Systematic Effects Research Articles

Self‐quantification and consumer well‐being: A meta‐analytic review

AbstractSelf‐quantification technology is increasingly and irrevocably transforming consumers' relationships with their own minds and bodies. However, existing research findings on the contribution of self‐quantification to consumer well‐being are disparate. Given the popularity of self‐quantification technology among consumers in the post‐pandemic era and its inherent transformative nature, it is surprising that this gap remains unaddressed. To resolve this inconsistency and to examine how and when self‐quantification influences consumer well‐being, we conduct a meta‐analysis of consumer well‐being in the context of self‐quantification technology. Our findings reveal that self‐quantification positively influences consumer well‐being. However, self‐quantification also negatively affects consumer well‐being through body image and self‐esteem. The systematic moderation effects of cultural dimensions (e.g., uncertainty avoidance and individualism), prior experience, data sharing, and sample characteristics on the relationship between self‐quantification and consumer well‐being are also confirmed. While uncertainty avoidance, prior experience, and data sharing accentuate the positive effects of self‐quantification on consumer well‐being, an individualistic culture attenuates this influence. This study contributes to the consumer well‐being literature and extends objectification theory in the context of self‐quantification. These findings will guide practitioners and policymakers in devising strategies and policies to allow self‐quantification technology to be used in a way that enhances consumers' health and well‐being.

Sensitivity of Bayesian 21 cm power spectrum estimation to foreground model errors

Abstract Power spectrum estimators are an important tool in efforts to detect the 21 cm brightness temperature fluctuations from neutral hydrogen at early times. An initial detection will likely be statistical in nature, meaning that it will not be possible to make a coherent map of the brightness temperature fluctuations; instead, only their variance will be measured against a background of noise and residual systematic effects. Optimal Quadratic Estimator (OQE)-based methods often apply an inverse covariance weighting to the data. However, inaccurate covariance modelling can lead to reduced sensitivity and, in some cases, severe signal loss. We recently proposed a Bayesian method to jointly estimate the 21 cm fluctuations, their power spectrum, and foreground emission. Instead of requiring a fixed a priori estimate of the covariance, we estimate the covariance as part of the inference. Choices of parametrization, particularly of the foregrounds, are subject to model errors and could lead to biases and other ill effects if not properly controlled. In this paper, we investigate the effects of inaccurate foreground models on 21 cm power spectrum recovery. Using simulated visibilities, we find that, even in the most extreme scenarios tested, our approach is capable of recovering 21 cm delay power spectrum estimates consistent with a known input signal for delays ≳ 300 ns (∼88% of the available Fourier modes). This is true even when using foreground models derived from modified foreground catalogs containing spatial and spectral perturbations at the quoted level of uncertainty on our foreground catalogs.

Study and Comparison of Hyperbolic and Pseudorange Positioning Algorithms in the eLoran System

The positioning algorithms of the Enhanced Long-Range Navigation (eLoran) system primarily include the hyperbolic positioning algorithm and the pseudorange positioning algorithm. However, the calculations present in the existing literature are inaccurate and lack empirical data, and a thorough and precise comparison of the two algorithms has yet to be conducted. Therefore, this paper employs a combination of simulation analysis and empirical analysis to explore these two positioning algorithms in depth, with an optimization of the initial position calculation in the pseudorange algorithm. Under ideal conditions without observational errors, through precise calculations and analysis, the positioning errors of both algorithms are approximately zero, and full-area solutions can be achieved. Under conditions with observational errors, this study shows that both algorithms exhibit positioning errors, with the pseudorange algorithm achieving a level of accuracy comparable to that of the hyperbolic algorithm. At the same time, the empirical analysis further verified this conclusion. Additionally, this study found that the pseudorange positioning algorithm demonstrated better applicability in practical applications, as it successfully resolved the multivalued and singularity issues present in the hyperbolic positioning algorithm.

