Domain Of Interest Research Articles

Stability analysis and numerical evaluations of a COVID-19 model with vaccination

A novel (nonlinear) mathematical model for the transmission of Coronavirus 19 (COVID-19) with eight compartments and considering the impact of vaccination is examined in this manuscript. The qualitative behavior of the system such as the boundedness of solutions, the basic reproduction number, and the stability of the equilibrium points is investigated in detail. Some domestic real data collected from the Kerman University of Medical Science (KUMC) is used to estimate the parameters of the proposed model. We predict the dynamical behavior of the system through numerical simulations based on a combined spectral matrix collocation methodology. In this respect, we first linearize the nonlinear system of equations by the method of quasilinearization (QLM). Hence, the shifted version of Chebyshev polynomials of the second kind (SCPSK) is utilized along with the domain-splitting strategy to acquire the solutions of the system over a long time interval. The uniform convergence and upper bound estimation of the SCPSK bases are proved in a rigorous manner. Moreover, the technique of residual error functions is used to testify the accuracy of the QLM-SCPSK method. The presented numerical results justify the robustness and good accuracy of the QLM-SCPSK technique. The achieved numerical orders of convergence indicate that the QLM-SCSK algorithm has exponential rate of convergence. Using the linearization technique in one hand and the domain-splitting strategy on the other hand, enable us to predict the behaviour of similar disease problems with high accuracy and maximum efficiency on an arbitrary domain of interest.

Integrating the patient voice: patient-centred and equitable clinical risk prediction for kidney health and disease.

Personalized approaches to care are increasingly common in clinical nephrology. Although risk prediction models are developed to estimate the risk of kidney-disease related outcomes, they infrequently consider the priorities of patients they are designed to help. This review discusses certain steps in risk prediction tool development where patients and their priorities can be incorporated. Considering principles of equity throughout the process has been the focus of recent literature. Applying a person-centred lens has implications for several aspects of risk prediction research. Incorporating the patient voice may involve partnering with patients as researchers to identify the target outcome for the tool and/or determine priorities for outcomes related to the kidney disease domain of interest. Assessing the list of candidate predictors for associations with inequity is important to ensure the tool will not widen disparity for marginalized groups. Estimating model performance using person-centred measures such as model calibration may be used to compare models and select a tool more useful to inform individual treatment decisions. Finally, there is potential to include patients and families in determining other elements of the prediction framework and implementing the tool once development is complete.

Prospective memory performance in veterans with and without histories of mild traumatic brain injury: effect of the apolipoprotein E (APOE) ε4 genotype

ABSTRACT Objective Identifying factors that moderate cognitive outcomes following mild traumatic brain injury (mTBI) is crucial. Prospective memory (PM) is a cognitive domain of interest in mTBI recovery as it may be especially sensitive to TBI-related changes. Since studies show that genetic status – particularly possession of the apolipoprotein E (APOE) ε4 allele – can modify PM performance, we investigated associations between mTBI status and APOE-ε4 genotype on PM performance in a well-characterized sample of Veterans with neurotrauma histories. Methods 59 Veterans (mTBI = 33, Military Controls [MCs] = 26; age range: 24–50; average years post-injury = 10.41) underwent a structured clinical interview, neuropsychological assessment, and genotyping. The Memory for Intentions Test (MIST) measured PM across multiple subscales. ANCOVAs, adjusting for age and posttraumatic stress symptoms, tested the effects of mTBI status (mTBI vs. MC) and ε4 status (ε4+ vs. ε4−) on MIST scores. Results Veterans with mTBI history performed more poorly compared to MCs on the MIST 15-min delay (p=.002, ηp 2 =.160), Time Cue (p = .003, ηp 2 =.157), and PM Total (p = .016, ηp 2 =.102). Those with at least one copy of the ε4 allele performed more poorly compared to ε4- Veterans on the MIST 15-min delay (p = .011, ηp 2 =.113) and PM Total (p = .048, ηp 2 = .071). No significant interactions were observed between mTBI and APOE-ε4 status on MIST outcomes (ps>.25). Within the mTBI group, APOE-ε4+ Veterans performed worse than APOE-ε4− Veterans on the MIST 15-min delay subscale (p = .031, ηp 2 = .150). Conclusions mTBI history and APOE-ε4 genotype status were independently associated with worse PM performance compared to those without head injury histories or possession of the APOE-e4 genotype. Performance on the MIST 15-min delay was worse in Veterans with both risk factors (mTBI history and APOE-ε4 positivity). Findings suggest that genetic status may modify outcomes even in relatively young Veterans with mTBI histories. Future research examining longitudinal associations and links to neuroimaging and biomarker data are needed.

