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Optical coherence tomography (OCT) helps identify coronary artery disease of different etiologies. Vasospasm from OCT catheter is a rarely reported complication that is more commonly seen in the right coronary artery. We report a case of OCT-catheter induced vasospasm of the left anterior descending artery that resolved with administration of nitroglycerine. Interventionalists need to weary of the occurrence of catheter-related coronary artery spasm to avoid stenting when not necessary.

Objectives: Colistin is known as the last resort antibiotic to treat the infections caused by multidrug resistant foodborne pathogens. The emergence and widespread dissemination of plasmid-mediated colistin resistance gene mcr-1 in the Escherichia coli (E. coli) incurs potential threat to public health. Here, we investigated the epidemiology, transmission dynamics, and genetic characterization of mcr-1 harbouring E. coli isolates from poultry originated in Hebei Province, China. Methods: A total of 297 faecal samples were collected from the two large poultry farms in Hebei Province, China. The samples were processed for E. coli identification by matrix-assisted laser desorption ionization-time of flight mass spectrometry and 16S rDNA sequencing. Then, the mcr-1 gene harbouring E. coli strains were identified by polymerase chain reaction and subjected to antimicrobial susceptibility testing by broth microdilution assay. The genomic characterization of the isolates was done by whole genome sequencing using the various bioinformatics tools, and multi-locus sequence typing was done by sequence analysis of the seven housekeeping genes. The conjugation experiment was done to check the transferability of mcr-1 along with the plasmid stability testing. Results: A total of six mcr-1 E. coli isolates with minimum inhibitory concentration of 4 μg/mL were identified from 297 samples (2.02%). The mcr-1 harbouring E. coli were identified as multidrug resistant and belonged to ST101 (n = 4) and ST410 (n = 2). The genetic environment of mcr-1 presented its position on IncHI2 plasmid in 4 isolates and p0111 in 2 isolates, which is a rarely reported plasmid type for mcr-1. Moreover, both type of plasmids was transferable to recipient J53, and mcr-1 was flanked by 3 mobile elements ISApl1, Tn3, and IS26 forming a novel backbone Tn3–IS26-mcr-1– pap2-ISApl1 on the p0111 plasmid. The phylogenetic analysis shared a common lineage with mcr-1 harbouring isolates from the environment, humans, and animals, which indicate its horizontal spread among the diverse sources, species, and hosts. Conclusion: This study recommends the one health approach for future surveillance across multiple sources and bacterial species to adopt relevant measures and reduce global resistance crises.

The current investigative exploration exemplifies the conceptualization of a novel design intelligent computing paradigm based on artificial neural networks (ANNs) by utilizing radial basis function (RBF) to analyze magnetohydrodynamic (MHD) Williamson nanofluid two-dimensional flow along a stretchable sheet under the effect of chemical reaction as well as thermal radiation in a porous medium. This newly designed technique is an amalgam of a well-known reliable global solver named genetic algorithms (GAs) and a swift convergence generated local solver named sequential quadratic programming (SQP) used in ANNs by taking RBF as a kernel function i.e. ANNs-RBF-GASQP solver. The PDEs demonstrating the current nanofluid problem flow are transformed into the system of non-linear ODEs through a relevant similarity transformation and subsequently solved using ANNs-RBF-GASQP solver to investigate thermohydraulic properties by manipulating the values of various system parameters present in the ODEs. Moreover, the simulation results show that increasing the heat source parameter leads to a significant decrease in temperature. Additionally, an increase in the porosity parameter causes a decrease in the velocity of nanofluid, as a higher value of porosity increases fluid permeability and greater resistance to flow. The efficacy of the suggested solver is scrutinized through various statistical and convergence analyses.

Cardiac strangulation from leads of an epicardial pacemaker is a rarely reported life-threatening complication. We report the case of a 72-year-old female with history of supraventricular tachycardia status post sino-atrial node ablation with epicardial pacemaker placement who presented to the outpatient setting with shortness of breath. Heart catheterization was performed which showed elevated right and left heart pressures. Compression involving the mid left anterior descending artery and left circumflex artery was noted from an epicardial lead on coronary angiography consistent with cardiac strangulation. The patient was then referred to cardiothoracic surgery for epicardial pacemaker lead removal. The literature has only reported one other survival case of cardiac strangulation in an adult.

The surgical site infections cost 0.5 % of the annual budget of the hospitals. Therefore, present research aimed to develop and evaluate antibacterial Neem/chitosan-coated silk braided sutures to reduce the risk of hospital-acquired surgical site infections. The silk braided sutures were coated by dip-coating technique by using 5, 10, 15 and 20 % concentrations of ethanolic Neem extract and chitosan. Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and tensile strength measurements were used to examine the surface morphology, chemical composition, and tensile strength of the coated sutures. At a 20 % concentration, the sutures showed strong antibacterial action against Escherichia coli and Staphylococcus aureus, with inhibition zones of 15 and 19 mm, respectively. Additionally, the sutures demonstrated low cytotoxicity in the MTT assay, good radical scavenging activity, and hemolytic activity of less than 2 % at all concentrations. The sutures for the in vivo investigation were coated at a concentration of 20 % since this concentration showed the best antibacterial action. The rats were incised on both sides of their spine, with the left side stitched with uncoated sutures, and the right side stitched with coated sutures. The wound was inoculated with E. coli and S. aureus and examined for 7 days. The results indicated complete healing of the wound at the site where the coated sutures were applied. On other hand, wound with uncoated suture was still not completely healed and revealed signs of infection. The in vivo study revealed the efficacy of Neem/chitosan-coated sutures in wound healing and infection control.

This paper introduces a precise method for solving time-fractal parabolic equations specifically designed to represent complex physical phenomena using fractal calculus. The suggested technique attains third-order accuracy by utilizing the fractal Taylor series and implements a concise scheme for spatial discretization. Stability and convergence analyses are offered for scalar and system fractal parabolic equations. The mathematical model analyzes fluid flow over both flat and oscillatory surfaces, considering the effects of heat and mass transfer. This involves transforming the governing equations into dimensionless partial differential equations, which are then solved using the suggested method. From the calculated results, it can be verified that the proposed scheme produces less error than the existing two schemes. This study enhances computational techniques for fractal calculus and introduces novel opportunities for comprehending non-Newtonian fluids in diverse physical contexts.

