Fitting Technique Research Articles

Joint Placement and Beamforming Design for IRS-Enhanced Multiuser MISO Systems

The fundamental intelligent reflecting surface (IRS) deployment problem is studied for IRS-aided downlink multi-user communication system, where IRSs are arranged to be deployed in a specific area for enhancing the desired signal and suppressing interference. Specifically, we aim to maximize the minimum achievable rate over all locations in a specific area by jointly optimizing the transmit beamforming at the access point (AP) as well as the placement and reflective beamforming at the IRS. The formulated problem is non-convex and thus difficult to be solved directly. To draw essential insights, we first consider the single-user case and the optimal solution is derived in closed-form. The result shows that the optimal locations are in the connecting line between the AP and user, and the IRSs can be optimally deployed along the connecting line. Besides, a hybrid offline and online design scheme is developed for the multi-user case, where an area discretization strategy and deep neural network (DNN)-based curve fitting technique are proposed for optimizing the IRS locations in the offline manner. Then, an online iterative algorithm is presented to solve the transmit and received beamformig vectors, respectively. Numerical results show that the performance gain is increased by optimizing the IRS locations.

DOWEL PIN - SNAP FIT TECHNIQUE: A NOVEL SPLIT CAST TECHNIQUE

Complete denture construction comes with a lot challenges both during the clinical and laboratory steps. Lab remounting is one of those steps it is done to mount the master cast back in position after acrylisation of the complete denture to undergo selective grinding. But the conventional methods used for this technique have not been good enough to hold the master cast in place in a stable manner. The novel technique introduced in this article uses dowel pins for remounting, and it provides a more stable, reliable and snug fit technique without making the technique any more complicated.

AI-driven lightweight real-time SDR sensing system for anomalous respiration identification using ensemble learning

In less than three years, more than six million fatalities have been reported worldwide due to the coronavirus pandemic. COVID-19 has been contained within a broad range due to restrictions and effective vaccinations. However, there is a greater risk of pandemics in the future, which can cause similar circumstances as the coronavirus. One of the most serious symptoms of coronavirus is rapid respiration decline that can lead to mortality in a short period. This situation, along with other respiratory conditions such as asthma and pneumonia, can be fatal. Such a condition requires a reliable, intelligent, and secure system that is not only contactless but also lightweight to be executed in real-time. Wireless sensing technology is the ultimate solution for modern healthcare systems as it eliminates close interactions with infected individuals. In this paper, a lightweight real-time solution for anomalous respiration identification is provided using the radio-frequency sensing device USRP and the ensemble learning approach extra-trees. A wireless software-defined radio platform is used to acquire human respiration data based on the change in the channel state information. To improve the performance of the trained models, the respiration data is utilised to produce large simulated data sets using the curve fitting technique. The final data set consists of eight distinct types of respiration: eupnea, bradypnea, tachypnea, sighing, biot, Cheyne-stokes, Kussmaul, and central sleep apnea. The ensemble learning approach: extra-trees are trained, validated, and tested. The results showed that the proposed platform is lightweight and highly accurate in identifying several respirations in a static setting.

Varactor-tuned wideband band-pass filter for 5G NR frequency bands n77, n79 and 5G Wi-Fi

A wide-band band-pass filter (BPF) using coupled lines, rectangular stubs and Stepped-Impedance Resonators (SIRs) is presented in this paper. The proposed BPF operates over a large pass-band from 3.15 to 6.05 GHz covering 5G New Radio (NR) frequency Bands n77, n79 and 5G Wi-Fi, which includes the G band of US (3.3 to 4.2 GHz), 5G band of Japan (4.4 to 5 GHz) and 5G Wi-Fi (5.15 to 5.85 GHz). The presented filter has a maximum pass-band Insertion-Loss (IL) of 2 dB, a sharp roll-off rate and suppresses all the unwanted harmonics from 4.2 GHz up to 12 GHz with a 15 dB attenuation level. The performance of each section can be analyzed based on lumped-element circuit models. The electrical size of the BPF is 0.258 λg × 0.255 λg, where λg is the guided wavelength at the central frequency. The design accuracy is verified through implementing and testing the final BPF. The pass-band band-width can be controlled by adding the varactor diodes. A good relationship between the band-width and the varactor diodes are extracted by the curve fitting technique.