The role of supply chain analytics on supply chain responsiveness, resilience, and restoration (3Rs) capabilities in the United Arab Emirates

AbstractThis paper responds to calls for research and conceptualizes resilience, responsiveness and reconfigurable (3Rs) capabilities required for handling disruptions in pre‐disruption, during disruption and post‐disruption stages. This paper examined the impact of analytics on 3Rs capabilities in the supply chain context building on the tenets of contingency theory. Drawing on survey data obtained from individuals with managerial positions in firms operating in the United Arab Emirates (n = 151), this study investigates how supply chain analytics foster 3Rs capacities in the face of a pandemic using canonical correlation and linear regression analyses. Supply chain analytics is a relevant predictor for supply chain (i) resilience, (ii) responsiveness, and (iii) restoration/reconfiguration. Inevitable supply chain disruptions call for investment and integration of digital tools like supply chain analytics because they offer timely information for informed decision‐making. This work provides theoretical contributions to the domain of interest by exploring the role of supply chain analytics on the 3Rs capabilities.

CovXtreme : MATLAB software for non-stationary penalised piecewise constant marginal and conditional extreme value models

The covXtreme software provides functionality for estimation of marginal and conditional extreme value models, non-stationary with respect to covariates, and environmental design contours. Generalised Pareto (GP) marginal models of peaks over threshold are estimated, using a piecewise-constant representation for the variation of GP threshold and scale parameters on the (potentially multidimensional) covariate domain of interest. The conditional variation of one or more associated variates, given a large value of a single conditioning variate, is described using the conditional extremes model of Heffernan and Tawn (2004), the slope term of which is also assumed to vary in a piecewise constant manner with covariates. Optimal smoothness of marginal and conditional extreme value model parameters with respect to covariates is estimated using cross-validated roughness-penalised maximum likelihood estimation. Uncertainties in model parameter estimates due to marginal and conditional extreme value threshold choice, and sample size, are quantified using a bootstrap resampling scheme. Estimates of environmental contours using various schemes, including the direct sampling approach of Huseby et al. 2013, are calculated by simulation or numerical integration under fitted models. The software was developed in MATLAB for metocean applications, but is applicable generally to multivariate samples of peaks over threshold data. The software and case study data can be downloaded from GitHub, with an accompanying user guide.

Optimal decay rates in Sobolev norms for singular values of integral operators

The regularity of integration kernels forces decay rates of singular values of associated integral operators. This is well-known for symmetric operators with kernels defined on (a,b)×(a,b), where (a,b) is an interval. Over time, many authors have studied this case in detail [9,10,4,15,12]. The case of spheres has also been resolved [3]. A few authors have examined the higher dimensional case or the case of manifolds [1,6]. Typically, these authors have provided decay estimates of singular values in lp norms, p≥1 or in case of faster decay due to regularity, lp quasi-norms, 0<p<1. With that approach, it is straightforward to show that their estimates are optimal using periodic kernels obtained from Fourier series. Our new approach for deriving decay estimates of these singular values uses Weyl's asymptotic formula for Neumann eigenvalues [21] that we combine to an appropriately defined inverse Laplacian. We obtain decay estimates in the form nα where for the n -th singular value where α depends on dimension and on the Sobolev regularity of the kernel. Since we are interested in optimal estimates in case of regular kernels, instead of writing an upper bound by a constant times nα, we use the singular values of the kernel obtained by differentiation. While in [1,6]lp estimates were proven to be optimal by simply considering periodic Fourier series, these Fourier series do not provide sharp results for our estimates. Instead, series of Neumann eigenfunctions for the Laplacian that are specific to the domain of interest are used to prove that our decay estimates are optimal. Finally, we cover the case of real analytic kernels where we are also able to derive optimal estimates.

