Background Model Research Articles

One‐way waveform inversion: Real marine data application

AbstractThe reflection waveform inversion is a powerful technique to build a large‐scale velocity model of the subsurface by fitting the reflected recorded seismic waves. The reflection waveform inversion is designed based on the pillar concept of model and data‐scale separation. Therefore, its success is related to the ability of its forward modelling engine to separate reflected events distinctly from other propagation modes (diving waves, multiples, etc.). However, the standard Born modelling based on the two‐way wave equation may generate internal multiples in case of an insufficient smooth background model. These internal multiples may lead to a distorted sensitivity kernel, which adds more non‐linearity to the inverse problem. In addition, simulating the wave equation using two‐way propagators is still an overburden step of the algorithm especially in large three‐dimensional real surveys. In this proposal, we introduce an alternative to the two‐way wave equation by using a one‐way approach for the reflection waveform inversion. The Born modelling based on one‐way propagators significantly reduces the computational cost and I think it should be allows to relax the smooth background velocity model assumption by restricting the forward modelling to primary reflected waves. After a brief theoretical description of the one‐way waveform inversion, we present an application of the algorithm on the real marine dataset to review its promises and pitfalls. Our approach produces an acceptable large‐scale velocity model whose accuracy is confirmed by the migrated image and the offset gathers.

Robust and efficient FISTA-based method for moving object detection under background movements

Moving object detection is a fundamental task in many video processing applications, such as video surveillance. The robustness and efficiency of background subtraction make it one of the most common methods for detecting moving objects from a video stream. However, adapting background models with moving cameras remains challenging. Major issues include maintaining the model given viewpoint alterations and compensating motions considering depth variations. Moreover, gradual illumination changes, dynamic backgrounds, and complex motions intensify over time in moving camera scenarios, further complicating background model maintenance. In this context, this paper proposes a novel Robust and Online Tensor-based model named ROTAB that incorporates a more implicit consideration of the relationship between sequential frames than the previous methods, allowing for better adaptation to background changes. Moreover, we propose an Improved version of FISTA named IFISTA that employs two strategies to reduce oscillatory behavior and minimize iterations, improving stability and efficiency. Practically, the combination of IFISTA and ROTAB (IFISTA-ROTAB) demonstrates suitable performance for real-time applications. Quantitative and qualitative experiments are conducted on three large-scale datasets, namely CDnet 2014, BMC 2012 and LASIESTA showing the superiority of IFISTA-ROTAB with a gain from two up to seven percent in average.

Trading war: Evolving landscape of discount brokerage in India

The case study chronicles the evolution of Trading Tech sector in India, focusing on the emergence of the digital discount brokerage model. The case delves into the brokerage industry disruption in India, comprising primarily of Industry 4.0-based technological advancements, regulatory policies, and consumer demand shifts. India, a developing economy that was largely characterized by traditional modes of financial transactions, brokerage, and wealth management, shifting to predominantly digital modes with the advent of technology, makes an interesting context. This case sheds light on Zerodha and Groww, India’s biggest online discount brokerage companies, their background, and business model evolution. We intend to make the readers aware of the industry dynamics, the specific business model of the two leading brokerage firms in India, and their challenges. The readers can use various strategy and innovation frameworks to evaluate the strategic choices and business models adopted by the firms and the strategic challenges that need to be addressed to achieve sustained advantage in the financial services market.

A quantum moving target segmentation algorithm for grayscale video based on background difference method

The classical moving target segmentation (MTS) algorithm in a video can segment the moving targets out by calculating frame by frame, but the algorithm encounters a real-time problem as the data increases. Recently, the benefits of quantum computing in video processing have been demonstrated, but it is still scarce for MTS. In this paper, a quantum moving target segmentation algorithm for grayscale video based on background difference method is proposed, which can simultaneously model the background of all frames and perform background difference to segment the moving targets. In addition, a feasible quantum subtractor is designed to perform the background difference operation. Then, several quantum units, including quantum cyclic shift transformation, quantum background modeling, quantum background difference, and quantum binarization, are designed in detail to establish the complete quantum circuit. For a video containing 2m\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$2^{m}$\\end{document} frames (every frame is a 2n×2n\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$2^{n} \ imes 2^{n}$\\end{document} image with q grayscale levels), the complexity of our algorithm is O(n+q)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$(n+q)$\\end{document}. This is an exponential speedup over the classical algorithm and also outperforms the existing quantum algorithms. Finally, the experiment on IBM Q demonstrates the feasibility of our algorithm in this noisy intermediate-scale quantum (NISQ) era.