Multi-bandwidth observer-based adaptive robust pressure control for electro-hydraulic brake system with uncertainties and measurement noise

Accurate pressure regulation of electro-hydraulic brake system (EHB) is essential for enhancing braking performance in automobiles. However, component wear and pressure measurement noise can cause model parameters and controlled states to drift, resulting in system chattering. This nonlinear and uncertain state can significantly degrade pressure control performance. Motivated by this, the article presents a controlled state reconstruction-based adaptive robust pressure control technique with noise suppression. Firstly, a reduced dynamics model is established for controller design, which effectively captures the fundamental characteristic of the EHB. Secondly, a multi-bandwidth extended state observer (MBESO) capable of noise suppression is designed to reconstruct controlled states, including unknown disturbances and unmeasured pressure change rates of the EHB. Thirdly, an MBESO-based adaptive robust pressure control technique is introduced, where the control gain and robust factor are updated based on control and observation errors. In addition, the overall performance of the proposed control strategy is analyzed in the frequency domain, and close-loop stability is demonstrated via the Lyapunov method. Finally, the pressure control precision and robustness of the proposed algorithm are validated in both dynamic and static scenarios through sinusoidal and step-response experiments on a hardware-in-loop test bench. The results indicate that the proposed algorithm reduces dynamic pressure-tracking error by up to 62% compared to traditional method under sinusoidal conditions, with steady-state error remaining within 1 bar under step-response conditions.

Evaluating the Prediction Performance of the WRF-CUACE Model in Xinjiang, China

Dust and air pollution events are increasingly occurring around the Taklimakan Desert in southern Xinjiang and in the urban areas of northern Xinjiang. Predicting such events is crucial for the advancement, growth, and prosperity of communities. This study evaluated a dust and air pollution forecasting system based on the Weather Research and Forecasting model coupled with the China Meteorological Administration Chemistry Environment (WRF-CUACE) model using ground and satellite observations. The results showed that the forecasting system accurately predicted the formation, development, and termination of dust events. It demonstrated good capability for predicting the evolution and spatial distribution of dust storms, although it overestimated dust intensity. Specifically, the correlation coefficient (R) between simulated and observed PM10 was up to 0.85 with a mean absolute error (MAE) of 721.36 µg·m−3 during dust storm periods. During air pollution events, the forecasting system displayed notable variations in predictive accuracy across various urban areas. The simulated trends of PM2.5 and the Air Quality Index (AQI) closely aligned with the actual observations in Ürümqi. The R for simulated and observed PM2.5 concentrations at 24 and 48 h intervals were 0.60 and 0.54, respectively, with MAEs of 28.92 µg·m−3 and 29.10 µg·m−3, respectively. The correlation coefficients for simulated and observed AQIs at 24 and 48 h intervals were 0.79 and 0.70, respectively, with MAEs of 24.21 and 27.56, respectively. The evolution of the simulated PM10 was consistent with observations despite relatively high concentrations. The simulated PM2.5 concentrations in Changji and Shihezi were notably lower than those observed, resulting in a lower AQI. For PM10, the simulation–observation error was relatively small; however, the trends were inconsistent. Future research should focus on optimizing model parameterization schemes and emission source data.

A-196 Using Aggregated Proficiency Testing Results to Identify Systematic Error

Abstract Background Proficiency testing (PT) should identify systematic errors, which are likely to recur. The Clinical Laboratory Improvement Amendments of 1988 (CLIA) acceptance limits (ALs) include 3SD and concentration limits. We investigated the ability of PT to detect systematic error, especially as affected by the AL type. Methods Ungradable, duplicate, and irregular scores were removed from 2008 to 2018 CLIA laboratory PT results. For analytes that had at least 200 events and at least 200 participating laboratories, we calculated the overall sample miss rate, the unsatisfactory event rate, i.e., less than 80, and event rates for Score 100 to 0. We used paired t-tests and the Wilcoxon Signed-Rank test to compare miss rates and unsatisfactory event rates between short- and long-term participants. We used the binomial distribution to model the expected event scores under the assumption that all errors may be independent (random); see Figure. For each analyte and each of the possible PT scores, we compared observed event scores with their expected values as a ratio. Results 40,596 laboratories produced 15,140,128 event scores for 75 analytes. The distribution of event scores was over-weighted with higher-order event errors (Score 0 - Score 60) compared to the predicted distribution. Miss rates and unsatisfactory rates were significantly higher for short-term laboratories. Plotting the log ratio of observed versus expected rates for event scores showed that the degree of systematic effect increased progressively for each analyte. The magnitude of the effect was less for 3SD ALs. Conclusions In an event, PT errors are often dependent. Concentration and 3SD ALs were similarly sensitive to detect systematic error; both were more sensitive than ALs for qualitative or dilution assays. Expressing systematic error using PT data could help to identify remediable analytical issues.

