Academic literature on the topic 'Multilinear fraction'
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Journal articles on the topic "Multilinear fraction"
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Sousa, Daniel, and Christopher Small. "Spectral Mixture Analysis as a Unified Framework for the Remote Sensing of Evapotranspiration." Remote Sensing 10, no. 12 (December 5, 2018): 1961. http://dx.doi.org/10.3390/rs10121961.
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This study illustrates a unified, physically-based framework for mapping landscape parameters of evapotranspiration (ET) using spectral mixture analysis (SMA). The framework integrates two widely used approaches by relating radiometric surface temperature to subpixel fractions of substrate (S), vegetation (V), and dark (D) spectral endmembers (EMs). Spatial and temporal variations in these spectral endmember fractions reflect process-driven variations in soil moisture, vegetation phenology, and illumination. Using all available Landsat 8 scenes from the peak growing season in the agriculturally diverse Sacramento Valley of northern California, we characterize the spatiotemporal relationships between each of the S, V, D land cover fractions and apparent brightness temperature (T) using bivariate distributions in the ET parameter spaces. The dark fraction scales inversely with shortwave broadband albedo (ρ < −0.98), and show a multilinear relationship to T. Substrate fraction estimates show a consistent (ρ ≈ 0.7 to 0.9) linear relationship to T. The vegetation fraction showed the expected triangular relationship to T. However, the bivariate distribution of V and T shows more distinct clustering than the distributions of Normalized Difference Vegetation Index (NDVI)-based proxies and T. Following the Triangle Method, the V fraction is used with T to compute the spatial maps of the ET fraction (EF; the ratio of the actual total ET to the net radiation) and moisture availability (Mo; the ratio of the actual soil surface evaporation to potential ET at the soil surface). EF and Mo estimates derived from the V fraction distinguish among rice growth stages, and between rice and non-rice agriculture, more clearly than those derived from transformed NDVI proxies. Met station-based reference ET & soil temperatures also track vegetation fraction-based estimates of EF & Mo more closely than do NDVI-based estimates of EF & Mo. The proposed approach using S, V, D land cover fractions in conjunction with T (SVD+T) provides a physically-based conceptual framework that unifies two widely-used approaches by simultaneously mapping the effects of albedo and vegetation abundance on the surface temperature field. The additional information provided by the third (Substrate) fraction suggests a potential avenue for ET model improvement by providing an explicit observational constraint on the exposed soil fraction and its moisture-modulated brightness. The structures of the T, EF & Mo vs SVD feature spaces are complementary and that can be interpreted in the context of physical variables that scale linearly and that can be represented directly in process models. Using the structure of the feature spaces to represent the spatiotemporal trajectory of crop phenology is possible in agricultural settings, because variations in the timing of planting and irrigation result in continuous trajectories in the physical parameter spaces that are represented by the feature spaces. The linear scaling properties of the SMA fraction estimates from meter to kilometer scales also facilitate the vicarious validation of ET estimates using multiple resolutions of imagery.
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Rossi, Elena, Isabella Pecorini, and Renato Iannelli. "Multilinear Regression Model for Biogas Production Prediction from Dry Anaerobic Digestion of OFMSW." Sustainability 14, no. 8 (April 7, 2022): 4393. http://dx.doi.org/10.3390/su14084393.
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The aim of this study was to develop a multiple linear regression (MLR) model to predict the specific methane production (SMP) from dry anaerobic digestion (AD) of the organic fraction of municipal solid waste (OFMSW). A data set from an experimental test on a pilot-scale plug-flow reactor (PFR) including 332 observations was used to build the model. Pearson′s correlation matrix and principal component analysis (PCA) examined the relationships between variables. Six parameters, namely total volatile solid (TVSin), organic loading rate (OLR), hydraulic retention time (HRT), C/N ratio, lignin content and total volatile fatty acids (VFAs), had a significant correlation with SMP. Based on these outcomes, a simple and three multiple linear regression models (MLRs) were developed and validated. The simple linear regression model did not properly describe the data (R2 = 0.3). In turn, the MLR including all factors showed the optimal fitting ability (R2 = 0.91). Finally, the MLR including four uncorrelated explanatory variables of feedstock characteristics and operating parameters (e.g., TVSin, OLR, C/N ratio, and lignin content), resulted in the best compromise in terms of number of explanatory variables, model fitting and predictive ability (R2 = 0.87).