Accounting for residual errors in atmosphere–ocean background models applied in satellite gravimetry

The Atmosphere and Ocean non-tidal De-aliasing Level-1B (AOD1B) product is widely used in precise orbit determination and satellite gravimetry to correct for transient effects of atmosphere–ocean mass variability that would otherwise alias into monthly mean global gravity fields. The most recent release is based on the global ERA5 reanalysis and ECMWF operational data together with simulations from the general ocean circulation model MPIOM consistently forced with fields from the corresponding atmospheric dataset. As background models are inevitably imperfect, residual errors will consequently propagate into the resulting geodetic products. Accounting for uncertainties of the background model data in a statistical sense, however, has been shown before to be a useful approach to mitigate the impact of residual errors leading to temporal aliasing artefacts. In light of the changes made in the new release RL07 of AOD1B, previous uncertainty assessments are deemed too pessimistic and thus need to be revisited. We here present an analysis of the residual errors in AOD1B RL07 based on ensemble statistics derived from different atmospheric reanalyses, including ERA5, MERRA2 and JRA55. For the oceans, we investigate the impact of both the forced and intrinsic variability through differences in MPIOM simulation experiments. The atmospheric and oceanic information is then combined to produce a new time-series of true errors, called AOe07, which is applicable in combination with AOD1B RL07. AOe07 is further complemented by a new spatial error variance–covariance matrix. Results from gravity field recovery simulation experiments for the planned Mass-Change and Geosciences International Constellation (MAGIC) based on GFZ’s EPOS software demonstrate improvements that can be expected from rigorously implementing the newly available stochastic information from AOD1B RL07 into the gravity field estimation process.

Robust inference of the Galactic Centre gamma-ray excess spatial properties

ABSTRACT The gamma-ray Fermi-LAT Galactic Centre excess (GCE) has puzzled scientists for over 15 yr. Despite ongoing debates about its properties, and especially its spatial distribution, its nature remains elusive. We scrutinize how the estimated spatial morphology of this excess depends on models for the Galactic diffuse emission, focusing particularly on the extent to which the Galactic plane and point sources are masked. Our main aim is to compare a spherically symmetric morphology – potentially arising from the annihilation of dark matter (DM) particles – with a boxy morphology – expected if faint unresolved sources in the Galactic bulge dominate the excess emission. Recent claims favouring a DM-motivated template for the GCE are shown to rely on a specific Galactic bulge template, which performs worse than other templates for the Galactic bulge. We find that a non-parametric model of the Galactic bulge derived from the VISTA Variables in the Via Lactea survey results in a significantly better fit for the GCE than DM-motivated templates. This result is independent of whether a galprop-based model or a more non-parametric ring-based model is used to describe the diffuse Galactic emission. This conclusion remains true even when additional freedom is added in the background models, allowing for non-parametric modulation of the model components and substantially improving the fit quality. When adopted, optimized background models provide robust results in terms of preference for a boxy bulge morphology for the GCE, regardless of the mask applied to the Galactic plane.

Compton Scattering of Electrons in the Intergalactic Medium

This paper investigates the distribution and implications of cosmic-ray electrons within the intergalactic medium (IGM). Utilizing a synthesis model of the extragalactic background, we evolve the spectrum of Compton-included cosmic rays. The energy density distribution of cosmic-ray electrons peaks at redshift z ≈ 2, and peaks in the ∼MeV range. The fractional contribution of cosmic-ray pressure to the general IGM pressure progressively increases toward lower redshift. At mean density, the ratio of cosmic-ray electron to thermal pressure in the IGM PCRe/Pth is 0.3% at z = 2, rising to 1.0% at z = 1, and 1.8% at z = 0.1 (considering only the cosmic rays produced locally by Compton scattering). We compute the linear Landau damping rate of plasma oscillations in the IGM caused by the ∼MeV cosmic-ray electrons, and find it to be of order ∼10−6 s−1 for wavenumbers 1.2 ≲ ck/ω p ≲ 5 at z = 2 and mean density (where ω p is the plasma frequency). This strongly affects the fate of TeV e + e − pair beams produced by blazars, which are potentially unstable to oblique instabilities involving plasma oscillations with wavenumber ck/ωp≈secθ (θ being the angle between the beam and wavevector). Linear Landau damping is at least thousands of times faster than either pair beam instability growth or collisional effects; it thus turns off the pair beam instability except for modes with very small θ (ck/ω p → 1, where linear Landau damping is kinematically suppressed). This leaves open the question of whether the pair beam instability is turned off entirely, or can still proceed via the small-θ modes.

