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Yara Sarkis,1 Nasir Saleem,2 Raj Vuppalanchi,2 Mark Gromski2 1Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA; 2Division of Gastroenterology and Hepatology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USACorrespondence: Mark Gromski, Gastroenterology and Hepatology Department, Indiana University Hospital, 550 N. University Blvd, Suite 1634, Indianapolis, IN, 46202, USA, Tel +317-944-0925, Fax +317-968-1265, Email mgromski@iu.eduAbstract: The complete impact of COVID-19 infection continues to develop since the onset of the COVID-19 pandemic. COVID-19 cholangiopathy has been recently described in a subset of patients who recovered from severe COVID-19 infection. The most common phenotype of patients suffering from COVID-19 cholangiopathy had severe infection requiring a stay in the intensive care unit, mechanical ventilation and vasopressor medications. Patients with COVID-cholangiopathy present with severe and prolonged cholestatic liver injury. In cases where biliary cast formation is identified, we defined the entity as “COVID-19 cast-forming cholangiopathy”. This subset of COVID-19 cholangiopathy is not well understood and there are no standardized diagnosis or management to this date. The reported clinical outcomes are variable, from resolution of symptoms and liver test abnormalities to liver transplant and death. In this commentary, we discuss the proposed pathophysiology, diagnosis, management, and prognosis of this disease.Keywords: COVID-19 infection, cast-forming cholangiopathy, COVID-19 cholangiopathy, long COVID syndrome

Abstract Artificial Neural Networks are incredibly efficient at handling complicated and nonlinear mathematical problems, making them very useful for tackling these challenges. Artificial neural networks offer a special computational architecture that is extremely valuable in disciplines like biotechnology, biological computing, and computational fluid dynamics. The present work investigates the applicability of back-propagation artificial neural networks in conjunction with the Levenberg-Marquardt algorithm for evaluating heat transmission in hybrid nanofluids. This work focuses on the computational analysis of a MgO + GO/EG hybrid nanofluid’s steady mixed convection flow over an exponentially stretched sheet, considering multiple slip boundary conditions, thermal conductivity, heat generation, and thermal radiation. A nonlinear system of ordinary differential equations is produced from the basic associated partial differential system by performing the proper exponential similarities modifications. For generating benchmark datasets, the resulting ordinary differential equations are processed employing the bvp4c method. Considering benchmark datasets set aside for training (70%), testing (15%), and validation (15%), the Levenberg-Marquardt algorithm, which employs back-propagation in artificial neural networks, is implemented. The accuracy of the suggested strategy for handling nonlinear problems is verified utilizing mean squared error, error histograms, and regression analysis, which are all used to evaluate the methodology. Outstanding agreement is seen when ANN outputs are compared to numerical results. The flow properties, including temperature, velocity, and concentration profiles, are shown graphically and numerically. For practical purposes, it is therefore essential to analyze the flow and heat transfer in hybrid nanofluids over exponentially extending and shrinking surfaces under mixed convection and heat source scenarios. Hybrid nanofluid problems have a wide range of practical and industrial applications, such as medication delivery, manufacturing, microelectronics, nuclear plant cooling, and marine engineering.

This article presents a non-linear mathematical model that captures the dynamics of drinking prevalence within a population. The model is analyzed under an optimal control framework, dividing the total population into four compartments: susceptible, heavy drinker, drinker in treatment, and recovered classes. The model’s validity is affirmed through considerations of positivity, boundedness, reproduction number, stability, and sensitivity analysis. Stability theory is employed to explore both local and global stabilities. Sensitivity analysis identifies parameters with a significant impact on the reproduction number (R0{R}_{0}), with maximum sensitivity observed in parameters related to drinking transmission and transitions from heavy drinking to treatment stages. These parameters exhibit sensitivity indices of (0.538,1)\left(0.538,1), indicating that a 10% increase in these parameters would result in a (5.38,1)\left(5.38,1) increase in the threshold quantity. The study introduces an optimal control strategy that involves awareness campaigns and treatment as control variables. These controls aim to minimize the number of heavy drinkers while maximizing the number of recovered individuals. Pontryagin’s maximum principle is used to solve optimal control problems. Additionally, the research explores various parametric settings for each compartment, enriching the study environment. The effectiveness of the proposed control scheme is evaluated through rigorous numerical simulations, highlighting its competitive edge. The results, validated using MATLAB simulations, are detailed throughout the article.