Preface: 8th International Conference on Environmental Science and Material Application (ESMA 2024)

The organizing committee is proud to present the proceedings of the 2024 8th International Conference on Environmental Science and Material Application (ESMA 2024), held in Lisbon, Portugal during February 03-04, 2024. We warmly welcome prospected authors submit new research papers to ESMA 2024, and share the valuable experiences with the scientist and scholars around the world. The conference is an international forum for the presentation of technological advances and research results in the fields of natural resources, environmental science, environmentally friendly materials, green energy and power engineering. The conference will bring together leading researchers, engineers and scientists in the domain of interest from around the world. By submitting a paper to ESMA 2024, the authors agreed to the review process and understood that papers would undergo a peer-review process. Manuscripts were reviewed by appropriately qualified experts in the field selected by the conference committee, who took detailed comments and the authors would submit a revised version that took into account this feedback. All papers were reviewed using a double-blind review process: authors declared their names and affiliations in the manuscript for the reviewers to see, but reviewers did not know each other's identities, nor did the authors receive information about who had reviewed their manuscript. The committees of ESMA 2024 invested great efforts in reviewing the papers submitted to the conference and organizing the sessions to enable the participants to gain maximum benefit. We do believe your great participation will make our conference success and we would appreciate your participation. With our warmest regards, Conference Organizing Chair Lisbon, Portugal

An offline coupling approach for efficient SPH simulations of long-distance tsunami events using wave source boundary condition

The entire procedure of tsunami events consists of wave generation and propagation toward the coastal structures encompassing the long-range system domain. Therefore, instead of an integrated continuous simulation, two or several consecutive simulations are preferred for numerical efficiency, where the hydrodynamic information within a particular domain from the preceding simulation can be utilized for the wave sources in the subsequent simulation. In this study, as an offline coupling approach, the wave source boundary condition is proposed based on the particle-based Lagrangian framework. This boundary condition is integrated with the solution algorithm of the smoothed particle hydrodynamics (SPH) in-house parallel code. In the dambreak simulation, it is validated that the wave source boundary condition can encompass both the forward and reverse flows, where the numerical results of the full and partial simulations show good agreement in terms of the wave profile, velocity distributions, and measured pressure histories. Thereafter, the full and partial simulations of a tsunami experiment are carried out. The numerical results in the partial simulation agree well with those obtained in the full simulation in terms of the particle distributions, physical quantities, and loading histories (e.g., pressure, acceleration) on the shoreline structure. It is demonstrated that the proposed offline coupling approach using the wave source boundary condition is compatibly utilized with the developed in-house SPH parallel solver, facilitating the detailed FSI simulations within a reduced domain of interest for long-distance tsunami-like phenomena.

GAN-Based Data Augmentation with Vehicle Color Changes to Train a Vehicle Detection CNN

Object detection is a challenging task that requires a lot of labeled data to train convolutional neural networks (CNNs) that can achieve human-level accuracy. However, such data are not easy to obtain, as they involve significant manual work and costs to annotate the objects in images. Researchers have used traditional data augmentation techniques to increase the amount of training data available to them. A recent trend in object detection is to use generative models to automatically create annotated data that can enrich a training set and improve the performance of the target model. This paper presents a method of training the proposed ColorGAN network, which is used to generate augmented data for the target domain of interest with the least compromise in quality. We demonstrate a method to train a GAN with images of vehicles in different colors. Then, we demonstrate that our ColorGAN can change the color of vehicles of any given vehicle dataset to a set of specified colors, which can serve as an augmented training dataset. Our experimental results show that the augmented dataset generated by the proposed method helps enhance the detection performance of a CNN for applications where the original training data are limited. Our experiments also show that the model can achieve a higher mAP of 76% when the model is trained with augmented images along with the original training dataset.