Frozen-Density Embedding for Including Environmental Effects in the Dirac-Kohn-Sham Theory: An Implementation Based on Density Fitting and Prototyping Techniques.

Frozen density embedding (FDE) represents an embedding scheme in which environmental effects are included from first-principles calculations by considering the surrounding system explicitly by means of its electron density. In the present paper, we extend the full four-component relativistic Dirac–Kohn–Sham (DKS) method, as implemented in the BERTHA code, to include environmental and confinement effects with the FDE scheme (DKS-in-DFT FDE). The implementation, based on the auxiliary density fitting techniques, has been enormously facilitated by BERTHA’s python API (PyBERTHA), which facilitates the interoperability with other FDE implementations available through the PyADF framework. The accuracy and numerical stability of this new implementation, also using different auxiliary fitting basis sets, has been demonstrated on the simple NH3–H2O system, in comparison with a reference nonrelativistic implementation. The computational performance has been evaluated on a series of gold clusters (Aun, with n = 2, 4, 8) embedded into an increasing number of water molecules (5, 10, 20, 40, and 80 water molecules). We found that the procedure scales approximately linearly both with the size of the frozen surrounding environment (consistent with the underpinnings of the FDE approach) and with the size of the active system (in line with the use of density fitting). Finally, we applied the code to a series of heavy (Rn) and super-heavy elements (Cn, Fl, Og) embedded in a C60 cage to explore the confinement effect induced by C60 on their electronic structure. We compare the results from our simulations, with respect to more-approximate models employed in the atomic physics literature. Our results indicate that the specific interactions described by FDE are able to improve upon the cruder approximations currently employed, and, thus, they provide a basis from which to generate more-realistic radial potentials for confined atoms.

Parallel Frequency Function-Deep Neural Network for Efficient Approximation of Complex Broadband Signals.

In recent years, with the growing popularity of complex signal approximation via deep neural networks, people have begun to pay close attention to the spectral bias of neural networks—a problem that occurs when a neural network is used to fit broadband signals. An important direction taken to overcome this problem is the use of frequency selection-based fitting techniques, of which the representative work is called the PhaseDNN method, whose core idea is the use of bandpass filters to extract frequency bands with high energy concentration and fit them by different neural networks. Despite the method’s high accuracy, we found in a large number of experiments that the method is less efficient for fitting broadband signals with smooth spectrums. In order to substantially improve its efficiency, a novel candidate—the parallel frequency function-deep neural network (PFF-DNN)—is proposed by utilizing frequency domain analysis of broadband signals and the spectral bias nature of neural networks. A substantial improvement in efficiency was observed in the extensive numerical experiments. Thus, the PFF-DNN method is expected to become an alternative solution for broadband signal fitting.

Research on the Integration and Innovation of Ceramic Decorative Pattern Design and Traditional Culture

Ceramics, from the initial practicality to artistry, has a profound impact on social life and its value is unique. Tracing back to the origin of Chinese ceramic art, the application of decorative patterns is legendary. These decorative arts with national and cultural characteristics have distinctive characteristics of the times. Traditional culture is embedded in decorative patterns, which are dominated by the spirit of traditional culture. It is a profound historical accumulation of national traditional culture. Looking at the decorative patterns of ceramic art, these patterns are melting the collision of thoughts, reflecting the ceramic decorative patterns of the light of Chinese civilization for thousands of years, and are also the profound embodiment of the cultural and artistic values of Chinese civilization. This article mainly takes Ganzhou Qilizhen kiln ceramic design as an example to interpret the application of traditional culture in ceramic decorative art. A curve fitting technique based on adaptive genetic algorithm is proposed. The process of curve fitting using adaptive genetic algorithm and curve tools is described in detail, and the comparison results are given. The research shows that the adaptive genetic algorithm can effectively reduce the original iteration times by 30.14% compared with other algorithms. At the same time, this method is applied to the process of pattern design.