Temporal dynamics in vertical leaf angles can confound vegetation indices widely used in Earth observations

Earth observation data is key for monitoring vegetation dynamics across temporal and spatial scales. The most widely used method to estimate vegetation properties from Earth observation data is vegetation indices. However, temporal dynamics in vertical leaf angles can strongly alter reflectance signals and, hence, vegetation indices. Here, we derive leaf angles from plant photographs to simulate the effect on vegetation indices with radiative transfer models. We show that leaf angle dynamics systematically confound widely applied vegetation indices. Moreover, we demonstrate that these effects are not random but tightly linked to abiotic environmental conditions. These systematic effects of vertical leaf angles have implications for monitoring plant properties, biodiversity, and ecosystem functional properties. We discuss the related challenges and opportunities to assess spatio-temporal vegetation dynamics with remote sensing and vegetation indices.

Precision polishing force control technology of abrasive belt-wheel polishing servo control system based on HGO-SMC

Due to cylinder friction, valve dead zone effect, flow nonlinearity, and measurement noise exist in pneumatic system. In the actual machining, it is difficult to achieve precise control of the pneumatic servo polishing system using the traditional PID algorithm, so the machining accuracy of the blades cannot be guaranteed. A sliding mode control (SMC) method based on high gain observer (HGO) is proposed. The HGO observes the output signal of the system and feeds the estimated signal back to the SMC to ensure that the observation error is uniform ultimate boundedness. Simulation and experiments show that compared with the PID algorithm, the steady-state error of HGO-SMC can be reduced by 66.4%. Compared with SMC(sgn), the high-frequency chattering of HGO-SMC can be reduced by 72.5%. Moreover, the polishing processing experiment shows that HGO-SMC can improve the form and position accuracy by 52.6% and reduce the surface roughness by 55.6%, compared with the original PID control algorithm of the polishing machine tool.

Robust flexible spacecraft attitude tracking under measurement errors, actuator nonlinearities, and faults

Reliable spacecraft attitude maneuver is one of the core research areas of the space industry due to the need for sustainable space exploration and advancements in the space technology. The paper presents an observer-based adaptive backstepping (OB-ABST) fault-tolerant control scheme for a flexible spacecraft attitude tracking under the effect of state measurement errors, inertial uncertainties, external disturbances, multiple actuator faults, and input nonlinear saturation (INS) while suppressing vibrations due to flexible modes. Initially, we design an adaptive backstepping controller to mitigate the system’s unwanted nonlinearities and observers to get estimates of the attitude dynamics in a recursive design framework. Later, the control structure incorporates a simple actuator saturation compensator to tackle input torque saturation in the system. The critical feature of the proposed OB-ABST control structure is to ensure precise attitude-tracking maneuvers and vibration suppression without intelligent materials such as shape memory alloys (SMA) and piezoelectric material (PZT). Lyapunov stability analysis proves practical finite-time convergence of the system state errors, observation errors, and actuator saturation compensators. Moreover, we provide specific conditions to tune design parameters. Finally, comparative simulation experiments validate the control performance of the proposed controller in the presence of multiple challenges.