Wasserstein distance-based full waveform inversion method for density reconstruction

Reliable density information is an essential factor in lithologic interpretation and reservoir evaluation. However, density reconstruction is hampered by a lack of long-wavelength information and the crosstalk between different parameters. The powerful nonlinearity in density inversion constrains the multi-parameter full waveform inversion application. This paper introduces the Wasserstein distance into the full waveform inversion for velocity and density based on the optimal transport theory. We construct an objective function with better convexity to avoid the cycle-skipping caused by the poor initial model. Furthermore, due to the sensitivity of the quadratic Wasserstein distance to low-frequency, we add the low-wavenumber component absent in the conventional density inversion. We acquire a long-wavelength density background using optimal transport inversion and then apply it as an initial model for least-squares inversion. Numerical examples show that our optimal transport inversion effectively builds a reasonable density background model for FWI. Density inversion can be improved by incorporating optimal transport and least-squares theory, leading to a reliable quantitative description for petroleum exploration and development.

Fast computation of the observer motion effects induced on monopole frequency spectra for tabulated functions

Context. Various methods have been studied to compute the boosting effects produced by the observer peculiar motion, which modifies and transfers the isotropic monopole frequency spectrum of the cosmic background radiation to higher multipoles. Explicit analytical solutions for the spherical harmonic expansion coefficients were already presented and applied to different types of background spectrum, strongly alleviating the computational effort needed for accurate theoretical predictions. The frequency spectra at higher multipoles are inherently led by higher-order derivatives of the monopole spectrum. Provided that it can be well described by analytic or semi-analytic functions, the computation of its transfer is not affected by numerical instabilities when evaluated at the required level of numerical accuracy. Instead, monopole frequency spectra described by tabulated functions are computed with a relatively poor frequency resolution in comparison with the Doppler shift, which necessitates interpolation of the tabular representation. The spectra are also affected by uncertainties related to intrinsic inaccuracies in the modelling or in the related observational data as well as to limited accuracy in their numerical computation. These uncertainties propagate and increase with the derivative order, possibly preventing the trustworthy computation of the transfer to higher multipoles and of the observed monopole. Aims. We study methods to filter the original function or its derivatives and the multipole spectra, to mitigate numerical instabilities, and to derive reliable predictions of the harmonic coefficients for different cosmic background models. Methods. From the analytical solutions, and assuming that the monopole spectrum can be expanded in Taylor’s series, we derive explicit expressions for the harmonic coefficients up to the multipole ℓmax = 6 in terms of monopole spectrum derivatives. We then consider different low-pass filters: prefiltering in Fourier space of the tabular representation; filtering in both real and Fourier space of the numerical derivatives; interpolation approaches; and a dedicated method based on amplification and deamplification of the boosted signal. We study the quality of these methods when applied to suitable analytical approximations of the tabulated functions, possibly polluted with simulated noise. These methods are then applied to the tabulations. Results. We consider two very different types of monopole spectra superimposed to the cosmic microwave background: the (smooth) extragalactic source microwave background signal from radio-loud active galactic nuclei and the (feature-rich) redshifted 21 cm line, and present our results in terms of spherical harmonic coefficients. The direct prediction of these coefficients can be noisy at ℓ > 1 or, depending on the uncertainty level, even at ℓ ≤ 1. Without assuming a functional form for the extragalactic background spectrum, the Gaussian prefiltering coupled to the sequential real-space filtering of derivatives allows us to derive accurate predictions up to ℓ ∼ 6, while a log–log polynomial representation, which is appropriate over several decades, gives accurate solutions at any ℓ. Instead, it is difficult to characterise the 21 cm line model variety, and so it is relevant to work without assumptions about the underlying function. Typically, the prefiltering provides accurate predictions up to ℓ ≃ 3 or 4, while the further sequential filtering of the derivatives or the boosting amplification and deamplification method improves the results up to ℓ = 4, while also allowing reasonable estimations of the spectrum at higher ℓ. Conclusions. The proposed methods can significantly extend the range of realistic cosmic background models manageable with a fast computation, beyond the cases characterised a priori by analytical or semi-analytical functions. These methods require only an affordable increase in computation time compared to the direct calculation via simple interpolation.