Introducing a New Model for the Analysis of Iot Business Model Ecosystem in the Field Of Rehabilitation in Iran Using the Sem Approach

Background and Aims The purpose of this article was to present a local model for ecosystem analysis of the IoT business model in the field of rehabilitation in Iran with an emphasis on improving the quality of life of the disabled community in the country. Methods The current article can be categorized as applied, combined (qualitative and quantitative), and survey-based descriptive. The society, i.e., the statistical sample, included eight experts in the qualitative part and consisted of 100 executives and experts in the quantitative part. A questionnaire was used to analyze the dimensions and indicators. The face validation of the questionnaire was performed by the Delphi technique. Content validity ratio and content validity ratio were used as two content-related validity indicators for qualitative validation. The reliability of all dimensions and indicators was confirmed through Cronbach’s test with a population consisting of 30 experts. To analyze the proposed model of the researcher, we used inferential tests, loading factor analysis, t, structure equation modeling, and fitting techniques on a 100-person sample. Results The fitness of the model was approved by experts with 99% confidence according to the philosophy, goals of the theoretical foundations of dimensions and components, feedback, and output of the model. Conclusion The findings of this paper led to a model with several dimensions: (1) environment, (2) actors, (3) ecosystem cycle, and (4) IoT architecture in business. The corresponding indicators for the identified dimensions were validated. Independence, interaction, and empowerment were considered three principle values for the target community.

Effects of 15th January 2010 Annular Solar Eclipse on Traveling Ionospheric Disturbances and Equatorial Plasma Bubbles over Low Latitude Regions of East Africa

The influence of the 15th January 2010 annular solar eclipse on traveling ionospheric disturbances (TIDs) and equatorial plasma bubbles (EPBs) is studied using data from six global navigation and satellite system (GNSS) receivers spread across the path of annularity over the low latitude region of East Africa. The GNSS receivers are stationed at Nairobi (RCMN), Malindi (MAL2), and Eldoret (MOIU) in Kenya; Mbarara (MBAR) in Uganda; Kigali (NURK) in Rwanda; and Mtwara (MTWA) in Tanzania. The study period ranges from 12th to 18th January 2010, three days before and after the 15th January 2010 annular solar eclipse. The year 2010 marked the beginning phase of solar cycle 24, evidently observed in low total electron content (TEC) values and the disturbed storm time index (Dst). The eclipse started at 7 : 06 LT and ended at 10 : 14 LT, with MOIU and RCMN experiencing eclipse magnitudes of 0.946 and 0.93, respectively. The maximum obscuration occurred between 8 : 21 LT and 8 : 34 LT across most of the stations. A detrending on vertical TEC (VTEC) derived from GNSS receivers across or close to the path of totality revealed a reduction of ∼2-3 TECU during the maximum phase of the eclipse. The level of reduction was highly close to the totality path and decreased smoothly away from the totality path. Using a background polynomial fitting technique on diurnal TEC, we analyzed TIDs along NURK-MBAR-MOIU and MOIU-RCMN-MAL2 GPS arrays. The results revealed a wavelike perturbation with a virtual horizontal velocity of 830m/s and ∼1 TECU amplitude propagating eastward along the MOIU-RCMN-MAL2 GPS array. The study reports a moderate scintillation activity of 0.5 ≤ ROTI ≤ 0.9 values, demonstrating the presence of few EPBs over the region. The results show a latitudinal variation in GPS-TEC scintillation activities and suggest a possible influence of the eclipse on the observed increase in average scintillation levels across East Africa.

A theory-based model of cumulative activity

Energy expenditure can be used to examine the health of individuals and the impact of environmental factors on physical activity. One of the more common ways to quantify energy expenditure is to process accelerometer data into some unit of measurement for this expenditure, such as Actigraph activity counts, and bin those measures into physical activity levels. However, accepted thresholds can vary between demographics, and some units of energy measurements do not currently have agreed upon thresholds. We present an approach which computes unique thresholds for each individual, using piecewise exponential functions to model the characteristics of their overall physical activity patterns corresponding to well established sedentary, light, moderate and vigorous activity levels from the literature. Models are fit using existing piecewise fitting techniques and software. Most participants’ activity intensity profile is exceptionally well modeled as piecewise exponential decay. Using this model, we find emergent groupings of participant behavior and categorize individuals into non-vigorous, consistent, moderately active, or extremely active activity intensity profiles. In the supplemental materials, we demonstrate that the parameters of the model correlate with demographics of age, household size, and level of education, inform behavior change under COVID lockdown, and are reasonably robust to signal frequency.