Spectrum of global string networks and the axion dark matter mass

Cold dark matter axions produced in the post-inflationary Peccei-Quinn symmetry breaking scenario serve as clear targets for their experimental detection, since it is in principle possible to give a sharp prediction for their mass once we understand precisely how they are produced from the decay of global cosmic strings in the early Universe. In this paper, we perform a dedicated analysis of the spectrum of axions radiated from strings based on large scale numerical simulations of the cosmological evolution of the Peccei-Quinn field on a static lattice. Making full use of the massively parallel code and computing resources, we executed the simulations with up to 112643 lattice sites, which allows us to improve our understanding of the dependence on the parameter controlling the string tension and thus give a more accurate extrapolation of the numerical results. We found that there are several systematic effects that have been overlooked in previous works, such as the dependence on the initial conditions, contaminations due to oscillations in the spectrum, and discretisation effects, some of which could explain the discrepancy in the literature. We confirmed the trend that the spectral index of the axion emission spectrum increases with the string tension, but did not find a clear evidence of whether it continues to increase or saturates to a constant at larger values of the string tension due to the severe discretisation effects. Taking this uncertainty into account and performing the extrapolation with a simple power law assumption on the spectrum, we find that the dark matter mass is predicted in the range of m a ≈ 95–450 μeV.

Disentangling the Faraday rotation sky

Context. Magnetic fields permeate the diffuse interstellar medium (ISM) of the Milky Way, and are essential to explain the dynamical evolution and current shape of the Galaxy. Magnetic fields reveal themselves via their influence on the surrounding matter, and as such are notoriously hard to measure independently of other tracers. Aims. In this work, we attempt to disentangle an all-sky map of the line-of-sight (LoS)-parallel component of the Galactic magnetic field from the Faraday effect, utilizing several tracers of the Galactic electron density, ne. Additionally, we aim to produce a Galactic electron dispersion measure map and quantify several tracers of the structure of the ionized medium of the Milky Way. Methods. The method developed to reach these aims is based on information field theory, a Bayesian inference framework for fields, which performs well when handling noisy and incomplete data and constraining high-dimensional-parameter spaces. We rely on compiled catalogs of extragalactic Faraday rotation measures and Galactic pulsar dispersion measures, a well as data on bremsstrahlung and the hydrogen α spectral line to trace the ionized medium of the Milky Way. Results. We present the first full sky map of the LoS-averaged Galactic magnetic field. Within this map, we find LoS-parallel and LoS-averaged magnetic field strengths of up to 4 µG, with an all-sky root mean square of 1.1 µG, which is consistent with previous local measurements and global magnetic field models. Additionally, we produce a detailed electron dispersion measure map that agrees with existing parametric models at high latitudes but suffers from systematic effects in the disk. Further analysis of our results with regard to the 3D structure of ne reveals that it follows a Kolmogorov-type turbulence for most of the sky. From the reconstructed dispersion measure and emission measure maps, we construct several tracers of variability in ne along the LoS. Conclusions. This work demonstrates the power of consistent joint statistical analysis including multiple datasets and physical quantities and defines a road map toward a full three-dimensional joint reconstruction of the Galactic magnetic field and the ionized ISM.

A systematic review and meta-analysis of cow-level factors affecting milk urea nitrogen and urinary nitrogen output under pasture-based diets