Copyright of Journal of Education & Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Education & Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Artificial neural networks have revolutionized machine learning by providing exceptional capabilities for modeling complicated mechanisms and solving various challenges. Backpropagation is an important training technique in the field of artificial neural networks. However, this technique must be optimized when working with complicated fluid dynamics. This study analyzes the three-dimensional radiative flow of a tangent hyperbolic fluid driven by the Cattaneo-Christov flux system across a porous stretching sheet using ANN backpropagation enhanced by Bayesian Regularization approach. Heat and mass transfer analysis includes thermal radiation, chemical reactions and Cattaneo-Christov flux model. Porosity, radiation, chemical reaction rate, and ion slip effect are among the important physical characteristics that are modified to see how they affect fluid dynamics. Using MATLAB's BVP4C solver, the velocity, temperature, and concentration profiles that result from these model equations provide the training dataset for ANNs. The dataset is divided into 80 % for training, 10 % for testing, and 10 % for validation. Performance plots, regression graphs, and error histograms are used to analyze the performance of the LMT-based ANN and demonstrate its high accuracy and efficiency. With an R2 value of 1, the ANN produced a mean squared error of around 10⁻11. Fluid mobility drops as the magnetic parameter grows, while the thermal profile exhibits an increasing trend. Similarly, decreasing fluid velocity is the outcome of raising the porosity parameter. The study's conclusions have great potential for use in sectors that need sophisticated cooling and heating equipment.

The present work aimed to highlight an efficient, readily accessible, and cost-effective adsorbent derived from Dalbergia sissoo (DS) leaf powder for removing the environmentally hazardous dye “alizarin red S” (ARS) from hydrous medium. A variant of the adsorbent is activated via sulfuric acid and composited with magnetic iron oxide nanoparticles (DSMNC). Both adsorbents are thoroughly characterized using techniques such as Fourier transform infrared spectroscopy, point of zero charge, energy-dispersive X-ray spectroscopy, and scanning electron microscopy, which show that they have a porous structure rich in active sites. Different adsorption conditions are optimized with the maximum removal efficiency of 76.63% for DS and 97.89% for DSMNC. The study was highlighted via the application of various adsorption isotherms, including Freundlich, Langmuir, Temkin, and Dubinin–Radushkevich, to adsorption data. Pseudo-first-order, pseudo-second-order, and intra-particle diffusion models were utilized to investigate the kinetics and mechanism of adsorption. The Freundlich model and pseudo-second-order kinetics exhibited the best fit, suggesting a combination of physical interactions, as confirmed by the D–R and Temkin models. The dominant adsorbate–adsorbent interactive interactions responsible for ARS removal were hydrogen bonding, dispersion forces, and noncovalent aromatic ring adsorbent pi-interactions. Thermodynamic parameters extracted from adsorption data indicated that the removal of the mutagenic dye “ARS” was exothermic and spontaneous on both DS and DSMNC, with DSMNC exhibiting higher removal efficiency.

In modern Internet-based communication, especially mobile systems, a mobile node (MN) will commonly have more than one possibility for Internet Protocol (IP) connectivity. For example, an MN such as a smartphone may be associated with an IEEE 802.11 network at a site while also connected to a cellular base station for 5G. In such a scenario, the smartphone might only be able to utilise the IEEE 802.11 network, not making use of the cellular connectivity simultaneously. Currently, IP does not allow applications and devices to easily utilise multiple IP connectivity opportunities—multihoming for the MN—without implementing special mechanisms to manage them. We demonstrate how the use of the Identifier Locator Network Protocol (ILNP), realised as an extension to IPv6, can enable mobility with multihoming using a duality mechanism that treats mobility and multihoming as the same logical concept. We present a network layer solution that does not require any modification to transport protocols, can be implemented using existing application programming interfaces (APIs), and can work for any application. We have evaluated our approach using an implementation in Linux and a testbed. The testbed consisted of commercial equipment to demonstrate that our approach can be used over existing network infrastructure requiring only normal unicast routing for IPv6.