KIC 10417986: Spectroscopic Confirmation of the Nature of the Binary System with a δ Scuti Component

KIC 10417986 is a short orbital period (0.0737 day) ellipsoidal variable star with a δ Sct and γ Dor hybrid pulsation component discovered by Kepler. The ground-based spectroscopic observations were carried out in the winters of 2020 and 2021 to investigate the binary nature of this star. We derive the orbital parameters using the rvfit code with a result of K 1 = 29.7 ± 1.5 km s−1, γ = −18.7 ± 1.7 km s−1, and confirm an orbital period of 0.84495 day instead of the result given by Kepler. The atmospheric parameters of the primary are determined by the synthetic spectral fitting technique with the estimated values of T eff = 7411 ± 187 K, log g = 4.2 ± 0.3 dex, [M/H] = 0.08 ± 0.09 dex and vsini = 52 ± 11 km s−1. KIC 10417986 is a circular orbit binary system. From the single-lined nature and mass function of the star, the derived orbital inclination is 26° ± 6°, and the mass of the secondary is M ⊙, which should be a late-K to early-M type star. Fourteen frequencies are extracted from Kepler light curves, of which six independent frequencies in the high-frequency region are identified as the p-mode pulsations of δ Sct star, and one independent frequency in the low-frequency region (f 2 = 1.3033 cd−1) is probably the rotational frequency due to the starspots rather than the ellipsoidal effect.

A Metadata-Based Approach to the Integration of Educational Resources in Ethnic Traditional Physical Education

Ethnic traditional sports are a comprehensive interdisciplinary field that draws on and integrates theories and methods from related disciplines, particularly anthropology, to reveal the laws governing the emergence and development of ethnic traditional sports. The evolution of ethnic traditional sports has historically contributed to the construction of theoretical systems, the transmission of traditional culture, the formation of distinctive fitness techniques, the intensification of socioeconomic interests, and the development of cultural self-confidence. The majority of ethnic traditional sports teaching resources, however, are unstructured data including documents, images, animations, programs, tools, and videos. They rely heavily on ethnic traditional physical education teachers for instructional design, and there is no way to create a unified integration and sharing platform. From the preparation, supplementation, and description of resource metadata specifications to the clustering, integration, and cataloging of digital resources and the implementation of retrieval and service interfaces, this paper focuses on the effective integration of high-quality digital traditional ethnic sports resources in the process of network sharing.

Data Analysis and Modelingof Claim Amounts of Car Insurance using Big Data: A Study for Pakistan

Modelling of data of claim amount is of paramount importance to manage risk reserve for payment of claims. Actuaries model uncertainty using probability distributions.
 In this research paper claim amount distribution of the data of an insurance concern has been estimated and analysis was performed on big-data of claim amounts for better understanding and fitting of various probability distribution using R.
 It was noticed that the claim amounts distribution is highly positive skewed, therefore we have studied Exponential distribution, Gamma distribution and Weibull distribution as possible candidates for modelling the claim amount data. Chi Square test has been used as goodness of fit technique to decide suitable statistical model to representing the claim amounts under study.
 Exponential distribution is found suitable for modelling the data under study.
 Proposed model is usefulto estimate claim amount on aggregate for insurance concern when total loss is required to be computed to manage the risk reserve for the payments of claims.

A New Unsupervised Technique to Analyze the Centroid and Frequency of Keyphrases from Academic Articles

Automated keyphrase extraction is crucial for extracting and summarizing relevant information from a variety of publications in multiple domains. However, the extraction of good-quality keyphrases and the summarising of information to a good standard have become extremely challenging in recent research because of the advancement of technology and the exponential development of digital sources and textual information. Because of this, the usage of keyphrase features for keyphrase extraction techniques has recently gained tremendous popularity. This paper proposed a new unsupervised region-based keyphrase centroid and frequency analysis technique, named the KCFA technique, for keyphrase extraction as a feature. Data/datasets collection, data pre-processing, statistical methodologies, curve plotting analysis, and curve fitting technique are the five main processes in the proposed technique. To begin, the technique collects multiple datasets from diverse sources, which are then input into the data pre-processing step by utilizing some text pre-processing processes. Afterward, the region-based statistical methodologies receive the pre-processed data, followed by the curve plotting examination and, lastly, the curve fitting technique. The proposed technique is then tested and evaluated using ten (10) best-accessible benchmark datasets from various disciplines. The proposed approach is then compared to our available methods to demonstrate its efficacy, advantages, and importance. Lastly, the results of the experiment show that the proposed method works well to analyze the centroid and frequency of keyphrases from academic articles. It provides a centroid of 706.66 and a frequency of 38.95% in the first region, 2454.21 and 7.98% in the second region, for a total frequency of 68.11%.