This work reports the observation of sunlight diffraction through empty spaces in between twigs and leaves in tree crowns. The diffraction patterns are dominated by red wavelength on front followed by the trail of rest of spectral bands of white light rainbow bursts. Tree crowns act as obstacles and apertures causing diffraction/ interference of solar rays. Separation between leaves and twigs (d) is a few inches and distance between crown and camera (D) is in range of a few meters. Empty spaces in tree crown are much longer than red to violet wavelengths in sunlight therefore scattering, dispersion, reflection, diffraction and interference effects cannot be ruled out. Tree crown deflects solar rays into narrow angles all around to enable interference inside camera lens to create grating structure diffracting light into regular patterns. Diffraction pattern look like red petunia flower having 3 × 3 rose petals. Motorola mobile phone camera was used to record the sunlight diffraction patterns. Mobile camera consists of small focal length (≈5 mm) lens, a fixed opening aperture and an image sensor. Lens consists of few plastic or glass elements and sensor consists of charge coupled device (CCD) or CMOS technology. These diffraction/ interference patterns have potential to increase efficiency of Next Gen solar cells. This paper describes possible theoretical background of light diffraction and multiple ray interference through tree twigs and leaves.

Enhancement in the visible light absorption and efficient interfacial charge transfer is crucial for optimizing photocatalytic efficiency in the degradation of pollutants such as methyl orange (MO) and formaldehyde. This study focuses on the properties of a TiO2/C/g-C3N4 hybrid efficient photocatalyst, which is developed using an air calcination method to deposit graphitic nitride (g-C3N4) onto a carbon-modified TiO2 surface. The characterization techniques, including high-resolution transmission electron Microscopy (HRTEM), X-ray diffraction (XRD), Raman spectroscopy, thermogravimetric analysis (TGA), Fourier-transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS), were used to provide a comprehensive understanding of the material’s structural, morphological, thermal, and chemical properties. This hybrid catalyst is specifically engineered for the efficient decomposition of methyl orange (MO) and formaldehyde, demonstrating a significant increase in photocatalytic activity. The TiO2/C/g-C3N4 photocatalyst also exhibits an enhanced specific surface area of 181.2 m2/g, which facilitates increased physical adsorption and photo-catalytic active sites. Experimental results confirm that this catalyst effectively adsorbs MO physically even in the dark without degradation. Combining physical and photo-catalytic functions, this catalyst degrades 94 % of MO within 180 min with the initial concentration 0.2 mol/L of MO, and achieves almost 100 % decolorization of MO under visible light irradiation. Notably, the catalyst retains its high activity after 4 cycles of MO degradation, underscoring its durability and consistent performance. Additionally, the hybrid catalyst features a staggered type-II energy level configuration, which effectively enhances charge separation and boosts photocatalytic efficacy. The incorporation of an ultrafine carbon layer further augments electron mobility towards the surface, crucial for effective catalytic reactions. This study paves the way for future development of highly efficient photocatalytic materials for environmental purification.

This study aims to develop an efficient predictive model for Cattaneo-Christov heat and mass transformation of dissipative Williamson fluid with the effects of double stratification (CCHMT-DWF-DS) using the Levenberg-Marquardt Backpropagation (LMA-BP) algorithm. The under-consideration Williamson fluid flow is magneto-hydro-dynamic, incompressible and two-dimensional through a stretching sheet. The mathematical model of nonlinear partial differential equations for physical phenomena is transformed into ordinary differential equations by means of renowned similarity transformations. The solutions of physical problem are computed by bvp4c technique through MATLAB. The LMA-BP is employed to train a backward neural network capable of accurately predicting velocity, temperature, and concentration profiles under various physical conditions such as changes in the Hartmann number Ha, Prandtl number Pr, Schmidt number Sc, Williamson parameter λ, the relaxation time of temperature γ1, the relaxation time of concentration γ2, temperature stratification δ1, and concentration stratification δ2 for generating a variety of graphical outcomes and statistics. This research is significant for its innovative use of the LMA-BP in analyzing the complex dynamics of non-Newtonian fluids specifically the Williamson fluid, alongside the Cattaneo-Christov heat and mass flux model. The obtaining graphs have been discussed in detail. The thermal and solutal relaxation factors reduce heat and mass flow while fluid motion is delayed by the time-dependent parameter λ and further reduced by the Hartman number. The Cattaneo-Christov heat flux model enhances simulation accuracy by integrating temporal delays in heat transfer, proving beneficial for sophisticated industrial and scientific endeavors related to non-Newtonian fluids. This analysis offers a powerful predictive tool for applications in thermal management, industrial cooling systems, and biomedical fluid dynamics, advancing machine learning in fluid mechanics.

Herein, five Schiff’s base analogs (designated as AK1 to AK5) were synthesized and screened for their analgesic and anti-inflammatory efficacies. All compounds exhibited analgesic activity at selected doses from 45.31 % to 75.75 %, while the standard drug diclofenac sodium produced result as 86.33 %. Compounds AK1, AK3, AK4 and AK5 were found significantly P

This article presents the notion about Slow Invariant Manifold (SIM) and their fundamental role in model reduction techniques (MRTs) for challenges encountered in mechanical engineering within dissipative systems of chemical kinetics. Focusing on the reaction routes of complex mechanisms, we construct and compare primary approximations of the SIM through MRTs, including the Spectral Quasi Equilibrium Manifold (SQEM) and Intrinsic Low Dimensional Manifold (ILDM). These methods effectively transform high-dimensional complex problems into lower dimensions, solving them without compromising crucial information about the complex systems modified for homogeneous reactive systems. Employing the sensitivity analysis by using the MATLAB's toolbox, we present the numerical findings in a tabular format obtained through MRTs. This study provides the understanding about the accessible exploration of numerical solutions, improving insights of the complex variation within the system.

It is imperative to investigate the proposed computational scheme for the resolution of two-dimensional partial differential equations that result from non-Newtonian nanofluid flow over flat and oscillatory sheets, taking into account the effects of the magnetic field and chemical reactions, as it is designed to address stochasticity, unstable flow conditions, and Maxwellian behaviour. Consequently, it offers valuable insights into the dynamics of nanofluids. A stochastic computational scheme is suggested to address the two-dimensional partial differential equations (PDEs) caused by non-Newtonian nanofluid flow over flat and oscillatory sheets in the presence of a magnetic field and chemical reaction effects. The Taylor series analysis is employed to construct a scheme. The two-stage approach can be employed to discretize the time derivative in the differential equations. The stability study results and the consistency by mean square measure are also included. The continuity equation in the given partial differential equations (PDEs) is discretized using the first-order finite difference approach. The remaining equations, including the energy equation, the nanoparticle volume fraction, and the Navier-Stokes equation, are discretized using the second-order difference in space scheme that is proposed. Three separate figures show how the various parameters affect the nanoparticle volume fractions, velocity, and temperature. Through a focus on a specific computational technique designed to handle the challenges given by stochasticity, unstable flow conditions, and the Maxwellian behaviour shown by these nanofluids, this study introduction serves as a portal into the involved domain of nanofluid dynamics.