Knowledge Development Trajectories of Intelligent Video Surveillance Domain: An Academic Study Based on Citation and Main Path Analysis.

Smart city is an area where the Internet of things is used effectively with sensors. The data used by smart city can be collected through the cameras, sensors etc. Intelligent video surveillance (IVS) systems integrate multiple networked cameras for automatic surveillance purposes. Such systems can analyze and monitor video data and perform automatic functions required by users. This study performed main path analysis (MPA) to explore the development trends of IVS research. First, relevant articles were retrieved from the Web of Science database. Next, MPA was performed to analyze development trends in relevant research, and g-index and h-index values were analyzed to identify influential journals. Cluster analysis was then performed to group similar articles, and Wordle was used to display the key words of each group in word clouds. These key words served as the basis for naming their corresponding groups. Data mining and statistical analysis yielded six major IVS research topics, namely video cameras, background modeling, closed-circuit television, multiple cameras, person reidentification, and privacy, security, and protection. These topics can boost the future innovation and development of IVS technology and contribute to smart transportation, smart city, and other applications. According to the study results, predictions were made regarding developments in IVS research to provide recommendations for future research.

Impacts of temporal resolution of atmospheric de-aliasing products on gravity field estimation

SUMMARY Despite the increasing accuracies of GRACE (Gravity Recovery and Climate Experiment)/GRACE-FO (GRACE Follow-On) gravity field models through worldwide endeavours, the temporal aliasing effect caused by the imperfect background models used in gravity field modelling is still a crucial factor that degrades the quality of gravity field solutions. Since the important role of temporal resolution of atmospheric de-aliasing models, this paper specifically investigates the influence of temporal resolution on gravity field modelling from the perspectives of frequency, spectral and spatial domains. To this end, we introduced the gravitational acceleration and geoid height derived from the static gravity field GOCO06s in the inner integral. The introduction of the static gravity field has a comparable impact on LRI (Laser Ranging Interferometers) range-rate residuals as the accuracy of the LRI range-rate data, despite its magnitude of being less than 0.1 mm in the spatial domain. This finding also highlights the significance of error level in existing de-aliasing products as a crucial factor that restricts the current accuracy of gravity field solutions. Further analyses show that increasing the temporal resolution from 3 to 1 hr has an insignificant impact on the gravity solutions in both the frequency and spectral domains, which is also smaller than that caused by using different atmospheric data sets. However, in the spatial domain, LRI range-rate residuals can be effectively mitigated in certain regions of the Southern Hemisphere at mid- and high-latitudes by increasing the temporal resolution. Particularly, the discrepancies of mass change estimates brought about by enhancing temporal resolution have distinct characteristics, especially in the Congo River and the Amazon River Basins. The mass changes in terms of equivalent water height derived by using P4M6 filtering show that the maximum root mean square value of spatial differences caused by improving the temporal resolution of the atmospheric de-aliasing models can reach ∼13.4 mm in the subregion of the Congo River Basin. However, using different atmospheric data sets can lead to a maximum difference of ∼16.5 mm. For the Amazon River Basin, the corresponding maximum discrepancy is ∼18.1 mm, and that caused by improving temporal resolution is ∼9.4 mm. We further divide the Congo River Basin into several subregions using a lat-lon regular grid with a spatial resolution of 3°. The subsequent time-series results of mass changes reveal that the maximum contribution of temporal resolution and changes in the atmospheric data sets can reach 11.09 and 21.24 per cent, respectively. This suggests that it is necessary to consider the temporal resolution of de-aliasing products when studying mass changes at a regional scale.

Waveform simulation in PandaX-4T* *This work was supported in part by the National Science Foundation of China (12090060, 12090061), Ministry of Science and Technology of China (2023YFA1606200), and Office of Science and Technology, Shanghai Municipal Government (22JC1410100). We are thankful for the support provided by the Double First Class Plan of the Shanghai Jiao Tong University and Guangzhou Municipal Science

Signal reconstruction through software processing is a crucial component of the background and signal models in the PandaX-4T experiment, which is a multi-tonne dark matter direct search experiment. The accuracy of signal reconstruction is influenced by various detector artifacts, including noise, dark count of photomultiplier, photoionization of impurities in the detector, and other relevant considerations. In this study, we presented a detailed description of a semi-data-driven approach designed to simulate a signal waveform. This work provides a reliable model for the efficiency and bias of the signal reconstruction in the data analysis of PandaX-4T. By comparing critical variables that relate to the temporal shape and hit pattern of the signals, we found good agreement between the simulation and data.