Financial assets prediction based on ARIMA, Random Forest and GRU

As a matter of fact, financial asset prediction is a domain of great interest because of its potential to generate revenue. In reality, financial asset prediction models have evolved from basic time series analysis models to contemporarily hybrid models with the help of machine learning algorithms. To be specific, this study will introduce and analyse three popular financial asset forecasting models and their hybrids in terms of the properties they have demonstrated in the completed studies. In reality, good results have been achieved using a time series analysis method named autoregressive integrated moving average (ARIMA) for capturing linear elements. According to the analysis, in dealing with data noise and interpretability, Random Forest (RF) algorithm, a machine learning technique, produced positive outcomes. Deep learning technique gate recurrent unit (GRU) produced positive outcomes in terms of prediction accuracy. Based on the evaluations, this study indicates future research directions in the field of financial asset forecasting by analysing and organizing the characteristics of three different mainstream models.

Performance and CO2 emission of a single cylinder compression ignition engine powered by Khaya senegalensis non-edible seeds fuel blends

This work aimed at investigating blends of Khaya senegalensis biodiesel in a compression ignition engine, attempting to improve engine performance and reduce CO2 emission compared with conventional diesel. Analysis of System (ANSYS) was used to predict in-cylinder behavior of the fuel. ANSYS SpaceClaim generated the geometric model on which 5° sector and mesh refinement was on ANSYS Internal Combustion Engine Modeler (ICEM). Computational domain of interest lies within the compression and expansion strokes. Experimental validation followed: 5% biodiesel, 95% diesel (B5); 15% biodiesel, 85% diesel (B15); 25% biodiesel, 75% diesel (B25); pure diesel (D100); pure biodiesel (B100) in volume proportions. B15 has the highest brake mean effective pressure (BMEP) of 4 bar as load increases. An experimental and numerical comparison reveals pressure declination against speed increment. Ignition temperature fluctuated between 799.76 and 806.256 K for D100 and 760.73–790.62 K for B100 within 1800–2800 rpm speed limit prediction. Power and brake thermal efficiency (BTE) had parallel load increment with all blends. CO2 emission on increasing load conditions were 47.01%, 8.07%, 21.72% and 6.06% for B5, B15, B25, and B100 respectively lower than D100. Pressure and temperature contours gave proper combustion predicted behaviors. All blends possess replaceable performance potential for D100 however, B5 offers better reliable potentials.

Facile synthesis of CuO-Ag2O hybrid metal oxide composite using carica papaya, cocooning with hydroxyapatite, and photocatalytic degradation of organic dyes

The synthetic strategy of mixed metal oxide nanoparticle has emerged to be one of the interesting domains in material science. Here carica papya fruit extract assisted synthesis of silver copper oxide is reported via co precipitation method. For the comparative study, using similar precursor material silver copper mixture composite is synthesized using conventional base via coprecipitation method. The composites were subjected to degradation of methylene blue for observation of its efficiency as a photocatalyst. Hydroxyapatite was obtained from fish bones and was used for encapsulating the composite material. The resultant composite was analysed using XRD, SEM-EDS, TEM, XPS and FT-IR techniques. The uncapped composites reported to degrade 90.12 % and 91.5 % of methylene blue within 60 min. The conventionally synthesized composite degraded 30 ppm Bibersch scarlet up to 86.04 % in 40 min under assistance of sunlight. The encapsulated composite was successful in degradation of Brilliant green 89.9 % in 140 min and eosin yellow 92.2 % in 120 min. The catalyst in encapsulated and un encapsulated form reveals its efficiency to be a potential photo catalyst.