For the growth of sustainable energy, increasing the efficiency of photovoltaic/thermal (PV/T) systems is still essential. Particularly ternary hybrid nanofluids have shown promise as a means of improving the systems' electrical and thermal performance. This study delves deeply into the exploration of a three-dimensional annulus-type Photovoltaic Thermal (PV/T) system, centering on its cooling function through the utilization of ternary hybrid nanofluids featuring diverse nanomaterial shapes. The system structure involves a circular cylinder enveloped within glass, silicon, and copper layers, forming the core of the PV panel. The investigation employs COMSOL Multiphysics 6.0 with boundary conditions like radiative heat flux on the upper domain. Nanofluids with varied shapes (spherical, brick-like, cylindrical, platelet, and blade-shaped) comprising copper, aluminum oxide, and micro-wall carbon Nanotubes suspended in water are scrutinized as the base fluid. Using COMSOL Multiphysics, our simulation employs a conjugate heat transfer interface to solve the intricate three-dimensional Navier-Stokes equations and energy equations, addressing the combined convection challenges in the system. Analyzing parameters such as Reynolds number (100–1500), aspect ratios (0.1, 0.15, 0.2), shape factors (3–8.9), and total volume fractions of nanomaterials (1%–10 %), we noted a significant decline (21–28 %) in the average Nusselt number as nanoparticle concentration increased. This trend highlights strengthened convection processes, notably observable with increased flow pipe diameter or aspect ratio. This decrease suggests that the system's convective heat transfer efficiency has decreased. Nonetheless, because the PV cells are better cooled, the decrease in convective heat transfer is accompanied by an increase in the electrical efficiency of the system. The practical implication of this trend is that even though the thermal performance in terms of heat removal may decline, the overall cooling effect still helps to keep the PV cells' temperature down, thereby raising their electrical output. The study revealed enhanced electrical efficiency (up to 9.3834 %) with blade-shaped particles under specific conditions: Re = 1500, 10 % volume fraction, and an aspect ratio of 0.1. Additionally, the investigation suggests a potential for achieving a maximum thermal efficiency of 85.62 %. These findings underline the promising impact of nanomaterial manipulation on both electrical and thermal efficiencies within the PV/T system while the maximum temperature at the outlet reaced to 5.470 C.

Studying compressible inviscid flow over symmetric and supercritical airfoils is important because it is useful to understand and maximize aerodynamic performance in a range of industrial contexts. This study addresses the analysis of symmetric and supercritical airfoils for compressible inviscid flow for both time-dependent and stationary scenarios. The primary goal of these simulations’ is to look at how different velocity variations affected certain airfoil parameters. Air is utilized as the material above airfoils. The best fit for these simulations is the Spalart-Allmaras Turbulence model, which shows good agreement over a wider velocity range with published experimental data from other studies. The COMSOL Multiphysics software is used for all the analysis, for angle of attack 0°, temperature 288K, pressure 1bar and the airfoils that are constructed by importing airfoil data file in COMSOL in the form of excel file. On the comparison of compressible inviscid flow, the supercritical airfoil has better results on the basis of velocity magnitude, pressure, vorticity magnitude, shear rate and rotation rate. The results are converging faster in supercritical airfoil than symmetric airfoil. At a zero-degree angle of attack, velocity increases more on the suction side while decreases on the pressure side (bottom). The final solution error for supercritical airfoil is 1.6E-05, while final residual error for “velocity u pressure p” is 1.2E-05. For symmetric airfoil, final solution error is 0.15, although final residual error is 2.6. Based on the error plot, it decreases rapidly in case of supercritical with a range from 100 to 10-4. Additionally, the wall resolution is constructed for both cases i.e., symmetric airfoil and supercritical airfoil. The main result is that ,with the passage of time, the flow is becoming to stationary state. In the end, model validation via comparison with experimental data highlights the validity and applicability of the results.

This study presents a significant contribution to the field of chemical kinetics by providing a detailed analysis of a multi-step chemical kinetic process using ordinary differential equations (ODEs). The aim is to describe complex chemical processes' kinetics and the steady-state behavior of chemical species. The research employs reduction techniques to simplify the model by separating fast and slow processes based on their time scales, with a focus on a two-step reversible reaction mechanism. Special consideration is given to the phase flow of solution trajectories near equilibrium points, providing a clear depiction of system behavior. MATLAB simulations demonstrate the physical properties of observed data, while sensitivity analysis reveals parameters' impact on species behavior. Overall, this study enhances our understanding of chemical kinetics and offers insights into modeling complex reaction processes, with implications for various applications in chemistry and related fields.

Growing population, burgeoning economic growth and technological advancements have caused a surge in electricity demand worldwide. In recent years, Pakistan, contributing less than 1 % to global Greenhouse Gas (GHG) emissions, has endured severe weather, including the 2022 floods that affected nearly 30 million people. Pakistan is a developing country and signatory of Paris accord has made an international commitment to cut down its GHG emissions. To date, the total electricity generation of Pakistan is about 122,934 GWh, from which 60.9 % (744,862 GWh) is met by thermal sources (coal, oil gas), 23.7 % (29,181 GWh) by hydel, 12.4 % (15,182 GWh) by nuclear and 3 % (3709 GWh) of electricity generation is done through renewable energy sources. The demand for electricity in Pakistan is rising exponentially, and around 60.9 % is fulfilled by hydrocarbons. This study explores a novel approach by assessing the electricity sector's GHG emissions using CO2, CH4, and N2O, expanding beyond the traditional focus on CO2 alone. This work uses a time-varying carbon intensity approach to calculate the GHG emissions and, subsequently, carbon intensity from fossil fuel-driven electrical systems. One notable aspect of this approach is its capability to calculate GHG emissions and carbon intensity with a more frequent temporal resolution, frequently. The peak GHG emissions are observed in January and December at values 650gCO2-eq/kWh and 550gCO2-eq/kWh respectively, causing smog issues in the country. Results show that the carbon intensity in Pakistan is relatively higher during January, February, October, November and December due to unavailability of hydel resources.