AIUB-GRACE gravity field solutions for G3P: processing strategies and instrument parameterization

Abstract. This paper discusses strategies to improve the Gravity Recovery And Climate Experiment (GRACE) monthly solutions computed at the Astronomical Institute of the University of Bern (AIUB) which are contributing to the Horizon 2020 project G3P – Global Gravity-based Groundwater Product. To improve the AIUB-GRACE gravity field solutions, we updated the use of the Level-1B observations, adapted the background models, and improved the processing strategies in terms of instrument screening and parameterization. We used the Release 3 K-band product (KBR) and star camera data (L1B RL03), and we adopted Release 6 of the Atmospheric and Ocean De-aliasing (AOD1B RL06) product. For the accelerometer parameterization, we used arc-wise full scale factor matrix and arc-wise third-order polynomial biases. The new accelerometer parameterization is effective in reducing noise over the oceans in gravity field solutions, especially for the late years of the GRACE mission when the thermal control was switched off. In this paper, we show that the outliers in the KBR antenna offset correction (AOC) are projected into the range rate residuals; therefore, we used the KBR AOC as the main source for outlier detection and eliminated the AOC above a threshold for all data before the gravity field processing. The full time series of GRACE AIUB-G3P gravity field solutions (Darbeheshti et al., 2023b) is available at https://doi.org/10.5880/ICGEM.2023.001.

BERTground: A Transformer-Based Model of Background Spectra on the ISS-Based NICER Space Telescope

The Neutron star Interior Composition Explorer (NICER) is an International Space Station (ISS)-based Space Telescope developed by NASA and devoted to the study of high-energy X-Ray sources in the universe, including but not limited to neutron stars, pulsars, and black holes in stellar systems and active galactic nuclei (AGN). One prominent problem with NICER observations is the highly variable background spectra, obscuring actual signals of astrophysical sources and negatively affecting scientific analysis of the targets. Therefore, obtaining accurate estimations of the background spectra is crucial to filter the noise and facilitate better scientific discoveries of new astronomical objects. In this paper, we propose the very first Deep Neural Network architecture to model the NICER background spectra variation using information about the spacecraft and telescope associated with each observation. In particular, we develop a BERT-based architecture with tokenizers applied to different groups of features in our tabular dataset. We also introduce an adapted Tabular Deep Residual Network architecture as the predictor following the Transformer modules in our network. We show that our model outperforms the current state-of-the-art background model developed by the NICER team in most evaluation metrics. Finally, we discuss pathways and future work for the deployment of this model on NASA’s next versions of HEASARC Software packages.

Preserving the spots: Jaguar (Panthera onca) distribution and priority conservation areas in Colombia.

The jaguar (Panthera onca) is a charismatic species considered Vulnerable in Colombia but yet largely unknown in the country. The species is mostly threatened by the continuous decline in its habitats, mostly derived from deforestation and habitat loss, additional to hunting and conflicts with humans. Thus, the future of jaguars in Colombia depends on protecting and recovering existing habitats. The aims of this study were to 1) evaluate jaguar distribution and identify the remnant patches of habitat in Colombia, 2) define an ecological connectivity network within the country, and 3) propose a priority areas portfolio for the conservation and recovery of jaguars. We used a presence background model for estimating species potential distribution and subsequently identified remaining habitat patches across the country based on land cover and species-specific ecological attributes. We then created an ecological connectivity network based on circuit theory and following a multi-criteria approach identified jaguar priority areas for conservation (JPCA) and recovery (JPRA). Jaguar potential distribution comprises 1103122.43 km2, from which 56.71% maintain suitable patches of potential habitat. We identified 960 corridors between remnant patches of natural or semi-natural vegetation. Based on the criteria, JPCAs with greater importance were identified in each of the five Colombian regions. JPRAs were located mainly towards the Andean and Caribbean regions. These JPCAs and JPRAs could serve as a guide for designing and implementing management strategies for the long-term conservation and recovery of the species in Colombia.

