unit cost, size of the project, and measures of the project team’s integration and cohesion. The findings of this study show that, while owners in Spain opt for a more robust procurement process, DBB projects in the U.S. have comparable unit cost and are delivered faster and with a greater intensity when all other variables are held constant. This suggests that factors unique to the country of origin, not directly related to the execution of DBB projects, have a distinct effect on their performance.

Application of Linear Body Measurement for Predicting Body Weight of Swamp Buffalo

The objective of this research was to predict swamp buffalo weight using simple linier body measurement. The height of wither (TB), height of hip (TP), heart girth (LiD), width of chest (LD), dept of chest (DD), width of hip (LP), thurl length (PK), width of rump (LK), body length (PB) were measured in centimeters using measuring rib, one meter ruler, and measuring stick, whereas body weight was determined in kilograms using two tons weighing scale. Linier body measurement ratio between male and female swamp buffaloes was analyzed using t-test analysis. The relationship between linier measurement variables were analyzed using correlation analyisis. Body weight estimator was estimated using best subset regression, linier regression and multiple linier regression analysis. Result of T-test analysis showed that linier body measurement between male and female buffaloes have significant difference in hearth girth, height of hip and body length. Result of coefficient analysis showed that heart girth and height of hip have a highly correlation with body weight of male and female swamp buffalo. Estimation of swamp buffalo body weight could be estimated through multiple regression with heart girth and height of hip variables. It could be concluded that weight of swamp buffalo can be estimated using multiple linier regression formula, BB = 3.6435LiD + 0.1208TP - 265.43 for male swamp buffalo and BB = 4.1783LiD – 0.3086TP - 305,19 for female swamp buffalo.

Impact of sickle cell anaemia on cardiac chamber size in the paediatric population.

Right ventricle and left ventricle diastolic diameters, left ventricle mass estimate, left ventricle shortening fraction, myocardial performance index, and an index of myocardial relaxation (E/E') were calculated and correlated with haematological parameters. A total of 110 patients (65% haemoglobin SS, 29% haemoglobin SC) were studied at a mean age of 12.14±5.26 years. Right ventricle dilatation and left ventricle dilatation were present in 61.5 and 42.9%, respectively. Left ventricle mass was abnormal in 21.9%; all patients had normal myocardial performance index, 31.4% had abnormal E/E', and left ventricle shortening fraction was low in 38.1%. Cardiac dilatation was best correlated with haemoglobin, aspartate aminotransferase, reticulocytosis and bilirubin. Best subset regression analysis yielded significant additional prediction for right ventricle or left ventricle dilatation with haemoglobin, bilirubin, and lactate dehydrogenase. Abnormal E/E' was solely predictable with haemoglobin level. Hydroxyurea-treated patients had improved diastolic function. Right ventricle dilatation was more prevalent than left ventricle dilatation. The long-term consequences of right ventricular dilatation, clinical consequences, and association with pulmonary vasculopathy need to be further determined.

Limited Sampling Strategy for the Estimation of Tacrolimus Area Under the Concentration-Time Curve in Chinese Adult Liver Transplant Patients

Objectives: Limited sampling strategies (LSS) have been proposed as an alternative method for estimating area under concentration-time curve (AUC) of immunosuppressive agent tacrolimus (TAC). In this study, we aimed to develop the LSS models for predicting AUC of TAC in Chinese liver transplant patients. Methods: Twenty-eight adult liver transplant patients receiving immunosuppressive regimen including TAC were enrolled. A total of 47 pharmacokinetic profiles were obtained after 1 or 3 weeks therapy. TAC concentrations were determined before dose (0 h) and at 1, 1.5, 2, 2.5, 3, 4, 6, 8 and 12 h after dosing by LC-MS/MS assay. Optimal subset regression analysis was used to establish the models for estimating TAC AUC_0-12. Prediction error (PE) and absolute PE were calculated. The agreement between predicted and measured AUC_0-12 was investigated by Bland-Altman analysis. The obtained models were validated by bootstrap analysis. The prediction performance among various CYP3A5 and ABCB1 genotypes was compared. The models selected from previous published studies were also validated using our data. Results: Twenty-eight models including 1, 2, 3 and 4 blood time points sampling were established (r² = 0.653-0.979). The best model for prediction of TAC AUC_0-12 was 0.81 + 1.73C₁ + 1.32C₂ + 3.87C₄ + 3.75C₈ (r² = 0.979). Forty profiles (85.1%) had estimated TAC AUC_0-12 within ±15% of observed TAC AUC_0-12. Model with C₀-C₂ (r² = 0.880) can be used for outpatients who need monitoring to be carried out in a short period. We also found that ABCB1 genotype may be a reason of variation in the prediction performance. There was good correlation between predicted and measured AUC_0-12 (r² = 0.880-0.928) by using models from previous studies with sample collected within 4 h post dose. Conclusion: The LSS is an effective approach for estimation of full TAC AUC_0-12 in Chinese liver transplant patients.