This research explores the intricate concept of the Slow Invariant Manifold (SIM) and its pivotal role in developing model reduction techniques (MRTs) for challenges within dissipative systems in chemical kinetics, specifically in mechanical engineering. Focusing on the multi-step mechanism with two intermediates, primary approximations of the SIM are constructed and compared using two prominent MRTs: The Spectral Quasi Equilibrium Manifold (SQEM) and Intrinsic Low Dimensional Manifold (ILDM). At the given rate coefficient, a special computational experiment was performed in which the efficiency of chemical species has been compared. Noteworthy innovation involves evaluating SIM separately for reduced species, departing from the conventional approach of considering every species within the mechanism. The study employs local sensitivity analysis with MATLAB's Sim-Biology toolbox, presenting quantitative findings in a tabular format for a comprehensive MRT comparison. Beyond contributing to a deeper understanding of model reduction techniques in complex chemical kinetics, this research marks the first computational exploration of dissipative systems. The novel perspective of evaluating SIM for reduced species offers nuanced insights, emphasizing the critical role of SIM in effectively addressing challenges in mechanical engineering applications. In summary, this study introduces computational advancements and novel approaches, advancing model reduction techniques and highlighting the significance of SIM in addressing challenges within mechanical engineering contexts.

Artificial neural networks (ANNs) have brought a huge transformation to the machine learning regime by furnishing unparalleled abilities for modeling intricate phenomena and coping with a variety of challenges. In the oneiric field of ANNs, the essential approach for training ANNs is backpropagation. However, optimization is crucial when handling the complex fluid flow phenomenon through backpropagation. The current article implements the Levenberg Marquardt Technique with ANN backpropagation (LMT-BP-ANN) to investigate the radiative flow of a hybrid nanofluid (RHNF) addressing a Cattaneo-Christov flux system past a stretching sheet (SS). Fe2O3 with single-walled carbon nanotubes (SWCNT) and multi-walled carbon nanotubes (MWCNT) hybrid nanofluid is considered. The base fluid is ethylene glycol. Physical parameters of nanofluidic system, including the magnetic parameter M, the solid volume fractions φ1 and φ2, the velocity slip parameter λ, the relaxation time parameter Ω, the Biot number ϒ, and the thermal radiation parameter Gr are evaluated through appropriate variations that effectively accomplish the dynamics of the fluid model. These are utilized to create the dataset for LMT-BP-ANNs using the renowned deterministic Adam's numerical technique in Mathematica software. In MATLAB software, 70 % of data is kept for preparing, 15 % for checking, and 15 % for validating the neural network. The performance plot, regression graphs, and error histograms are constructed to demonstrate the suggested LMT-BP neural network scheme's high efficiency, efficacy, and exactness. When ANN performs, a mean squared error (MSE) of order 10−11 is observed. The observed R squared value is 1. As the magnetic parameter estimate grows, the fluid velocity declines, although the thermal profile shows increasing behavior. As the velocity slip parameter increases, the velocity of the fluid drops. Key findings of this study are anticipated to have a noteworthy bearing on sectors that require local refrigeration and heating by contravention.

Human respiratory syncytial virus (RSV) is an underlying cause of lower respiratory illnesses in children, elderly and immunocompromised adults. RSV contains multiple structural and non-structural proteins with two major glycoproteins that control the initial phase of infection, fusion glycoprotein and the attachment (G) glycoprotein. G protein attaches to the ciliated cells of airways initiating the infection. The hypervariable G protein plays a vital role in evolution of RSV strains. We employed multiple bioinformatics tools on systematically accessed large-scale data to evaluate mutations, evolutionary history, and phylodynamics of RSV. Mutational analysis of central conserved region (CCR) on G protein-coding sequences between 163 and 189 positions revealed frequent mutations at site 178 in human RSV (hRSV) A while arginine to glutamine substitutions at site 180 positions in hRSV B, remained prevalent from 2009 to 2014. Phylogenetic analysis indicates multiple signature mutations within G protein responsible for diversification of clades. The USA and China have highest number of surveillance records, followed by Kenya. Markov Chain Monte Carlo Bayesian skyline plot revealed that RSV A evolved steadily from 1990 to 2000, and rapidly between 2003 and 2005. Evolution of RSV B continued from 2003 to 2022, with a high evolution stage from 2016 to 2020. Throughout evolution, cysteine residues maintained their strict conserved states while CCR has an entropy value of 0.0039(±0.0005). This study concludes the notion that RSV G glycoprotein is continuously evolving while the CCR region of G protein maintains its conserved state providing an opportunity for CCR-specific monoclonal antibodys (mAbs) and inhibitors as potential candidates for immunoprophylaxis.

The fungus-growing termite is considered a distinct ecological niche because it involves a tripartite symbiosis between the termite host, gut microflora, and the in vitro fungus Termitomyces, which has led to the expansion of highly organized and complex societies among termite colonies. Tripartite symbiosis in fungus-growing termites may promote unique microbes with distinctive metabolic pathways that may serve as valuable resources for developing novel antimicrobial therapeutic options. Recent research on complex tripartite symbioses has revealed a plethora of previously unknown natural products that may have ecological roles in signaling, communication, or defense responses. Natural products produced by symbionts may act as crucial intermediaries between termites and their pathogens by providing direct protection through their biological activities. Herein, we review the state-of-the-art research on both microbes and natural products originated from fungus-growing termite tripartite symbiosis, highlighting the diversity of microbes and the uniqueness of natural product classes and their bioactivities. Additionally, we emphasize future research prospects on fungus-growing termite related microorganisms, with a particular focus on their potential roles in bioactive product discovery.

The complex and ever-changing characteristics of epidemic modelling, particularly when considering random elements, provide a substantial obstacle in creating precise and practical numerical methods for solving differential equations. This study contributes to this effort by introducing an innovative finite difference method for linear and non-linear stochastic and deterministic differential equations. This scheme expands explicitly upon the Euler Maruyama method, improving its precision for the deterministic aspect while ensuring coherence in dealing with stochastic terms. This contribution provides a numerical scheme that can be used to find solutions to linear and non-linear stochastic and deterministic differential equations. The scheme can be considered as the extension of the Euler Maruyama scheme for solving stochastic differential equations. The Euler Maruyama scheme offers a first-order accuracy of the deterministic model. Still, this scheme provides second-order accuracy for the deterministic part, whereas the integration of stochastic terms is the same in both schemes. The scheme is employed in a stochastic diffusive epidemic model with the effect of treatment, cure, and partial immunity. The comparison of the proposed scheme with the existing nonstandard finite difference method is made, and it is shown that the proposed scheme performs better than the nonstandard finite difference method in accuracy for the deterministic differential equations. It is also demonstrated that susceptible people rise whereas infected and recovered people decline by enhancing treatment cure rate. How does the cure rate of the treatment influence the number of the three populations, i.e., S(t), I(t), and R(t)? The results from the numerical simulation have provided useful insights into the dynamics of the epidemic model under various settings. This is particularly useful for influencing any public health plan and intervention. Thus, this work contributes numerical approaches and is an essential tool for epidemiological studies.