Towards a Model for Simulating Collision of Multiple Water Droplets Flow Down a Leaf Surface

In the current article, a physics-based mathematical model is presented to generate realistic trajectories of water droplets across the Frangipani leaf surface and can be applied on any other kind of leaves, which is the first in the series of two articles that we are going to present the second article later on. In the second article, we will study the collision between the droplet and the liquid streak. The model has many applications in different scientific and engineering fields, such as modelling pesticide movements on leaves surfaces and modeling absorption and nutrition systems. The leaf surface consists of a triangular mesh structure that needs to be constructed using different techniques such as a well-known technique called EasyMesh method. The leaf surface is constructed using surface fitting techniques, such as finite elements methods and Clough-Tocher method, using a set of 3D real-world data points collected by a laser scanner, and the motion of the droplet on each triangle is calculated using a derived equation of motion. The motion of the droplet affected different forces, such as gravity and drag forces. Simulations of the model were verified using Matlab programming, and the results seemed to be real and capture the droplet motion very well.

COSMOS2020: Manifold learning to estimate physical parameters in large galaxy surveys

We present a novel method for estimating galaxy physical properties from spectral energy distributions (SEDs) as an alternative to template fitting techniques and based on self-organizing maps (SOMs) to learn the high-dimensional manifold of a photometric galaxy catalog. The method has previously been tested with hydrodynamical simulations in Davidzon et al. (2019, MNRAS, 489, 4817), however, here it is applied to real data for the first time. It is crucial for its implementation to build the SOM with a high-quality panchromatic data set, thus we selected “COSMOS2020” galaxy catalog for this purpose. After the training and calibration steps with COSMOS2020, other galaxies can be processed through SOMs to obtain an estimate of their stellar mass and star formation rate (SFR). Both quantities resulted in a good agreement with independent measurements derived from more extended photometric baseline and, in addition, their combination (i.e., the SFR vs. stellar mass diagram) shows a main sequence of star-forming galaxies that is consistent with the findings of previous studies. We discuss the advantages of this method compared to traditional SED fitting, highlighting the impact of replacing the usual synthetic templates with a collection of empirical SEDs built by the SOM in a “data-driven” way. Such an approach also allows, even for extremely large data sets, for an efficient visual inspection to identify photometric errors or peculiar galaxy types. While also considering the computational speed of this new estimator, we argue that it will play a valuable role in the analysis of oncoming large-area surveys such as Euclid of the Legacy Survey of Space and Time at the Vera C. Rubin Telescope.

On the nature and propagation of errors in roughness parameters obtained from spectral analysis of atomic force microscopy topographic images

Scale-dependent surface roughness strongly affects critical surface properties of materials, including adhesion, wettability, and optical/thermal properties. As a particular example, tuning the ratio of the true to nominal area—a parameter that depends on the root mean square (RMS) local slope of the finest scales of topography—is an effective approach to tailor the wetting characteristics of solid surfaces. While power spectral density (PSD) analysis of atomic force microscopy (AFM) topographic images allows for directly assessing the scale-dependence of surface roughness, this approach to analyze AFM height maps requires power-law modeling and extrapolation of a PSD with inherently non-normal error distributions. Here, we use a Monte Carlo approach based on synthetic AFM images of known input power-law parameters to (1) evaluate the accuracy of fitting techniques based on the expected distribution of the PSD; (2) evaluate the error propagation from the standard errors of the fitted power-law parameters to the computed RMS slope and area ratio; and (3) evaluate the statistical power of various PSD regression techniques when differentiating surfaces. The results indicated that standard error for ordinary least squares on a log-log PSD (log OLS) underpredicts the observed variance by ∼50%. This underprediction can be eliminated by implementing a log-link gamma regression. Moreover, when propagating the standard error to derived parameters (e.g., the RMS slope), the propagated error is generally conservative relative to the observed variance and closely predicts the observed variance when extrapolating to the finest scale. This result demonstrates the possibility of accurately estimating roughness parameters that are critical for evaluating surface phenomena on the basis of fitting and extrapolating AFM data using self-affine models. Finally, our results provided evidence for the possibility of statistically differentiating surfaces with similar power-law parameters when using weighted gamma regression with a mean of 10 images, as opposed to unweighted log-OLS that requires as many as 10 000 images to differentiate images.