With dairy cattle farming under pressure to lower its environmental footprint it is important to find effective on-farm proxies for evaluation and monitoring of management practices aimed at reducing the risk of nitrogen (N) losses and optimizing N use efficiency of dairy farm systems. Urinary N (UN) is regarded as the most potent source of N emissions. In contrast to confinement systems, there have been few studies from pasture-based systems associating on-farm animal and nutritional factors with UN output. Thus, the aims of this meta-analysis were to collate a database from pasture-based research to: (a) investigate the associations of management, dietary, and animal variables with MUN concentration, and daily UN output; (b) describe the MUN-UN association; and (c) assess whether animal, management, and dietary factors influence the relationship. We developed a data set consisting of 95 observations representing 919 lactating dairy cattle fed pasture-based diets, which was compiled from 32 unique research publications that reported both MUN and UN output. Multi-level, mixed meta-analysis regression techniques were used to analyze the data. Initially, all variables were assessed as the sole fixed effect in a 2-level random effects model, accounting for within publication heterogeneity. Meta-regression techniques were then used to assess the relationship of all variables with MUN and UN output, respectively, accounting for 3 sources of variability: the sampling error of the individual observation, within publication heterogeneity, and among publication heterogeneity. At the univariable level, despite more than 10 dietary, animal, or management variables being significantly associated with MUN, none explained a large amount of the MUN variation. The variables that explained the greatest amount of variation were dietary crude protein (CP) content and the nitrogen: metabolizable energy content ratio, which explained about 33% and 31% of the variation in MUN concentrations, respectively. Combining factors in multiple regressions improved the model fit, such that the variation within publications explained by dietary CP and N intake increased to 40.0% in the final multiple meta-regression model. For UN output, individual variables explained a greater proportion of variance reported among observations, compared with MUN, whereby diet CP content (pseudo R2 = 66.1%), N to metabolizable energy intake ratio (pseudo R2 = 64.0%), N intake (pseudo R2 = 58.3%), and MUN (pseudo R2 = 43.5%) explained the greatest amount of the total variation. Milk urea nitrogen, N intake and dry matter intake were associated with UN output in the final multiple meta-regression model. Substantial heterogeneity existed in both MUN and UN among publications, with among publication heterogeneity accounting for 73.4% of all the variation noted in MUN, and 88.6% of all the variation in UN output. As such, the meta-analyses could not predict MUN and UN to any great extent. It is recommended that a consistent approach to measuring and reporting MUN concentrations and UN output is carried out for all future research in pasture-based systems.

Electron Densities in H ii Regions from Observation of [N ii] 205 μm Fine Structure and Radio Recombination Lines

We employ observations of the 205 μm [N ii] fine structure (FS) line and radio recombination line (RRL) emission to derive the electron density in 10 well-known H ii regions. The combination of these two spectral lines (the RRL–FS line method) provides a sensitive probe of electron density in regions with n(e) ≥ 30 cm−3 without requiring knowledge of the size of the ionized region. By using H54α data from the Green Bank Telescope and 205 μm data from the SOFIA Airborne Observatory, we have almost identical 18″ beamwidths, removing a significant source of error for observations of H ii regions due to nonuniform density across the sources observed. The electron densities vary widely among the sources observed, from 2600 to 36,000 cm−3, with two low-density outliers at 94 and 520 cm−3. On average, these densities are a factor of 4 greater than the highest-resolution single-antenna data and a factor of almost 13 greater than the 182″ angular resolution single-antenna data having more sources in common. The total 1σ fractional uncertainties in n(e) are in the range 0.15–0.29. In the RRL–FS line method, the observationally determined quantity is proportional to ∫n 2(z)dz / ∫n(z)dz. For a Gaussian density distribution much more extended than its 1/e radius, this is equal to n0/2 , where n 0 is the peak electron density. The high values of electron density found are plausibly the result of the RRL–FS line technique sampling the peak of a centrally condensed density distribution.

Fractional-order Sliding Mode Control of Manipulator Combined with Disturbance and State Observer

A fractional-order sliding mode control (FSMC) method for a manipulator based on disturbance and state observers is proposed. First, a state estimator is designed that can estimate the velocity and acceleration, and only joint position feedback and the mathematical model of the manipulator are needed. The state estimator converges in finite time. Then, the disturbance observer is designed. By designing the nominal system model of the manipulator, a disturbance observation error is introduced into the closed-loop control so that the performance of the manipulator can track the nominal system. Finally, a sliding mode controller (SMC) based on the fractional differential operator theory is also designed. The value of the sliding mode variable in the derivation of the controller is composed of the fractional derivative of the trajectory tracking error of the manipulator, whereas the fractional differentiation operation uses integration in its realization, and the integration is a low-pass filter, thus, high-frequency noise is suppressed. In the experimental section, the method designed is compared with the conventional sliding mode, which further reveals the rapidity and control accuracy of FSMC.