Transitioning from fossil fuels to renewable energy ensures a sustainable and green future by limiting greenhouse gas emissions and their nuisance effects. This study proposes a polygeneration system that harnesses renewable energy to produce multiple energy vectors simultaneously and equipped with storage to mitigate the impact of intermittent weather. To perform dynamic analysis, proposed system has been modeled, optimized, and simulated using the transient simulation software TRNSYS®. The designed model has been optimized in GenOpt, linked with TrnOpt, under the weather conditions of Gujrat, Pakistan. The power system is energized by solar photovoltaic, thermal and wind power to produce cooling, heating, electricity, hydrogen, and oxygen as energy vectors, and also provides electric, hydrogen, and thermal storage. Prime movers utilized for energy conversion consists of evacuated glass tube collector, photovoltaic panels, wind energy conversion system, fuel cell, electrolyzer, and absorption chiller, which facilitate air-conditioning, space and water heating, electric power supply for electric vehicle, building load & national grid, hydrogen for internal combustion engines, fuel cell electric vehicle and industrial applications, as well as oxygen for hospitals. The study has shown that thermal collector has 68% efficiency, a solar fraction 0.78, peak outlet temperature 143.77 °C, and generates a total 1399656.25 kJ thermal energy. The wind and photovoltaic systems have maximum efficiencies values 52.24 % and 10.90 % respectively, resulting in the production of 44.8 MWh electric energy. Embracing the synergy of solar and wind power in a polygeneration system holds the key to a sustainable and eco-friendly future.

Smoking has emerged as one of the leading global factors that is the source of health issues. It damages almost all of the body's organs. It damages various muscles and causes lung cancer. Additionally, it causes ulcers, pulmonary disease, and vascular deterioration. Except for the financial benefit to tobacco companies, manufacturers, and marketing companies, smoking has no advantages. Due to these factors, the present study exploited a feed forward neural networking based global optimization procedure with a local scheme to solve a mathematical model of smoking. A genetic based algorithm and sequential quadratic programming (GA-SQP) are utilized as hybridized global and local strategies. The model is categorized into five classes: potential smokers, occasional smokers, smokers, temporary quit, and permanent quit smokers. An objective optimization function is constructed to minimize the mean square error using the designed smoking model in form of feed forward neural networking. The comparative evaluation of hybrid GA-SQP and Adam numerical scheme is also assessed to authenticate the precision and correctness of the solution of the smoking model. The robustness, perfection, and convergence stability of GA-SQP are verified by establishing various statistical performance indicators. The quantitative analysis provides the minimum, mean, and semi-inter quartile range values for absolute errors up to 6 to 13 decimal places, demonstrating the worthiness and precision of the proposed GA-SQP.

In this work, we investigated the transmission of cholera infection through mathematical modelling. We calculated the contact number by applying the technique of next generation matrix method to find about infection dissemination as well as control in the population. Two types of constant solutions: infection free as well as infection present have been calculated. Stability of both constant solutions has been discussed. To control the dissemination of disease, we formulated an optimal control problem. Strategies, which are used to control this disease, have been composed on the consequences of sensitivity analysis. In order to gain better insight into the comparative results of analytical and numerical outcomes, the problem was revisited, and found that numerical results support our analytical findings. Our results conclude that pre-exposure vaccination can substantially reduce the risk of cholera.

Solar energy has emerged as a viable and competitive renewable resource due to its abundance and cost-effectiveness. To meet the global energy demands, there is a growing need for efficient devices with unique compositions. In this study, we designed and analyzed a perovskite solar cell (PSC) incorporating methylammonium tin iodide (CH3NH3SnI3) as the active optical absorber material, tin iodide (SnO2) as the electron transport layer (ETL), and copper thiocyanate (CuSCN) as the hole transport layer (HTL) using SCAPS-1D software for numerical investigations. Subsequently, the optimized outcomes were implemented in the PVSyst software package to derive the characteristics of a solar module based on the proposed novel solar cell composition. The objective of our research was to enhance the stability of solar cell for use in solar module. This was achieved by optimizing the thicknesses of the compositional layers which resulted in the enhancement of excess electron and hole mobilities and a reduction in defect densities, thereby leading to an improvement in the device performance. The optimization of excess electron and hole mobilities, as well as defect densities, was conducted to improve the device performance. SCAPS calculations indicated that the perovskite absorber layer (CH3NH3SnI3) may achieve the best possible performance with a maximum optimized thickness of 3.2 μm. The optimized thickness value for CuSCN-HTL and SnO2-ETL were found to be 0.07 μm and 0.05 μm respectively resulting in a maximum power conversion efficiency (PCE) of 23.57%. Variations in open circuit voltage (Voc), short circuit current (Jsc), fill factor (FF %), and quantum efficiency (QE) associated with the optimized thickness values of all layers in the ITO/SnO2/CH3NH3SnI3/CuSCN/Mo composition were critically analyzed. The use of these input parameters resulted in power creation of 557.4 W for a module consisting of 72 cells with an annual performance ratio of 80.3%. These recent investigations are expected to be effective in the design and fabrication of eco-friendly and high-performance solar cells in terms of efficiency.

The growing attractiveness of Artificial Neural Networks (ANNs) derives from their exceptional effectiveness in handling difficult and exceptionally nonlinear mathematical ideas. In complicated disciplines such as fluid mechanics, biological computation, and the field of biotechnology ANNs provide a diverse computing framework that is extremely valuable. This article's major aim is to harness the capabilities of the Levenberg-Marquardt technique with backpropagation intelligent neural networks (LM- BPINNs) to study there is still a lack of clarity regarding the mechanics underlying the increased heat transfer caused by dispersed nanoparticles. The using proposed LM-BPINNs to improve the heat transmission use activation energy and Hall current phenomena in nanofluid (HTAHCNF). The data set is obtained by using Lobatto-III. A method and then ANNs is applied. The LM- BPINNs technique is applied by utilizing reference datasets, with 80% of the dataset devoted to training, 10% to testing, and 10% to verification. The precision/accuracy and converging of developed LM- BPINNs are validated based on the obtained reliability via efficient fitness achieved on mean squared error (MSE), comprehensive regression analysis, and appropriate error histogram visualizations. A diminished MSE indicates that the model's predictions are more reliable. The outcome is consistent with getting a minimal absolute error close to zero, exhibiting the effectiveness of the proposed approach.