Abstract 7055: Elucidation of the interplay between phospholipid byosynthesis and oxidative stress in the pathogenesis of renal cell carcinoma

Abstract Introduction: Renal cell carcinoma (RCC) is a primary malignant kidney tumor. We reported on the critical role of Protein of Relevant Evolutionary and Lymphoid Interest Domain 2 (PRELID2) in cell proliferation, emphasizing its regulatory function on mitochondrial ROS and maintenance of mitochondrial spare respiratory capacity. Recent studies have begun to clarify the molecular mechanism of phospholipid biosynthesis by PRELID1 and PRELID3 under oxidative stress conditions. Inspired by this evidence, we analyzed the relationship between PRELID2 and phospholipid biosynthesis, aiming to elucidate the molecular mechanism of PRELID2 related to the control of mitochondrial ROS. Methods: To analyze the dynamics of phospholipids and related mitochondrial molecules induced by oxidative stress, we examined the expression of YME1L and its substrates (OPA1, PRELID1) using Western blotting, which has been shown to be related to the turnover of other PRELI family proteins. Lipidomic analysis was performed using LC-MS on samples from HEK293 cells with stable expression of PRELID2 and control cells, as well as renal carcinoma cell lines with suppressed PRELID2 expression using siRNA and control cells. Additionally, we analyzed the expression of mitochondrial respiratory chain complexes Cytochrome C and COX4 and their relationship with PRELID2 expression via Western blotting. Results: Oxidative stress led to a noticeable decline in YME1L expression, which was correlated with decreased OPA1 and PRELID1 levels in control cells. Conversely, PRELID2-overexpressing cells retained stable expression of OPA1 and PRELID1. Furthermore, these cells exhibited a significant reduction in phosphatidyl glycerol (PG) levels. Suppressing PRELID2 expression in 786-O cells using siRNA resulted in significantly higher PG levels. When analyzing the molecules of the mitochondrial respiratory chain during oxidative stress induction, we observed that although oxidative stress induced a decline in cytochrome C expression in control cells, it remained consistent in PRELID2-expressing cells. However, PRELID2 expression did not affect COX4 levels. Conclusions: Our findings underscore the pivotal role of PRELIDs in maintaining mitochondrial homeostasis, proving instrumental in the oxidative stress-driven progression of renal cancer. Citation Format: Renpei Kato, Daiki Ikarashi, Shigekatsu Maekawa, Mitsugu Kanehira, Ryo Takata, Yosuke Matsushita, Tetsuro Yoshimaru, Tomoya Fukawa, Toyomasa Katagiri, Wataru Obrara. Elucidation of the interplay between phospholipid byosynthesis and oxidative stress in the pathogenesis of renal cell carcinoma [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 7055.

Implicit-explicit time integration for the immersed wave equation

Efficient solution strategies for wave propagation problems with complex geometries are essential in many engineering fields. Immersed boundary methods simplify mesh generation by embedding the domain of interest into an extended domain that is easy to mesh, introducing the challenge of dealing with cells that intersect the domain boundary. We consider the finite cell method that extends the weak form from the physical into the extended domain, multiplied by a small value, to stabilize badly cut cells. Combined with explicit time integration methods, the presence of finite cells with very little support in the physical domain results in tiny critical time step sizes. While the finite cell stabilization limits how small the critical time step size can become, this limit is still restrictive for many applications. Explicit transient analyses commonly use the spectral element method due to its natural way of obtaining diagonal mass matrices through nodal lumping. The resulting method is very efficient since nodal lumping renders the solution of the equation systems trivial while not reducing the accuracy or the critical time step size. The combination of the spectral element and finite cell methods is called the spectral cell method. Unfortunately, a direct application of nodal lumping in the spectral cell method is impossible due to the special quadrature necessary to treat the discontinuous integrand inside the cut cells. The existing approaches to lump the mass matrices of cut cells significantly reduce the accuracy of the approximation.We analyze an implicit-explicit (IMEX) time integration method to exploit the advantages of the nodal lumping scheme for uncut cells on one side and the unconditional stability of implicit time integration schemes for cut cells on the other. In this hybrid, immersed Newmark IMEX approach, we use explicit second-order central differences to integrate the uncut degrees of freedom that lead to a diagonal block in the mass matrix and an implicit trapezoidal Newmark method to integrate the remaining degrees of freedom (those supported by at least one cut cell). The immersed Newmark IMEX approach preserves the high-order convergence rates and the geometric flexibility of the finite cell method while retaining the efficiency of the nodal lumping scheme for uncut cells without compromising the critical time step size. We analyze a simple system of spring-coupled masses to highlight some of the essential characteristics of Newmark IMEX time integration. We then solve the scalar wave equation with immersed boundaries on two- and three-dimensional examples with significant geometric complexity to show that our approach is more efficient than state-of-the-art time integration schemes when comparing accuracy and runtime. While we focus on the finite and spectral cell methods, we expect other immersed methods to benefit equally from Newmark IMEX time integration.