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Ulevicius, V., S. Byčenkienė, C. Bozzetti, A. Vlachou, K. Plauškaitė, G. Mordas, V. Dudoitis, et al. "Fossil and non-fossil source contributions to atmospheric carbonaceous aerosols during extreme spring grassland fires in Eastern Europe." Atmospheric Chemistry and Physics Discussions 15, no. 18 (September 29, 2015): 26315–55. http://dx.doi.org/10.5194/acpd-15-26315-2015.
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Abstract. In early spring the Baltic region is frequently affected by high pollution events due to biomass burning in that area. Here we present a comprehensive study to investigate the impact of biomass/grass burning (BB) on the evolution and composition of aerosol in Preila, Lithuania, during springtime open fires. Non-refractory submicron particulate matter (NR-PM1) was measured by an Aerodyne aerosol chemical speciation monitor (ACSM) and a source apportionment with the multilinear engine (ME-2) running the positive matrix factorization (PMF) model was applied to the organic aerosol fraction to investigate the impact of biomass/grass burning. Satellite observations over regions of biomass burning activity supported the results and identification of air mass transport to the area of investigation. Sharp increases in biomass burning tracers, such as levoglucosan up to 683 ng m−3 and black carbon (BC) up to 17 μg m−3 were observed during this period. A further separation between fossil and non-fossil primary and secondary contributions was obtained by coupling ACSM PMF results and radiocarbon (14C) measurements of the elemental (EC) and organic (OC) carbon fractions. Non-fossil organic carbon (OCnf) was the dominant fraction of PM1, with the primary (POCnf) and secondary (SOCnf) fractions contributing 26–44 % and 13–23 % to the TC, respectively. 5–8 % of the TC had a primary fossil origin (POCf), whereas the contribution of fossil secondary organic carbon (SOCf) was 4–13 %. Non-fossil EC (ECnf) and fossil EC (ECf) ranged from 13–24 % and 7–12 %, respectively. Isotope ratio of stable carbon and nitrogen isotopes were used to distinguish aerosol particles associated with solid and liquid fossil fuel burning.
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Ulevicius, Vidmantas, Steigvilė Byčenkienė, Carlo Bozzetti, Athanasia Vlachou, Kristina Plauškaitė, Genrik Mordas, Vadimas Dudoitis, et al. "Fossil and non-fossil source contributions to atmospheric carbonaceous aerosols during extreme spring grassland fires in Eastern Europe." Atmospheric Chemistry and Physics 16, no. 9 (May 4, 2016): 5513–29. http://dx.doi.org/10.5194/acp-16-5513-2016.
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Abstract. In early spring the Baltic region is frequently affected by high-pollution events due to biomass burning in that area. Here we present a comprehensive study to investigate the impact of biomass/grass burning (BB) on the evolution and composition of aerosol in Preila, Lithuania, during springtime open fires. Non-refractory submicron particulate matter (NR-PM1) was measured by an Aerodyne aerosol chemical speciation monitor (ACSM) and a source apportionment with the multilinear engine (ME-2) running the positive matrix factorization (PMF) model was applied to the organic aerosol fraction to investigate the impact of biomass/grass burning. Satellite observations over regions of biomass burning activity supported the results and identification of air mass transport to the area of investigation. Sharp increases in biomass burning tracers, such as levoglucosan up to 683 ng m−3 and black carbon (BC) up to 17 µg m−3 were observed during this period. A further separation between fossil and non-fossil primary and secondary contributions was obtained by coupling ACSM PMF results and radiocarbon (14C) measurements of the elemental (EC) and organic (OC) carbon fractions. Non-fossil organic carbon (OCnf) was the dominant fraction of PM1, with the primary (POCnf) and secondary (SOCnf) fractions contributing 26–44 % and 13–23 % to the total carbon (TC), respectively. 5–8 % of the TC had a primary fossil origin (POCf), whereas the contribution of fossil secondary organic carbon (SOCf) was 4–13 %. Non-fossil EC (ECnf) and fossil EC (ECf) ranged from 13–24 and 7–13 %, respectively. Isotope ratios of stable carbon and nitrogen isotopes were used to distinguish aerosol particles associated with solid and liquid fossil fuel burning.