Plagiarism detection (PD) is a process of identifying instances where someone has presented another person's work or ideas as their own. Plagiarism detection is categorized into two types (i) Intrinsic plagiarism detection primarily concerns the assessment of authorship consistency within a single document, aiming to identify instances where portions of the text may have been copied or paraphrased from elsewhere within the same document. Author clustering, closely related to intrinsic plagiarism detection, involves grouping documents based on their stylistic and linguistic characteristics to identify common authors or sources within a given dataset. On the other hand, (ii) extrinsic plagiarism detection delves into the comparative analysis of a suspicious document against a set of external source documents, seeking instances of shared phrases, sentences, or paragraphs between them, which is often referred to as text reuse or verbatim copying. Detection of plagiarism from documents is a long-established task in the area of NLP with remarkable contributions in multiple applications. A lot of research has already been conducted in the English and other foreign languages but Urdu language needs a lot of attention especially in intrinsic plagiarism detection domain. The major reason is that Urdu is a low resource language and unfortunately there is no high-quality benchmark corpus available for intrinsic plagiarism detection in Urdu language. This study presents a high-quality benchmark Corpus comprising 10,872 documents. The corpus is structured into two granularity levels: sentence level and paragraph level. This dataset serves multifaceted purposes, facilitating intrinsic plagiarism detection, verbatim text reuse identification, and author clustering in the Urdu language. Also, it holds significance for natural language processing researchers and practitioners as it facilitates the development of specialized plagiarism detection models tailored to the Urdu language. These models can play a vital role in education and publishing by improving the accuracy of plagiarism detection, effectively addressing a gap and enhancing the overall ability to identify copied content in Urdu writing.

This study aimed to identify and evaluate the primary safety factors that contribute to accidents in downstream oil and gas construction projects in Malaysia. A quantitative approach was used, including a pilot survey and a main survey of individuals involved in these types of projects. Exploratory factor analysis (EFA) was performed on the pilot survey data, and structural equation modeling (SEM) was conducted on the main survey results. The study identified several critical safety factors, including inadequate safety training, insufficient safety procedures, and inadequate supervision. The findings provide insights into the significant safety elements in Malaysia's oil and gas construction industry and may aid in improving both theoretical and practical aspects of construction safety in this field. This research helps to fill a gap in knowledge regarding construction safety in Malaysia's oil and gas sector by highlighting the most important contributors to accidents in this industry.

The use of artificial neural networks (ANNs) to solve complex fluid dynamics problems has revolutionized computational approaches. This article explores novel ANN solutions for fluid problems. ANNs have demonstrated unmatched effectiveness in comprehending and forecasting fluidic behaviors, from mimicking complex fluid flow trends to optimizing structures for dynamics. This study emphasizes the significance of neural networks in revolutionizing our understanding and control of fluidic systems by using ANNs and Bayesian Regularization. This study examined the 3D Darcy-Forchheimer stretching flow (TDDFSF) of nanofluid under convective conditions along with homogeneous and heterogeneous reactions by employing the scheme of Bayesian Regularization with the procedure of backpropagation in neural networks (SBR-BNNs). The flow is influenced by Brownian diffusion, thermophoresis and zero nanoparticles mass flux condition. The PDEs given in this model are converted into nonlinear ODEs. Results from MSE, regression analysis, and error histogram are used to verify the performance of SBR-BNNs. The absolute errors lie in the ranges up to 10−9, which depict the worth of the solver SBR-BNNs. The absolute error measures the discrepancy between the values actually observed and those predicted by the SBR-BNNs model. SBR-BNNs' high precision in TDDFSF models can lead to more effective and reliable solutions.

The non-linear ordinary differential equations (NODEs) in this article are estimated and analyzed numerically using the capability of the Quartic Splines Method (QSM) for mathematical modeling of the Falkner-Skan fluidic system and its optimization through global search Genetic Algorithms (GAs) and local search Active-Set (AS) techniques. The concept of hybridization is used to optimize the obtained results and provide a boost to the suggested method, QSM, which allows for rapid iteration. Falkner-Skan fluid model (FSFM) is solved by the proposed technique QSM-GAs-AS. The FSFM is solved for three, seven, and twelve splines successfully. The problem is analyzed for three scenarios, in which each scenario is based on the variation of a parameter out of the three involved parameters, namely the wall mass transfer parameter (γ), the wall movement parameter (λ), and the stream-wise pressure gradient parameter (β), appearing in FSFM. The QSM-GAs-AS produces an interpolated function that is continuous up to its fourth derivative. The solution outcomes of FSFM, treated by the designed scheme QSM-GAs-AS, are presented graphically. The evaluation of the planned solution is done with a deterministic numerical solver, the Homotopy Analysis Method (HAM). Statistical analysis for multiple runs is used to examine the proposed scheme's convergence, exactness, and accuracy.

In this scholarly exploration, we employ new mapping method to unveil new soliton solutions to the nonlinear fractional Kudryashov’s equation, using β-derivative and M-Truncated fractional derivatives. Soliton phenomena, invaluable for enhancing computational capabilities in computer systems, find particular utility in tasks like data analysis, image processing, and simulations across various computer science domains. Our research reveals diverse solution forms, encompassing dark, periodic, kink, singular, dark-bright, and bright solitons. These versatile soliton types offer adaptable tools for both modelling and simulation. To enhance understanding, we provide 3D and 2D graphical representations, which facilitate a comparative analysis of solutions derived from the two fractional derivatives. Additionally we compare the effects of different fractional derivative operators (β-derivative and M-Truncated derivative) involve in the nonlinear fractional Kudryashov’s equation. The incorporation of both derivatives allows for a broader exploration of solution forms and enhance the understanding of the equation’s behaviour and its solution. Our work bridges soliton physics and computer science, underscoring the significance of soliton phenomena in advancing computational modelling and simulations. This research contributes to the intersection of theoretical mathematics and practical computer science, highlighting the vital role of solitons in scientific fields.