On the long-time behaviour of solutions to unforced evolution Navier–Stokes equations under Navier boundary conditions

We study the asymptotic behaviour of the solutions to Navier–Stokes unforced equations under Navier boundary conditions in a wide class of merely Lipschitz domains of physical interest. The paper draws its main motivation from celebrated results by Foias and Saut (1984) under Dirichlet conditions; here the choice of the boundary conditions requires carefully considering the geometry of the domain Ω, due to the possible lack of the Poincaré inequality in presence of symmetries. In non-axially symmetric domains we show the validity of the Foias–Saut result about the limit at infinity of the Dirichlet quotient, in axially symmetric domains we provide two invariants of the flow which completely characterize the motion and we prove that the Foias–Saut result holds for initial data belonging to one of the invariants.

Reproducing Popularity Bias in Recommendation: The Effect of Evaluation Strategies

The extent to which popularity bias is propagated by media recommender systems is a current topic within the community, as is the uneven propagation among users with varying interests for niche items. Recent work focused on exactly this topic, with movies being the domain of interest. Later on, two different research teams reproduced the methodology in the domains of music and books, respectively. The results across the different domains diverge. In this paper, we reproduce the three studies and identify four aspects that are relevant in investigating the differences in results: data, algorithms, division of users in groups and evaluation strategy. We run a set of experiments in which we measure general popularity bias propagation and unfair treatment of certain users with various combinations of these aspects. We conclude that all aspects account to some degree for the divergence in results, and should be carefully considered in future studies. Further, we find that the divergence in findings can be in large part attributed to the choice of evaluation strategy.

Evidence of large-scale conceptual disarray in multi-level taxonomies in Wikidata

The distinction between types and individuals is key to most conceptual modeling techniques and knowledge representation languages. Despite that, there are a number of situations in which modelers navigate this distinction inadequately, leading to problematic models. We show evidence of a large number of representation mistakes associated with the failure to employ this distinction in the Wikidata knowledge graph, which can be identified with the incorrect use of instantiation, which is a relation between an instance and a type, and specialization (or subtyping), which is a relation between two types. The prevalence of the problems in Wikidata’s taxonomies suggests that methodological and computational tools are required to mitigate the issues identified, which occur in many settings when individuals, types, and their metatypes are included in the domain of interest. We conduct a conceptual analysis of entities involved in recurrent erroneous cases identified in this empirical data, and present a tool that supports users in identifying some of these mistakes.

Enhancing Human-in-the-Loop Ontology Curation Results through Task Design

The success of artificial intelligence (AI) applications is heavily dependent on the quality of data they rely on. Thus, data curation, dealing with cleaning, organising, and managing data, has become a significant research area to be addressed. Increasingly, semantic data structures such as ontologies and knowledge graphs empower the new generation of AI systems. In this article, we focus on ontologies as a special type of data. Ontologies are conceptual data structures representing a domain of interest and are often used as a backbone to knowledge-based intelligent systems or as an additional input for machine learning algorithms. Low-quality ontologies, containing incorrectly represented information or controversial concepts modelled from a single viewpoint, can lead to invalid application outputs and biased systems. Thus, we focus on the curation of ontologies as a crucial factor for ensuring trust in the enabled AI systems. While some ontology quality aspects can be automatically evaluated, others require a human-in-the-loop evaluation. Yet, despite the importance of the field, several ontology quality aspects have not yet been addressed and there is a lack of guidelines for optimal design of human computation tasks to perform such evaluations. In this article, we advance the state-of-the-art by making two novel contributions. First, we propose a human computation (HC)–based approach for the verification of ontology restrictions —an ontology evaluation aspect that has not yet been addressed with HC techniques. Second, by performing two controlled experiments with a junior expert crowd, we empirically derive task design guidelines for achieving high-quality evaluation results related to (i) the formalism for representing ontology axioms and (ii) crowd qualification testing . We find that the representation format of the ontology does not significantly influence the campaign results. Nevertheless, contributors expressed a preference in working with a graphical ontology representation. Additionally, we show that an objective qualification test is better fitted at assessing contributors’ prior knowledge rather than a subjective self-assessment and that prior modelling knowledge of the contributors had a positive effect on their judgements. We make all artefacts designed and used in the experimental campaign publicly available.