Abstract 3669: Clinical application of urinary DNA methylation biomarkers for identifying patients with non-muscle invasive bladder cancer

Abstract Introduction Differences in DNA methylation along cell-free DNA (cfDNA) fragments are known to exist between cancer patients and healthy individuals. Profiling these aberrant methylation patterns in urinary cfDNA (ucfNDA) holds great promise for the non-invasive detection and monitoring of bladder tumors, including non-muscle invasive bladder cancer (NMIBC). Here, we analyzed fragment-level DNA methylation patterns in urine samples obtained prior to repeat transurethral resection of a bladder tumor (reTURBT) from a cohort of NMIBC patients. The results were compared to traditional clinical disease assessment and mutation-based tumor fractions. We found a strong concordance between DNA methylation abnormality and clinical diagnosis, as well as tumor fractions, highlighting the potential of epigenetic alterations as a non-invasive indicator of bladder cancer status. Methods Patients with high-risk NMIBC (n = 30) were prospectively enrolled prior to reTURBT. The PredicineEPIC™ genome-wide methylation analysis and PredicineBEACON™ MRD profiling was performed on pre-repeat TURBT urine samples. Abnormally methylated genomic fragments were detected against a background model built from a panel of plasma samples from healthy donors. Results DNA methylation abnormality was significantly higher (p = 4.8 × 10−6) in samples classified as NIMBC positive by a clinical pathologist (n = 19) compared to negative samples (n = 11), as well as compared to hold-out healthy donor samples (n=24; p = 7.0 × 10−9). Classifying the samples based on a DNA methylation score cut-off derived from healthy donor samples, 24/30 samples showed concordant disease status. Furthermore, DNA methylation abnormality scores correlated positively with mutation-based tumor fractions (r = 0.62), with higher grade samples showing a stronger correlation (r = 0.81; n = 13) than lower grade samples (r = 0.54; n = 17). Conclusion We demonstrate the utility of methylation patterns extracted ucfDNA samples obtained prior to reTURBT to detect and monitor NMIBC. Urinary DNA methylation patterns were highly concordant with clinical pathology status and as well as tumor fractions determined from mutation analysis. Citation Format: Giancarlo Bonora, Ziqi Zhu, Billie Gould, Binggang Xiang, Kemin Zhou, Shidong Jia, Roger Li, Pan Du. Clinical application of urinary DNA methylation biomarkers for identifying patients with non-muscle invasive bladder cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3669.

CPhaMAS: An online platform for pharmacokinetic data analysis based on optimized parameter fitting algorithm

Background and objectiveClinical pharmacological modeling and statistical analysis software is an essential basic tool for drug development and personalized drug therapy. The learning curve of current basic tools is steep and unfriendly to beginners. The curve is even more challenging in cases of significant individual differences or measurement errors in data, resulting in difficulties in accurately estimating pharmacokinetic parameters by existing fitting algorithms. Hence, this study aims to explore a new optimized parameter fitting algorithm that reduces the sensitivity of the model to initial values and integrate it into the CPhaMAS platform, a user-friendly online application for pharmacokinetic data analysis. MethodsIn this study, we proposed an optimized Nelder-Mead method that reinitializes simplex vertices when trapped in local solutions and integrated it into the CPhaMAS platform. The CPhaMAS, an online platform for pharmacokinetic data analysis, includes three modules: compartment model analysis, non-compartment analysis (NCA) and bioequivalence/bioavailability (BE/BA) analysis. Our proposed CPhaMAS platform was evaluated and compared with existing WinNonlin. ResultsThe platform was easy to learn and did not require code programming. The accuracy investigation found that the optimized Nelder-Mead method of the CPhaMAS platform showed better accuracy (smaller mean relative error and higher R2) in two-compartment and extravascular administration models when the initial value was set to true and abnormal values (10 times larger or smaller than the true value) compared with the WinNonlin. The mean relative error of the NCA calculation parameters of CPhaMAS and WinNonlin was <0.0001 %. When calculating BE for conventional, high-variability and narrow-therapeutic drugs. The main statistical parameters of the parameters Cmax, AUCt, and AUCinf in CPhaMAS have a mean relative error of <0.01% compared to WinNonLin. ConclusionsIn summary, CPhaMAS is a user-friendly platform with relatively accurate algorithms. It is a powerful tool for analysing pharmacokinetic data for new drug development and precision medicine.