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Deliwala, Ajaz A., and Chandra S. Yerramalli. "Computational micromechanical modelling of the material removal process in a carbon fibre composite under single-erodent particle impact." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 234, no. 10 (May 19, 2020): 1605–17. http://dx.doi.org/10.1177/1350650120925576.
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A multiscale model is developed to understand the material removal process in a unidirectional carbon fibre epoxy composite impacted by a single-erodent particle. The embedded cell approach is used to model the carbon fibre and epoxy at a microscale. The micromodel is embedded centrally in the macroscale lamina of the composite plate. The carbon fibre is considered to be elastic with orthotropic strain limits as the failure criteria. The epoxy matrix is modelled as an elastic--plastic material with multilinear isotropic hardening. The maximum equivalent plastic strain limit is used as the matrix material failure limit. Using this embedded micromechanics model, the role of matrix and the fibre in developing the composite material erosion behaviour has been clearly elucidated. The results from the simulation indicate the change in the matrix erosion behaviour as a function of the fibre volume fraction. For the current thermoset matrix, material erosion response changes from brittle behaviour to ductile behaviour with an increase in fibre volume fraction. The current study has been able to highlight the individual role of matrix and the fibre in developing the semi-ductile erosion response peculiar to a fibre-reinforced composite material.
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Tan, Huachun, Bin Cheng, Jianshuai Feng, Li Liu, and Wuhong Wang. "Mixture Augmented Lagrange Multiplier Method for Tensor Recovery and Its Applications." Discrete Dynamics in Nature and Society 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/914963.
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The problem of data recovery in multiway arrays (i.e., tensors) arises in many fields such as computer vision, image processing, and traffic data analysis. In this paper, we propose a scalable and fast algorithm for recovering a low-n-rank tensor with an unknown fraction of its entries being arbitrarily corrupted. In the new algorithm, the tensor recovery problem is formulated as a mixture convex multilinear Robust Principal Component Analysis (RPCA) optimization problem by minimizing a sum of the nuclear norm and theℓ1-norm. The problem is well structured in both the objective function and constraints. We apply augmented Lagrange multiplier method which can make use of the good structure for efficiently solving this problem. In the experiments, the algorithm is compared with the state-of-art algorithm both on synthetic data and real data including traffic data, image data, and video data.
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Naganawa, Mika, Ming-Qiang Zheng, Nabeel Nabulsi, Giampaolo Tomasi, Shannan Henry, Shu-Fei Lin, Jim Ropchan, et al. "Kinetic Modeling of 11C-LY2795050, A Novel Antagonist Radiotracer for PET Imaging of the Kappa Opioid Receptor in Humans." Journal of Cerebral Blood Flow & Metabolism 34, no. 11 (September 3, 2014): 1818–25. http://dx.doi.org/10.1038/jcbfm.2014.150.
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11C-LY2795050 is a novel kappa opioid receptor (KOR) antagonist tracer for positron emission tomography (PET) imaging. The purpose of this first-in-human study was to determine the optimal kinetic model for analysis of 11C-LY2795050 imaging data. Sixteen subjects underwent baseline scans and blocking scans after oral naltrexone. Compartmental modeling and multilinear analysis-1 (MA1) were applied using the arterial input functions. Two-tissue compartment model and MA1 were found to be the best models to provide reliable measures of binding parameters. The rank order of 11C-LY2795050 distribution volume ( VT) matched the known regional KOR densities in the human brain. Blocking scans with naltrexone indicated no ideal reference region for 11C-LY2795050. Three methods for calculation of the nondisplaceable distribution volume ( VND) were assessed: (1) individual VND estimated from naltrexone occupancy plots, (2) mean VND across subjects, and (3) a fixed fraction of cerebellum VT. Approach (3) produced the lowest intersubject variability in the calculation of binding potentials ( BPND, BPF, and BPP). Therefore, binding potentials of 11C-LY2795050 can be determined if the specific binding fraction in the cerebellum is presumed to be unchanged by diseases and experimental conditions. In conclusion, results from the present study show the suitability of 11C-LY2795050 to image and quantify KOR in humans.