Creating a non-linear total sediment load formula using polynomial best subset regression model

Summary The aim of this study is to derive a new total sediment load formula which is more accurate and which has less application constraints than the well-known formulae of the literature. 5 most known stream power concept sediment formulae which are approved by ASCE are used for benchmarking on a wide range of datasets that includes both field and flume (lab) observations. The dimensionless parameters of these widely used formulae are used as inputs in a new regression approach. The new approach is called Polynomial Best subset regression (PBSR) analysis. The aim of the PBRS analysis is fitting and testing all possible combinations of the input variables and selecting the best subset. Whole the input variables with their second and third powers are included in the regression to test the possible relation between the explanatory variables and the dependent variable. While selecting the best subset a multistep approach is used that depends on significance values and also the multicollinearity degrees of inputs. The new formula is compared to others in a holdout dataset and detailed performance investigations are conducted for field and lab datasets within this holdout data. Different goodness of fit statistics are used as they represent different perspectives of the model accuracy. After the detailed comparisons are carried out we figured out the most accurate equation that is also applicable on both flume and river data. Especially, on field dataset the prediction performance of the proposed formula outperformed the benchmark formulations.

X‐ray CT‐Derived Soil Characteristics Explain Varying Air, Water, and Solute Transport Properties across a Loamy Field

Core Ideas We used CT‐derived parameters to explain solute, water, and air transport at field scale. CTmatrix was found to be the best parameter to explain solute transport. Limiting macroporosity gave the highest correlations with water and air transport. Combining macroporosity and CTmatrix improved the relationships of water and air flow. The characterization of soil pore space geometry is important for explaining fluxes of air, water, and solutes through soil and understanding soil hydrogeochemical functions. X‐ray computed tomography (CT) can be applied for this characterization, and in this study CT‐derived parameters were used to explain water, air, and solute transport through soil. Forty‐five soil columns (20 by 20 cm) were collected from an agricultural field in Estrup, Denmark, and subsequently scanned using a medical CT scanner. Nonreactive tracer leaching experiments were performed in the laboratory along with measurements of air permeability (Ka) and saturated hydraulic conductivity (Ksat). The CT number of the matrix (CTmatrix), which represents the moist bulk density of the soil matrix, was obtained from the CT scans as the average CT number of the voxels in the grayscale image excluding macropores and stones. The CTmatrix showed the best relationships with the solute transport characteristics, especially the time by which 5% of the applied mass of tritium was leached, known as the 5% arrival time (t0.05). The CT‐derived macroporosity (pores &gt;1.2 mm) was correlated with Ka and log10(Ksat). The correlation improved when the limiting macroporosity (the minimum macroporosity for every 0.6‐mm layer along the soil column) was used, suggesting that soil layers with the narrowest macropore section restricted the flow through the whole soil column. Water, air, and solute transport were related with the CT‐derived parameters by using a best subsets regression analysis. The regression coefficients improved using CTmatrix, limiting macroporosity, and genus density, while the best model for t0.05 used CTmatrix only. The scanning resolution and the time for soil structure development after mechanical activities could be factors that increased the uncertainty of the relationships. Nevertheless, the results confirmed the potential of X‐ray CT visualization techniques for estimating fluxes through soil at the field scale.

New Propped-Fracture-Conductivity Models for Tight Gas Sands

Summary In this work, we developed two models of fracture conductivity in the presence of proppant-pack damage. The models are derived from the analysis of experiments relating dynamic fracture conductivity to flowback rate, reservoir temperature, polymer loading, presence of breaker, closure stress, and proppant concentration. The first model is purely empirical, and it is modeled after fracture-conductivity studies conducted in previous work. The experimental design and planning table were derived from fractional-factorial designs. This meant that we could uniquely quantify the effect of all the dependent variables and also ensure that the factors are not correlated. We tested the empirical model for adequacy—that is, ensuring the model residuals are normally distributed with no obvious trend and approximately constant variance. Because empirical models are generally constrained by the data on which they are built, we developed a semiempirical model dependent on dimensional analysis and nonlinear regression. Dimensionless products from the dimensional analysis procedure form the backbone of this semiempirical model. We used subset-regression analysis to determine the optimal regression model and found that model was adequate. We also found two dimensionless groups and used them to develop the semiempirical model. We thereafter corrected the semiempirical model for the effect of temperature. Both the purely empirical model and the semiempirical model match experimental data with reasonable accuracy. These models can be used as a first approximation for the short-term fracture conductivity of field hydraulic fractures. We also present methodology by which engineers can develop empirical models, assess uncertainty, and test for regression-model adequacy given noisy data.