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Cross-validation of meshless Navier–Stokes solvers in porous media flows
Abstract In this paper, two mesh-free CFD solvers for pore-scale fluid flow through porous media are considered, namely the Lattice Boltzmann Method with the two relaxation time collision term and the direct Navier–Stokes solver under the artificial compressibility limit. The porous media is built with a regular arrangement of spherical grains with variable radii, which allows control of the porosity. Both solvers use the same h-refined meshless spatial discretization to adequately capture the underlying geometry and the same Radial Basis Function (RBF) method to approximate the involved fields and partial differential operators. First, the results are compared with the data from the literature in terms of drag coefficient and permeability at different porosities achieving excellent agreement with the reported results. Next, the simulations are extended beyond the porosity range reported in the literature using proposed h-refined CFD solvers. The results are supported by convergence and timing analyses and discussions on meshless parameters such as stencil size and refinement settings.
Enhanced intrusion detection in cybersecurity through dimensionality reduction and explainable artificial intelligence
Constrained coding for error mitigation in nanopore-based DNA data storage
Abstract DNA has been proposed as an alternative to magnetic and solid-state devices for storing digital data. In DNA data storage, writing data is performed through DNA synthesis, and reading is done via sequencing. Nanopore devices for sequencing DNA, like those produced by Oxford Nanopore Technologies, allow long reads and real-time sequencing but with lower accuracy compared to other sequencers, such as Illumina. To improve the reliability of data storage in DNA, we aim to combat the high error rate of nanopore sequencing using constrained coding. Certain aspects of the physical process underlying nanopore sequencing mean that some sequences are more prone to sequencing errors than others. We leverage this observation to design constrained codes using constrained de Bruijn graphs, along with a state-splitting encoder and a Viterbi-based decoder. We find that the overall performance of our novel coding system substantially improves upon the state-of-the-art DNN-based methods, reducing sequence-level errors by up to 6 times. We also visually demonstrate the performance of our approach through the simulated recovery of an image encoded and decoded using our method.
FairEduNet: a novel adversarial network for fairer educational dropout prediction
User cum expert judgement model for accessibility using fuzzy approach
Lactoferrin based copper nanoparticles: a promising anti-cancer strategy against malignant melanoma
Association of KCNJ11 rs5219 polymorphism with risk of type 2 diabetes and its cardiovascular and renal complications in Noakhali, Bangladesh
Evaluation of risk factors for falls among elderly patients attending a teaching hospital in southern Nigeria: a cross-sectional study
Parameter identification for PDEs using sparse interior data and a recurrent neural network
Modeling of reduction kinetics of Cr2O7−2 in FeSO4 solution via artificial intelligence methods
Palladium nanoparticles stabilized on the BPA-functionalized Fe3O4 as the recoverable catalysts for synthesis of aromatic sulfide by C–S coupling reactions
Efficacy of probiotic co-supplementation with omega-3 PUFAs on pancreatic beta-cell function in type 2 diabetes
Quantifying undetected tuberculosis in Ethiopia using a novel geospatial modelling approach
Abstract Tuberculosis (TB) is the leading infectious cause of death globally, with approximately three million cases remaining undetected, thereby contributing to community transmission. Understanding the spatial distribution of undetected TB in high-burden settings is critical for designing and implementing geographically targeted interventions for early detection and control. This study presents the first estimates of numbers of undetected TB cases in Ethiopia at national and local levels using novel geospatial method. We employed a Bayesian geostatistical modelling framework, incorporating national TB prevalence survey and TB notification data together with climatic and environmental variables, to estimate the number of undetected TB cases at district and national levels. Spatial clustering of undetected TB cases was assessed using Moran’s Index statistic and Local Indicator of Spatial Autocorrelation (LISA). A Bayesian Poisson regression model with conditional autoregressive (CAR) prior structure was developed to identify drivers of the clustering. We estimated a total of 51,041 undetected TB cases (95% CI: 50,599, 51,486) in Ethiopia, with the majority of these cases predicted in the Oromia region (20,440), Amhara region (9614), and South Ethiopia region (6061). Spatial clustering of undetected TB cases was observed in districts near the international borders, including the Ethiopia-Somalia and Ethiopia-Kenya border regions, as well as in several districts of Southern Ethiopia. The number of undetected TB cases was negatively associated with the proportion of the population with good mass media exposure (Incidence rate ratio (IRR): 0.67 95% CI: 0.56, 0.80) and the proportion of the population with high wealth index (IRR: 0.73, 95% CI: 0.60, 0.90). Our findings revealed a high burden of undetected TB in Ethiopia, with spatial clustering in border regions and areas with limited healthcare access. Targeted TB screening interventions to communities with low socioeconomic status along with improving mass media exposure in these regions, could significantly reduce the burden of undetected TB in Ethiopia.