Applications of artificial intelligence (AI) via soft computing procedures have attracted the attention of researchers due to their effective modeling, simulation procedures, and detailed analysis. In this article, the designing of intelligence computing through a neural network that is backpropagated with the Levenberg-Marquardt method (NN-BLMM) to study the Cattaneo-Christov heat flow model at the mixed impulse stagnation point (CCHFM-MISP) past a Riga plate is investigated. The original model CCHFM-MISP in terms of PDEs is converted into non-linear ODEs through suitable similarity variables. A data set is generated for all scenarios of CCHFM-MISP through Lobatto IIIA numerical solver by varying Hartman number, velocity ratio parameter, inverse Darcy number, mixed impulse variable, non-dimensional constraint, Eckert number, heat generation variable, Prandtl number, thermal relaxation variable. To find the physical impacts of parameters of interest associated with the presented fluidic system CCHFM-MISP, the approximate solution of NN-BLMM is carried out by performing training (80 %), testing (10 %), and validation (10 %), and then the results are equated with the reference data to ensure the perfection of the proposed model. Through MSE, state transition, error histogram, and regression analysis, the outcomes of NN-BLMM are presented and analyzed. The graphical illustration and numerical outcomes confirm the authentication and effectiveness of the solver. Moreover, mean square errors for validation, training and testing data points along with performance measures lie around 10−10 and the solution plots generated through deterministic (Lobatto IIIA) approach and stochastic numerical solver are matching up to 10−6, which surely validate the solver NN-BLMM. The outcomes of M and B on velocity present the similar impacts. The velocity of material particles decreases under Da while, it increases through velocity ratio and magnetic parameters.

الملخص: أهداف البحث: للتحقيق في مواقف وتصورات طلاب طب الأسنان حول التعاون / التعليم المهني الداخلي (التعليم الداخلي) والتعلم المشترك واستكشاف تأثير التعليم الداخلي على التطوير الشخصي والمهني للطلاب المشاركين. طرق البحث: تم استخدام استبانة مصمم خصيصا في هذه الدراسة المقطعية. تتكون الاستبانة من سبعة عشر سؤالا يستهدف معرفة تصورات الطلاب حول: 1- تفضيل / رأي طلاب طب الأسنان حول التعليم الداخلي؛ 2- خبرة طلاب طب الأسنان حول تأثير التعليم الداخلي على نتائج التعلم والتطوير المهني؛ 3- ملاحظات الطلاب حول أهمية التعليم الداخلي في مختبرات المحاكاة السريرية وإعدادات مكان العمل. قام الطلاب بتقييم كل من العبارات السبعة عشر على مقياس ليكرت من 5 نقاط (النطاق: 1 = لا أوافق بشدة، 5 = أوافق بشدة). النتائج: تم تحليل ما مجموعه 259 استبانة (معدل الاستجابة = 65٪). كان جميع الطلاب على دراية بالتعليم الداخلي في مجال طب الأسنان (متوسط الدرجة = 4.22). فضل الطلاب التعلم التعاوني / المشترك مع زملائهم في الفصل. كان هناك إجماع بين الطلاب حول التأثير الإيجابي للتعليم الداخلي على التعلم المعزز، وتعزيز مهارات الاتصال، وإثراء العلاقات المهنية مع الموظفين الداعمين وكذلك مع المرضى، وتحسين المهارات التحليلية والنفسية الحركية، وفهم المشاكل المعقدة في العيادات، وفهم نقاط القوة والقيود التي تؤدي إلى تحسين الذات وزيادة الكفاءة والإنتاجية. الاستنتاجات: كان للتعليم الداخلي تأثير مقنع وقوي وإيجابي وفقا لتجربة طلاب طب الأسنان المشاركين في الدراسة. يوصى بتصميم منهج موحد ووضع مبادئ توجيهية لـلتعليم الداخلي في مؤسسات طب الأسنان من أجل التفاعل الفعال بين المتعلمين في جميع المراحل. Abstract: Objectives: To investigate dental students’ attitudes and perceptions about intraprofessional collaboration/education (IPC/IPE) and shared learning, and to explore the impact of IPC/IPE on the personal and professional development of participating students. Methods: A custom-designed questionnaire was used in this cross-sectional study. The questionnaire comprised 17 questions targeting to capture the student's perceptions about IPC/IPE using three factors: (1) dental students' preference/opinion about the IPC/IPE; (2) dental students' experience about the impact of IPC/IPE on learning outcomes and professional development; and (3) students' feedback about the significance of IPC/IPE in clinical/clinical simulation labs and workplace setting. The students rated each of the 17 statements on the 5-point Likert scale (range: 1 = strongly disagree to 5 = strongly agree). Results: A total of 259 responses were analysed (response rate = 65%). All students were aware of IPC/IPE in the field of dentistry (mean score = 4.22). The students preferred collaborative/shared learning with their own classmates. There was a consensus among students about the positive impact of IPC/IPE on enhanced learning, enhancement of communication skills, and enrichment of professional relationships with supporting staff as well as with the patients. There was also improved analytical and psychomotor skills, understanding of complex problems in the clinic, and understanding of strengths and limitations leading to self-improvement and increased efficiency and productivity. Conclusion: IPC/IPE had a compelling, powerful, and positive impact according to the experience of the participating dental students. It is recommended that a standardized curriculum be designed and guidelines set for IPC/IPE at dental institutions for effective interactions among students of all stages.

The present communication examines the unsteady-two-dimensional (2-D) squeezing flow of magnetohydrodynamic (MHD) Jeffrey fluid between two parallel plates (HT2DUSMHDJF). In its own plane, the bottom channel plate is extended while the upper plate squeezes towards the lower plate. The complete structure is takan is a rotating frame. Cattaneo-Christov heat flux model (CCHFM) is forced to explore the features of heat transfer. Distinct the conventional position, Instead of the Fourier heat conduction law, the heat flux is implemented by the Cattaneo-Christov theory. The resultant systems are computed through Artificial Neural Network (ANN). The behaviors of a number of relevant parameters are analyzed through graphs and numerical data. The velocity profile increases for Deborah number β and squeezing parameter sq and decreases for rotation, magnetic and relaxation time parameter ω, M, and λ1 respectively. Also the Skin friction coefficient decreases for λ1 and Sq and increases for high value of Deborah numbers β, ω and M.