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Wane, Ousmane, Julián A. Ramírez Ceballos, Francisco Ferrera-Cobos, Ana A. Navarro, Rita X. Valenzuela, and Luis F. Zarzalejo. "Comparative Analysis of Photosynthetically Active Radiation Models Based on Radiometric Attributes in Mainland Spain." Land 11, no. 10 (October 21, 2022): 1868. http://dx.doi.org/10.3390/land11101868.
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The aims of this work are to present an analysis of quality solar radiation data and develop several hourly models of photosynthetically active radiation (PAR) using combinations of radiometric variables such as global horizontal irradiance (GHI), diffuse horizontal irradiance (DHI), and direct normal irradiance (DNI) from their dimensionless indices atmospheric clearness index (kt), horizontal diffuse fraction (kd), and normal direct fraction (kb) together with solar elevation angle (α). GHI, DHI, and DNI data with 1-minute frequencies in the period from 2016 to 2021 from CEDER-CIEMAT, in a northern plateau, and PSA-CIEMAT in the southeast of the Iberian Peninsula, were used to compare two locations with very different climates according to the Köppen—Geiger classification. A total of 15 multilinear models were fitted and validated (with independent training and validation data) using first the whole dataset and then by kt intervals. In most cases, models including the clearness index showed better performance, and among them, models that also use the solar elevation angle as a variable obtained remarkable results. Additionally, according to the statistical validation, these models presented good results when they were compared with models in the bibliography. Finally, the model validation statistics indicate a better performance of the interval models than the complete models.
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Liu, Li, Ye Kuang, Miaomiao Zhai, Biao Xue, Yao He, Jun Tao, Biao Luo, et al. "Strong light scattering of highly oxygenated organic aerosols impacts significantly on visibility degradation." Atmospheric Chemistry and Physics 22, no. 11 (June 15, 2022): 7713–26. http://dx.doi.org/10.5194/acp-22-7713-2022.
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Abstract. Secondary organic aerosols (SOAs) account for a large fraction of atmospheric aerosol mass and play significant roles in visibility impairment by scattering solar radiation. However, comprehensive evaluations of SOA scattering abilities under ambient relative humidity (RH) conditions on the basis of field measurements are still lacking due to the difficulty of simultaneously direct quantifications of SOA scattering efficiency in dry state and SOA water uptake abilities. In this study, field measurements of aerosol chemical and physical properties were conducted in winter in Guangzhou (lasting about 3 months) using a humidified nephelometer system and aerosol chemical speciation monitor. A modified multilinear regression model was proposed to retrieve dry-state mass scattering efficiencies (MSEs, defined as scattering coefficient per unit aerosol mass) of aerosol components. The more oxidized oxygenated organic aerosol (MOOA) with an O/C ratio of 1.17 was identified as the most efficient light scattering aerosol component. On average, 34 % mass contribution of MOOA to total submicron organic aerosol mass contributed 51 % of dry-state organic aerosol scattering. The overall organic aerosol hygroscopicity parameter κOA was quantified directly through hygroscopicity closure, and hygroscopicity parameters of SOA components were further retrieved using a multilinear regression model by assuming hydrophobic properties of primary organic aerosols. The highest water uptake ability of MOOA among organic aerosol factors was revealed with κMOOA reaching 0.23, thus further enhancing the fractional contribution of MOOA in ambient organic aerosol scattering. In particular, the scattering abilities of MOOA were found to be even higher than those of ammonium nitrate under RH of <70 %, which was identified as the most efficient inorganic scattering aerosol component, demonstrating that MOOA had the strongest scattering abilities in ambient air (average RH of 57 %) during winter in Guangzhou. During the observation period, secondary aerosols contributed dominantly to visibility degradation (∼70 %), with substantial contributions from MOOA (16 % on average), demonstrating significant impacts of MOOA on visibility degradation. The findings of this study demonstrate that more attention needs to be paid to SOA property changes in future visibility improvement investigations. Also, more comprehensive studies on MOOA physical properties and chemical formation are needed to better parameterize its radiative effects in models and implement targeted control strategies on MOOA precursors for visibility improvement.