Observing System Simulation Experiments Exploring Potential Spaceborne Deployment Options for a Differential Absorption Radar Measuring Marine Surface Pressures

AbstractA new technology for remote measurements of marine surface pressure has been proposed, employing a V‐band differential absorption radar and a radiometric temperature sounder to calculate the total column atmospheric mass. Observing System Simulation Experiments (OSSEs) are performed to evaluate the potential impact of Spaceborne Marine Surface Pressure (SMSP) on Numerical Weather Prediction. These experiments build on prior efforts (Privé, McLinden, et al., 2023, https://doi.org/10.16993/tellusa.3254), but with an updated version of the OSSE framework and with more sophisticated simulation of the SMSP observations and a longer experiment period. Several different instrument configurations are compared, including both scanning and non‐scanning orbits. SMSP impacts are calculated for analysis quality and forecast skill, and a forecast sensitivity observation impact tool is employed to place SMSP observations in context with the global observing network. The effects of rain contamination on observation quality are explored. Different magnitudes of simulated SMSP observation error are tested in the context of data assimilation to show the range of potential behaviors. Overall, SMSP observations are found to be most beneficial in the southern hemisphere extratropics, with statistically significant forecast improvements for the first 72 hr of the forecast. A constellation of four non‐scanning SMSP satellites is found to outperform a single scanning instrument with a 250 km wide swath.

Challenges in diagnosis of calcaneal fractures: an examination using the WIDI SIM platform.

The calcaneus is the most commonly fractured tarsal bone. Diagnosis is often challenging due to subtle radiographic changes and requires timely identification to prevent complications, including subtalar arthritis, neurovascular injury, malunion, osteomyelitis, and compartment syndrome. Treatment varies based on fracture type, with non-surgical methods for non-displaced stress fractures and surgical interventions for displaced or intra-articular fractures. This study utilized the Wisdom in Diagnostic Imaging Simulation (WIDI SIM) platform, an emergency imaging simulation designed to assess radiology resident preparedness for independent call. During an 8-hour simulation, residents were tested on 65 cases across various imaging modalities of varying complexity, including normal studies. A single, unique case of calcaneal fracture was included within the simulation in four separate years of testing. Cases were assessed using a standardized grading rubric by subspecialty radiology faculty, with errors subsequently classified by type. A total of 1279 residents were tested in five separate years on the findings of calcaneal fractures of 5 different patients. Analysis revealed a consistent pattern of missed diagnoses across all training years, primarily attributed to observational errors. There was limited improvement with training progression as all training years exhibited similar average performance levels. Calcaneal fractures pose a diagnostic challenge due to their frequent subtle radiographic findings, especially in stress fractures. Simulation-based evaluations using WIDI SIM highlighted challenges in radiology residents' proficiency in diagnosing calcaneal fractures. Addressing these challenges through targeted education and exposure to diverse cases is essential to improve diagnostic accuracy and reduce complications with calcaneal fractures.

Accurate calibration spectra for precision radial velocities

Astronomical spectrographs require calibration of their dispersion relation, for which external sources like hollow-cathode lamps or absorption-gas cells are useful. Laser frequency combs (LFCs) are often regarded as ideal calibrators because they provide the highest accuracy and dense sampling, but LFCs are facing operational challenges such as generating blue visual light or tunable offset frequencies. As an example of an external source, we aim to provide a precise and accurate frequency solution for the spectrum of molecular iodine absorption by referencing to an LFC that does not cover the same frequency range. We used a Fourier Transform Spectrometer (FTS) to produce a consistent frequency scale for the combined spectrum from an iodine absorption cell at 5200– 6200 Å and an LFC at 8200 Å. We used 17 807 comb lines to determine the FTS frequency offset and compared the calibrated iodine spectrum to a synthetic spectrum computed from a molecular potential model. In a single scan, the frequency offset was determined from the comb spectrum with an uncertainty of ∼1 cms−1. The distribution of comb line frequencies is consistent with no deviation from linearity. The iodine observation matches the model with an offset of smaller than the model uncertainties of ∼1 m s−1, which confirms that the FTS zero point is valid outside the range covered by the LFC, and that the frequencies of the iodine absorption model are accurate. We also report small systematic effects regarding the iodine model’s energy scale. We conclude that Fourier Transform Spectrometry can transfer LFC accuracy into frequency ranges not originally covered by the comb. This allows us to assign accurate frequency scales to the spectra of customized wavelength calibrators. The calibrators can be optimized for individual spectrograph designs regarding resolution and spectral bandwidth, and requirements on their long-term stability are relaxed because FTS monitoring can be performed during operation. This provides flexibility for the design and operation of calibration sources for high-precision Doppler experiments.