Optimizing dual-energy CT technique for iodine-based contrast-to-noise ratio, a theoretical study.

Dual-energy CT (DECT) systems provide valuable material-specific information by simultaneously acquiring two spectral measurements, resulting in superior image quality and contrast-to-noise ratio (CNR) while reducing radiation exposure and contrast agent usage. The selection of DECT scan parameters, including x-ray tube settings and fluence, is critical for the stability of the reconstruction process and hence the overall imagequality. The goal of this study is to propose a systematic theoretical method for determining the optimal DECT parameters for minimal noise and maximum CNR in virtual monochromatic images (VMIs) for fixed subject size and total radiationdose. The noise propagation in the process of projection based material estimation from DECT measurements is analyzed. The main components of the study are the mean pixel variances for the sinogram and monochromatic image and the CNR, which were shown to depend on the Jacobian matrix of the sinograms-to-DECT measurementsmap. Analytic estimates for the mean sinogram and monochromatic image pixel variances and the CNR as functions of tube potentials, fluence, and VMI energy are derived, and then used in a virtual phantom experiment as an objective function for optimizing the tube settings and VMI energy to minimize the image noise and maximize theCNR. It was shown that DECT measurements corresponding to kV settings that maximize the square of Jacobian determinant values over a domain of interest lead to improved stability of basis material reconstructions. Instances of non-uniqueness in DECT were addressed, focusing on scenarios where the Jacobian determinant becomes zero within the domain of interest despite significant spectral separation. The presence of non-uniqueness can lead to singular solutions during the inversion of sinograms-to-DECT measurements, underscoring the importance of considering uniqueness properties in parameter selection. Additionally, the optimal VMI energy and tube potentials for maximal CNR was determined. When the x-ray beam filter material was fixed at 2 mm of aluminum and the photon fluence for low and high kV scans were considered equal, the tube potential pair of 60/120 kV led to the maximal iodine CNR in the VMI at 53keV. Optimizing DECT scan parameters to maximize the CNR can be done in a systematic way. Also, choosing the parameters that maximize the Jacobian determinant over the set of expected line integrals leads to more stable reconstructions due to the reduced amplification of the measurement noise. Since the values of the Jacobian determinant depend strongly on the imaging task, careful consideration of all of the relevant factors is needed when implementing the proposedframework.

Cascaded Semantic Fractionation for identifying a domain in social media.

Searching social media to find relevant semantic domains often results in large text files, many of which are irrelevant due to cross-domain content resulting from word polysemy, abstractness, and degree centrality. Through an iterative pruning process, Cascaded Semantic Fractionation (CSF) systematically removes these cross-domain links. The social network procedure performs community detection in semantic networks, locates the semantic groups containing the terms of interest, excludes intergroup links, and repeats community detection on the pruned intragroup network until the domain of interest is clarified. To illustrate CSF, we analyzed public Facebook posts, using the CrowdTangle app for historical data search, from February 3, 2020, to March 13, 2021, about the possible Wuhan lab leak of COVID-19 over a daily interval. The initial search using keywords located six multi-day bursts of posts of more than 500 per day among 95 K posts. These posts were network analyzed to find the domain of interest using the iterative community detection and pruning process. CSF can be applied to capture the evolutions in semantic domains over time. At the outset, the lab leak theory was presented in conspiracy theory terms. Over time, the conspiratorial elements washed out in favor of an accidental release as the issue moved from social to mainstream media and official government views. CSF identified the relevant social media semantic domain and tracked its changes.