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Huang, Ru-Jin, Yao He, Jing Duan, Yongjie Li, Qi Chen, Yan Zheng, Yang Chen, et al. "Contrasting sources and processes of particulate species in haze days with low and high relative humidity in wintertime Beijing." Atmospheric Chemistry and Physics 20, no. 14 (July 31, 2020): 9101–14. http://dx.doi.org/10.5194/acp-20-9101-2020.
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Abstract. Although there are many studies of particulate matter (PM) pollution in Beijing, the sources and processes of secondary PM species during haze periods remain unclear. Limited studies have investigated the PM formation in highly polluted environments under low- and high-relative-humidity (RH) conditions. Herein, we present a systematic comparison of species in submicron particles (PM1) in wintertime Beijing (29 December 2014 to 28 February 2015) for clean periods and pollution periods under low- and high-RH conditions. PM1 species were measured with an aerosol chemical species monitor (ACSM) and an Aethalometer. Sources and processes for organic aerosol (OA) were resolved by positive matrix factorization (PMF) with a multilinear engine 2 (ME-2). The comparisons for clean, low-RH pollution and high-RH pollution periods are made from three different aspects, namely (a) mass concentration, (b) mass fraction and (c) growth rate in diurnal profiles. OA is the dominant component of PM1, with an average mass concentration of 56.7 µg m−3 (46 %) during high-RH pollution and 67.7 µg m−3 (54 %) during low-RH pollution periods. Sulfate had higher concentration and mass fraction during high-RH pollution periods, while nitrate had higher concentration and mass fraction during low-RH pollution periods. The diurnal variations of nitrate and oxygenated organic aerosol (OOA) showed a daytime increase in their concentrations during all three types of periods. Nitrate had similar growth rates during low-RH (0.40 µg m−3 h−1) and high-RH (0.55 µg m−3 h−1) pollution periods. OOA had a higher growth rate during low-RH pollution periods (1.0 µg m−3 h−1) than during high-RH pollution periods (0.40 µg m−3 h−1). In contrast, sulfate had a decreasing trend during low-RH pollution periods, while it increased significantly with a growth rate of 0.81 µg m−3 h−1 during high-RH pollution periods. These distinctions in mass concentrations, mass fractions and daytime growth rates may be explained by the difference in the formation processes affected by meteorological conditions. In particular, photochemical oxidation and aqueous-phase processes may both produce sulfate and nitrate. The relative importance of the two pathways, however, differs under different meteorological conditions. Additional OOA formation under high-RH (> 70 %) conditions suggests aqueous-related formation pathways. This study provides a general picture of the haze formation in Beijing under different meteorological conditions.
Book chapters on the topic "Multilinear fraction"
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Matušů, Radek, and Bilal Şenol. "Investigation of Robust Stability for Fractional-Order LTI Systems with Multilinear Structure of Ellipsoidal Parametric Uncertainty." In Software Engineering Perspectives in Intelligent Systems, 421–29. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63319-6_39.
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Alhaji Ibrahim, Musa, Yusuf Şahin, Auwal Ibrahim, Auwalu Yusuf Gidado, and Mukhtar Nuhu Yahya. "Specific Wear Rate Modeling of Polytetraflouroethylene Composites Via Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) Tools." In Virtual Assistant [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.95242.