Single Gaussian process method for arbitrary tokamak regimes with a statistical analysis

Gaussian process regression is a Bayesian method for inferring profiles based on input data. The technique is increasing in popularity in the fusion community due to its many advantages over traditional fitting techniques including intrinsic uncertainty quantification and robustness to over-fitting. This work investigates the use of a new method, the change-point method, for handling the varying length scales found in different tokamak regimes. The use of the Student’s t-distribution for the Bayesian likelihood probability is also investigated and shown to be advantageous in providing good fits in profiles with many outliers. To compare different methods, synthetic data generated from analytic profiles is used to create a database enabling a quantitative statistical comparison of which methods perform the best. Using a full Bayesian approach with the change-point method, Matérn kernel for the prior probability, and Student’s t-distribution for the likelihood is shown to give the best results.

Performance Evaluation of PV Model-Based Maximum Power Point Tracking Techniques

Maximum power point tracking (MPPT) techniques extract the ultimate power from the photovoltaic (PV) source. Therefore, it is a fundamental control algorithm in any PV configuration. The research in this area is rich and many MPPT methods have been presented in the literature. However, in the current study, we focus on the PV model-based MPPT algorithms. In this regard, the classification of this category can be mainly divided into curve fitting methods and techniques based on the mathematical model or characteristics of the PV source. The objective of the PV model-based MPPT algorithm is to allocate the position of the maximum power point (MPP). Thus, no searching efforts are required to capture that point, which makes it simple and easy to implement. Consequently, the aim of this study is to give an overview of the most commonly utilized model-based MPPT methods. Furthermore, discussion and suggestions are also addressed to highlight the gap in this area. The main methods from the literature are compared together. The comparison and evaluation are validated using an experimental hardware-in-the-loop (HIL) system, where high efficiency (more than 99%) can be obtained with a simple calculation procedure and fast convergence speed.

Precision of region of interest-based tri-exponential intravoxel incoherent motion quantification and the role of the Intervoxel spatial distribution of flow velocities.

The purpose of this work was to obtain precise tri-exponential intravoxel incoherent motion (IVIM) quantification in the liver using 2D (b-value and first-order motion moment [M1 ]) IVIM-DWI acquisitions and region of interest (ROI)-based fitting techniques. Diffusion MRI of the liver was performed in 10 healthy volunteers using three IVIM-DWI acquisitions: conventional monopolar, optimized monopolar, and optimized 2D (b-M1 ). For each acquisition, bi-exponential and tri-exponential full, segmented, and over-segmented ROI-based fitting and a newly proposed blood velocity SDdistribution (BVD) fitting technique were performed to obtain IVIM estimates in the right and left liver lobes. Fitting quality was evaluated using corrected Akaike information criterion. Precision metrics (test-retest repeatability, inter-reader reproducibility, and inter-lobar agreement) were evaluated using Bland-Altman analysis, repeatability/reproducibility coefficients (RPCs), and paired sample t-tests. Precision was compared across acquisitions and fitting methods. High repeatability and reproducibility was observed in the estimations of the diffusion coefficient (Dtri = [1.03 ± 0.11] × 10-3 mm2 /s; RPCs ≤ 1.34 × 10-4 mm2 /s), perfusion fractions (F1 = 3.19 ± 1.89% and F2 = 16.4± 2.07%; RPCs ≤ 2.51%), and blood velocity SDs (Vb,1 = 1.44 ± 0.14 mm/s and Vb,2 = 3.62 ± 0.13 mm/s; RPCs ≤ 0.41 mm/s) in the right liver lobe using the 2D (b-M1 ) acquisition in conjunction with BVD fitting. Using these methods, significantly larger (p< 0.01) estimates of Dtri and F1 were observed in the left lobe in comparison to the right lobe, while estimates of Vb,1 and Vb,2 demonstrated high interlobar agreement (RPCs ≤ 0.45 mm/s). The 2D (b-M1 ) IVIM-DWI data acquisition in conjunction with BVD fitting enables highly precise tri-exponential IVIM quantification in the right liver lobe.