Cosmological forecasts from the combination of Stage-IV photometric galaxy surveys and the magnification from forthcoming GW observatories

In this work we have investigated the synergy between Stage-IV galaxy surveys and future GW observatories for constraining the underlying cosmological model of the Universe, focussing on photometric galaxy clustering, cosmic shear and GW magnification as cosmological probes. We have implemented a Fisher matrix approach for the evaluation of the full 6×2pt statistics composed by the angular power spectra of the single probes together with their combination. For our analysis, we have in particular considered dynamical dark energy and massive neutrino scenarios. We have found that the improvement to galaxy survey performance is below 1%, in the case of ℓ GW max=100 and a luminosity distance error of σ dL /dL =10%. However, when extending the analysis to ℓ GW max=1000, we find that the GW magnification improves the galaxy survey performance on all the cosmological parameters, reducing their errors by 3%-5%, when σ dL /dL =10%, and by 10%-18% when σ dL /dL =1%, especially for Mν , w 0 and wa . However, here our analysis is unavoidably optimistic: a much more detailed and realistic approach will be needed, especially by including systematic effects. But we can conclude that, in the case of future gravitational wave observatories, the inclusion of the gravitational wave magnification can improve Stage-IV galaxy surveys performance on constraining the underlying cosmological model of the Universe.

Quantitative ultrasound for steatosis assessment using Hepatoscope®: Confounding technical factors

Quantitative ultrasound (QUS) is a well-suited modality to address large-scale screening and monitoring of liver steatosis as it is low-cost, non-invasive, and point-of-care. However, technical and biological confounders lead to difficulties in establishing universal cutoff values for diagnosis. Hepatoscope® remedies this issue as it integrates a unique QUS measurement technique enabling the simultaneous measurements of the backscattering coefficient, the attenuation coefficient and the speed of sound. Such measurements are performed in a large two-dimensional region of interest (ROI) and in a free breathing regime such that a relatively large portion of the liver is sampled. We studied the influence of the size of the ROI, its depth and the number of measurements on the QUS parameters with Hepatoscope® both in vitro on four calibrated phantoms and clinically on 60 participants referred to a hepatology consultation for chronic liver disease. We demonstrated that using large ROIs consistently improves the system reliability and the clinical applicability. We did not observe any systematic effect related to the depth of the ROI. We also showed that averaging consecutive measurements leads to better system reliability and better clinical applicability.

البرمجة الضبابية وتحسين أداء الخوارزميات في بيئة متعددة المتغيرات الرتيبة

In many situations of daily life, when we start statistical analysis, we encounter data that we suffer from inaccuracy in its measurement, either due to observation or measurement errors or the lack of appropriate conditions for collecting this data, such as failure times of machines, equipment, electrical devices, medical, engineering and economic data. In many medical studies, the researcher may have a strong conviction that the regression function used to describe the relationship between two variables has a certain form or formula that can be characterized by certain ordering restrictions such as monotonicity; monotonic regression has important applications in statistics, operations research, and signal processing, and these applications are often characterized by a very large value of n. For such large-scale problems, it is of great practical importance to develop algorithms whose complexity does not increase rapidly with increasing n. It is a nonparametric regression technique that preserves order. In this thesis, the proposed fuzzy autoregressive monotonic method, fuzzy autoregressive monotonic least squares (FILSR), fuzzy autoregressive M estimator (FIM), and fuzzy autoregressive maximum likelihood estimator (FIML) were proposed. The best method for estimating the autoregressive function is the fuzzy autoregressive maximum likelihood method with a mean square error of (0.51455).