The purpose of this study is to evaluate and compare how well different routing protocols perform in terms of load sharing, link failover, and overall network performance. Wireshark was used for packet-level analysis, VMWare was used for virtualization, GNS3 was used for network simulation, and Iperf3 was used to measure network performance parameters. Convergence time, packet losses, network jitter, and network delay are the parameters that were selected for assessment. To examine the behaviors of Open Shortest Path First (OSPF) and Enhanced Interior Gateway Routing Protocol (EIGRP) routing protocols in a variety of network settings, a simulated network environment incorporating both protocols along with Border Gateway Protocol (BGP) is created for the research. The setup for the experiment entails simulating different network conditions, such as fluctuating traffic loads and connection failures, to track how the protocols function in dynamic situations. The efficiency metrics for OSPF and EIGRP with BGP are measured and evaluated using the data generated by Wireshark and Iperf3. The results of this research show that EIGRP has a better failover convergence time and packet loss percentage as compared to the OSPF.

Abstract This study explores the impacts of heat transportation on hybrid (Ag + MgO) nanofluid flow in a porous cavity using artificial neural networks (Bayesian regularization approach (BRT-ANN) neural networks technique). The cavity considered in this analysis is a semicircular shape with a heated and a cooled wall. The dynamics of flow and energy transmission in the cavity are influenced by various features such as the effect of magnetize field, porosity and volume fraction of nanoparticles. To explore the outcomes of these features on hybrid nanofluid thermal and flow transport, a BRT-ANN model is developed. The ANN model is trained using a dataset generated through numerical scheme. The trained ANN model is then used to predict the heat and flow transport characteristics for various input parameters. The accuracy of the ANN simulation is confirmed through comparison of the predicted results with the results obtained through numerical simulations. By maintaining the corrugated wall uniformly heated, we inspected the levels of isotherms, streamlines and heat transfer distribution. A graphical illustration highlights the characteristics of the Hartmann and Rayleigh numbers, permeability component in porous material, drag force and rate of energy transport. According to the percentage analysis, nanofluids (Ag + MgO/H2O) are prominent to enhance the thermal distribution of traditional fluids. The study demonstrates the potential of ANNs in predicting the impacts of various factors on hybrid nanofluid flow and heat transport, which can be useful in designing and optimizing heat transfer systems.

This article analyzes the JK iteration process with the class of mappings that are essentially endowed with a condition called condition (E). The convergence of the iteration toward a fixed point of a specific mapping satisfying the condition (E) is obtained under some possible mild assumptions. It is worth mentioning that the iteration process JK converges better toward a fixed point compared to some prominent iteration processes in the literature. This fact is confirmed by a numerical example. Furthermore, it has been shown that the iterative scheme JK is stable in the setting of generalized contraction. The data dependence result is also established. Our results are new in the iteration theory and extend some recently announced results of the literature.

Abstract The flow at a time-independent separable stagnation point on a Riga plate under thermal radiation and electro-magnetohydrodynamic settings is examined in this research. Two distinct base fluids-H2O and C2H6O2 and TiO2 nanostructures develop the nanocomposites. The flow problem incorporates the equations of motion and energy along with a unique model for viscosity and thermal conductivity. Similarity components are then used to reduce these model problem calculations. The Runge Kutta (RK-4) function yields the simulation result, which is displayed in graphical and tabular form. For both involved base fluid theories, the nanofluids flow and thermal profiles relating to the relevant aspects are computed and analyzed. According to the findings of this research, the C2H6O2 model heat exchange rate is significantly higher than the H2O model. As the volume percentage of nanoparticles rises, the velocity field degrades while the temperature distribution improves. Moreover, for greater acceleration parameters, TiO2/ C2H6O2has the highest thermal coefficient whereas TiO2/ H2O has the highest skin friction coefficient. The key observation is that C2H6O2 base nanofluid has a little higher performance than H2O nanofluid.