Design and analysis of shadow detection and removal scheme in real-time still images

This research’s main objective is to study and evaluate the detection and removal of undesired shadows from still images since these shadows might mask important information caused by light sources and other obstructions. A variety of methods for detecting and eliminating shadows as well as object tracking approaches based on movement estimation and identification are investigated. This includes shadow removal methods like background subtraction, which are intended to improve obstacle recognition of the source item and increase the accuracy of shadow removal from objects. When new items enter the frame, they are first distinguished from the background using a reference frame. The tracking procedure is made more difficult by the merging of the shadow with the foreground object. The approach highlights the difficulties in object detection owing to frequent occurrences of obstacles by using morphological procedures for shadow identification and removal. The proposed approach uses feature extraction is also discussed, highlighting its importance in image processing research and the use of suggested methods to get over obstacles in image sequences. The proposed method for shadow identification and removal offers a novel approach to improve image processing when dealing with still images. The purpose of this technique is to better detect and remove shadows from images, which will increase the precision of object tracking and detection. Depending on the type of images being processed, the process begins with initializing a background model, which is based on a static image background.

Externally orienting cues improve cognitive control in OCD

Background and objectivesAn executive overload model of obsessive-compulsive disorder (OCD) posits that broad difficulties with executive functioning in OCD result from an overload on the executive system by obsessive thoughts. It implies that, if individuals with OCD “snap out” of their obsessive thoughts, their performance on neurocognitive tasks will improve. MethodsWe test this prediction using the revised Attention Network Test, ANT-R, and distinct subsamples of data from unmedicated OCD and healthy controls (HC). ANT-R includes Simon and Flanker tasks; in both, incongruent trials take longer to resolve (‘conflict costs’). On some trials, a warning cue helps participants to respond faster (‘alerting benefits’). In OCD (N = 34) and HC (N = 46), matched on age, IQ, and sex, we tested (1) the effect of OCD on alerting benefits, and (2) the effect of OCD on warning cue related reductions in conflict costs. In a distinct subsample of OCD (N = 32) and HC (N = 51), we assessed whether alerting benefits and cue-related reductions in conflict costs are associated differently with different OCD symptoms. ResultsA warning cue can help individuals with OCD more than HC to improve performance on Simon and Flanker tasks. This effect is positively associated with severity of contamination symptoms. LimitationsThis study did not directly assess how distracted participants are by obsessive thoughts. It relied on the ANT-R subtraction measures. Symptom severity was assessed using self-report measures. ConclusionsDifficulties in resolving conflict during decision-making in OCD can be modulated by a warning cue presented immediately before an attentional task.

GPS displacement dataset for the study of elastic surface mass variations

Abstract. Quantification of uncertainty in surface mass change signals derived from Global Positioning System (GPS) measurements poses challenges, especially when dealing with large datasets with continental or global coverage. We present a new GPS station displacement dataset that reflects surface mass load signals and their uncertainties. We assess the structure and quantify the uncertainty of vertical land displacement derived from 3045 GPS stations distributed across the continental US. Monthly means of daily positions are available for 15 years. We list the required corrections to isolate surface mass signals in GPS estimates and screen the data using GRACE(-FO) as external validation. Evaluation of GPS time series is a critical step, which identifies (a) corrections that were missed, (b) sites that contain non-elastic signals (e.g., close to aquifers), and (c) sites affected by background modeling errors (e.g., errors in the glacial isostatic model). Finally, we quantify uncertainty of GPS vertical displacement estimates through stochastic modeling and quantification of spatially correlated errors. Our aim is to assign weights to GPS estimates of vertical displacements, which will be used in a joint solution with GRACE(-FO). We prescribe white, colored, and spatially correlated noise. To quantify spatially correlated noise, we build on the common mode imaging approach by adding a geophysical constraint (i.e., surface hydrology) to derive an error estimate for the surface mass signal. We study the uncertainty of the GPS displacement time series and find an average noise level between 2 and 3 mm when white noise, flicker noise, and the root mean square (rms) of residuals about a seasonality and trend fit are used to describe uncertainty. Prescribing random walk noise increases the error level such that half of the stations have noise &gt; 4 mm, which is systematic with the noise level derived through modeling of spatially correlated noise. The new dataset is available at https://doi.org/10.5281/zenodo.8184285 (Peidou et al., 2023) and is suitable for use in a future joint solution with GRACE(-FO)-like observations.