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Lately, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models have been recognized as potential and good tools for mathematical modeling of complex and nonlinear behavior of specific wear rate (SWR) of composite materials. In this study, modeling and prediction of specific wear rate of polytetraflouroethylene (PTFE) composites using FFNN and ANFIS models were examined. The performances of the models were compared with conventional multilinear regression (MLR) model. To establish the proper choice of input variables, a sensitivity analysis was performed to determine the most influential parameter on the SWR. The modeling and prediction performance results showed that FFNN and ANFIS models outperformed that of the MLR model by 45.36% and 45.80%, respectively. The sensitivity analysis findings revealed that the volume fraction of reinforcement and density of the composites and sliding distance were the most and more influential parameters, respectively. The goodness of fit of the ANN and ANFIS models was further checked using t-test at 5% level of significance and the results proved that ANN and ANFIS models are powerful and efficient tools in dealing with complex and nonlinear behavior of SWR of the PTFE composites.
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"Multilinear Extensions of the Grothendieck Inequality (via Λ(2)-uniformizability)." In Analysis in Integer and Fractional Dimensions, 206–47. Cambridge University Press, 2001. http://dx.doi.org/10.1017/cbo9780511543012.009.
Full textConference papers on the topic "Multilinear fraction"
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Barry, Andrew, Adam Haecker, and Siddharth Misra. "RV/RH ANISOTROPY IN UNCONVENTIONAL FORMATIONS: RESOLVING THE RIDDLE OF RESISTIVITY." In 2021 SPWLA 62nd Annual Logging Symposium Online. Society of Petrophysicists and Well Log Analysts, 2021. http://dx.doi.org/10.30632/spwla-2021-0074.
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The problem of capricious log response is one that has persistently troubled formation evaluation experts since the Schlumberger brothers ran their first log in Pechelbronn, France. Since the advent of 6FF40 induction logs in the 1950’s, subtle differences have been noted between laterolog and induction response. As field resistivity measurements have evolved to array induction and array laterolog tools, resultant resistivity variability has increased. (Gianzero, 1999) This paper examines how the resistivity discrepancies between laterolog and induction response in an electrically anisotropic rock can greatly affect calculated water saturations (Sw), and ultimately oil in place. Further, several possible solutions are posited to resolve the riddle of resistivity. The root cause of the differences between the two measurement techniques is in how each tool measures the vertical resistivity (Rv) and horizontal resistivity (Rh) in addition to dielectric effects. In isotropic formations, the difference between Rv and Rh is miniscule. However most organic shales and many laminated low porosity formations are anisotropic. (Klein et al., 1997) In anisotropic formations, the ratio of Rv/Rh is not constant over the possible range of resistivities. This ratio has been observed to be as high as 5 at less than 1 ohmm of Rh, and approaches unity at infinite resistivity. Due to the high Rv/Rh ratio, at low resistivities, differences between laterolog response (Rh + fraction of Rv) and induction response (Rh) has a dramatic impact on resultant water saturation values. Laterolog array measurements exhibit a systematically higher resistivity than array induction measurements in the same formation. Variances in Sw as high as 30% has been observed. Since most North American unconventional fields have a mix of historical laterolog and induction data from different eras, it is imperative to address this apparent contradiction in values. Further confounding the issue, the mud salinity required to run both tools at peak performance is nearly mutually exclusive. This complicates efforts to resolve the conundrum because the tools cannot be run simultaneously. The closest measurements on the same rock come from sidetracked wells where one has a laterolog and the other induction. The next best possible measurement is the tri-axial resistivity which can be used to model the Rv and Rh. The issue with tri-axial tools, is that the laterolog apparent resistivity does not conform to either end member of the Rv or Rh. Since the detailed field measurements have been lost to time and only the measured resistivities are available in public LAS data sets, several practical solutions have been devised by the authors to untangle this mess. First, sets of proximal wells (<1000 ft apart) with either tool were depth-shifted and oriented for analysis. Wells with tri-axial resistivity modeled Rv and Rh supplemented the data set. Once the data was collected, the authors utilized simple x-y regression, multilinear regression, artificial neural net, and random forest regression to predict true Rh. The results of each predictor algorithm is discussed, as the optimal solution is situationally dependent.