Current work explores the intricacies of magnetohydrodynamic and mix convectional boundary-layer flow concerning couple stress Casson nanofluid (CSCNF) dynamics via a 3D stretchable surface. The addition of active and passive control mechanisms for nanoscales creates an innovative dimension to the exploration. Remarkably, the analysis incorporates the influence of non-Fourier and non-Fickian heat and mass flux, alongside the effects of thermophoresis and Brownian diffusion, to systematically investigate the heat and mass transportation phenomena. The governing equations (PDEs) describing the MHD-3DCSCNF model are converted into a set of ordinary differential equations to facilitate the ANN analysis. Employing the bvp4c technique, a dataset is systematically generated for the back propagation artificial neural network with Levenberg–Marquardt Algorithm (BANN-LMA) through four distinct scenarios. Via accurate testing, validation, and training, the BANN-LMA produces estimated results for the MHD-3DCSCNF problem. The performance validation of BANN-LMA is executed through several metrics, involving the mean squared error, error histogram and regression analysis. The training process, characterized by minimizing the MSE through a gradient descent methodology with optimized weights, exhibits a compelling correlation R = 1, between the target and network output. Furthermore, the consistent convergence observed highlights the method robustness and reliability.

A mathematical model is presented to analyze the double diffusive transport of hybrid nanofluids in microchannel. The hybrid nanofluids flow is driven by the cilia beating and electroosmosis in the presence of radiation effects and activation energy. Cu–CuO/blood hybrid nanofluids are considered for this analysis. Phase difference in the beatings of mimetic cilia arrays emerge symmetry breaking pump walls to control the fluid stream. Analytical solutions for the governing equations are derived under the assumptions of Debye–Hückel linearization, lubrication, and Rosseland approximation. Dimensional analysis has also been considered for applying the suitable approximations. Entropy analysis is also performed to examine the heat transfer irreversibility and Bejan number. Moreover, trapping phenomena are discussed based on the contour plots of the stream function. From the results, it is noted that an escalation in fluid velocity occurs with the rise in slippage effects near the wall surface. Entropy inside the pump can be eased with the provision of activation energy input or by the consideration of the radiated fluid in the presence of electroosmosis. The results of the present study can be applicable to develop the emerging thermofluidic systems which can further be utilized for the heat and mass transfer at micro level.

Abstract This study looks at the natural convections of Cu + Al2O3/H2O nanofluid into a permeable chamber. The magnetic field is also executed on the flow field and the analysis has been approached numerically by the control volume method. The study of hybrid nanofluid heat in terms of the transfer flux was supplemented with a wide range of parameters of hybrid nanofluid fractions, Rayleigh numbers Hartmann numbers and porosity factor. It's also determined that the flow and thermal distribution are heavily affected by the concentration of the nanoparticles. The concentration of nanoparticles increases the transport of convective energy inside the enclosure. The primary findings demonstrate that a rise in both the Rayleigh number and Darcy number leads to an improvement in convective heat transfer within the enclosure. However, the porosity has a negligible effect. Additionally, the rotation in a clockwise direction has a beneficial impact on the dispersion of heat transfer throughout the cavity. Furthermore, it is concluded that hybrid nanofluids are more reliable than conventional fluids in improving thermal properties.

This article contain the concepts of fuzzy (α - η) and fuzzy (β - ψ)-generalized proximal contractive mappings in the setup of b-fuzzy metric spaces. We proved the existence of coincidence and best proximity points of fuzzy (α - η)- and fuzzy (β - ψ)-generalized proximal contractions on b-fuzzy metric spaces. Some nontrivial examples are provided to elaborate the fact that the obtained results are potential generalizations of comparable existing results. Our results unify and complement various known results in the existing literature. [ABSTRACT FROM AUTHOR]