The impact of activation energy in chemical processes, heat radiations, and temperature gradients on non-Darcian steady MHD convective Casson nanofluid flows (NMHD-CCNF) over a radial elongated circular cylinder is investigated in this study. The network of partial differential equations (PDEs) for NMHD-CCNF is developed using the modified Buongiorno framework, and the network of controlling PDEs is then transformed into ordinary differential equations (ODEs) utilizing the Von Karman method. Finally, the resulting non-linear ODEs are computed using the ND-solve approach to produce sets of data to assess the proposed model's skills, which can then be handled using the Bayesian Regularization technique of artificial neural networks (BRT-ANN). A novel stochastic computing-based application is being developed to evaluate the importance of NMHD-CCNF across a spinning disc that is radially stretched. The novelty and significance of results for better understanding, clarity, and highlighting the innovative contributions and significance of the proposed scheme. Further, to check the validity of the defined results for NMHD-CCNF, error charts, validation, and mean squared error suggestions are employed. The impact of multiple physical parameters on concentration, radial and tangential velocities, and temperature profiles is shown via tables and figures. Additionally, the results demonstrate that as the Forchheimer number, Casson nanofluid parameter, magnetic parameter, and porosity parameter are strengthened, the radial and rotational nanofluid mobility drops dramatically. The stretching parameter, on the other hand, has a parallel developmental trend. The heat generation parameter, the thermophoresis process, the thermal radiation parameter, and the Brownian motion of nanoparticles can all be increased to give thermal enhancement. On the other side, with larger estimates in thermophoresis parameters and the activation energy, there is a noticeable increase in the concentration profile.

This article suggests a fourth-order numerical approach for solving ordinary differential equations (ODEs) that are both linear and nonlinear. The suggested scheme is an explicit predictor-corrector scheme. For linear ODE, the proposed numerical scheme's stability area is discovered. The proposed strategy yields the same stability region as the traditional fourth-order Runge-Kutta method. In addition, partial differential equations (PDEs) are used to develop the mathematical model for the flow of non-Newtonian micro-polar fluid over the sheet and heat and mass transit using electric field effects. These PDEs are further transformed into dimensionless boundary value problems. Boundary value problems are resolved using the proposed shooting-based scheme. The findings show that increasing values of ion kinetic work and Joule heating parameters cause the temperature profile to climb. The results produced by the suggested strategy are compared to those discovered through earlier studies. The results of this study could serve as a starting point for future fluid-flow investigations in a secure industrial environment.

The current study presents an analysis of an inclined magnetized unsteady Cross fluid flowing over a coated disk with buoyancy effects and energy loss. The flow is modeled using the Navier-Stokes equations, including buoyancy, magnetic field, and energy loss effects based on the coated disk. The governing equations are solved numerically by applying the process of bvp4c to analyze the effects of inclination angle, magnetic field strength, and coating thickness using the flow characteristics. The results indicate that the buoyancy effects have a significant impact on the flow along with the results of flow velocity increment along with static pressure decrement. The magnetic field also has significant effects on the flow, which shows the decreasing velocity by increasing the magnetic field. Additionally, the coating thickness has significant effects on energy loss that decrease by increasing the coating thickness. The purpose of this work is to provide the valuable insight using the buoyancy, magnetic field, and coating thickness effects on the flow characteristics and energy loss based on the inclined magnetic unsteady cross flow passing over a coated disk.

This paper explores the double-chain model of DNA known as deoxyribonucleic acid, which has a significant role in the preservation and conversion of genetic data in biological fields. The setup consists of two rods that represent polynucleotide chains found in the DNA and are interconnected by an elastic membrane, showing the hydrogen bonds existing between the base pairs of the chains. The extended hyperbolic function method (EHFM) is analyzed to extract the solutions, including singular, bright, periodic, and kink soliton. The 3D, 2D, and contour graphs provide information on longitudinal and transverse displacements within the DNA structure. The visual exploration of some results reveals the presence of solitary waves in the DNA strands, and these findings have the potential to evaluate various applications. This study represents a significant advancement in our understanding of DNA dynamics, and illustrates the fundamental nature of genetic information transmission and processing.

Nanofinishing is the process by which ultrafine dispersion of nanomaterials is applied to a textile for the development of functionalities. The utilization of nanometal oxides as antimicrobial agents have shown a substantial antimicrobial property in cotton. In the present study, previously synthesized powder containing ZnO nanoflowers (ZnO NFs) was characterized for morphology, surface composition, roughness, and charge using Transmission electron microscopy (TEM), Scanning transmission electron microscopy (STEM), Atomic force microscopy(AFM) and Zeta potential. Optical properties of crystalline ZnO were determined by Photoluminescence (PL), Diffused reflectance Spectroscopy (DRS), and bandgap energy determination. Highly crystalline, ZnO NFs bearing crystal defects and high surface charge were loaded onto the pristine cotton by a dip coating method using Triton X-100 as dispersant and iSys MTX fabric binder. The pristine cotton fabric of 125 g/m2 was nano finished by loading 20,42 and 58 µg/cm2 (1–3 dip cycles) ZnO NFs respectively. The loading of ZnO NFs onto the surface of cotton fabric was confirmed by SEM and used for antibacterial activity against E. coli as a photocatalytic reactor. The prepared samples were irradiated with a UV lamp of λmax = 254 nm (15 min, 30 min, 45 min) and D65 artificial sunlight (60 min, 120 min, 180 min) to investigate their photocatalytic activity against pathogenic E. coli using modified Breed Smear’s method. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of ZnO NFs@ cotton were determined as 19.53 µg/ml and 39.06 µg/ml respectively after exposure to UV light. After exposure to sunlight MIC and MBC observed were higher i.e. 156.25 µg/ml and 312.5 µg/ml respectively showing lesser activity in sunlight as compared to ionizing UV radiations. To verify the photocatalytic activity, hydroxyl radicals generated by ZnO NFs@ cotton were also determinedtime-resolved PL on exposure to a UV lamp and D65 artificial sunlight. This nano-finished cotton is a promising candidate to be used as a medical textile with high antibacterial activity even after 20 washing cycles with only a 5% decrease in efficiency.

Plant communities that occur along water corridors are termed riparian vegetation. Although relatively narrow and long, riparian zones do provide an extensive spatially linear network of connectivity between existing habitats, allowing species to move more easily in response to changing microclimate conditions. Knowledge of riparian ecosystems in the Himalayan Region of Kashmir has not been explored, even though this region is unique and comprises some of the most important hotspots for Himalayan biodiversity. The current study characterizes the vegetation community along a riparian ecosystem in the Zabarwan Range, where there is no information on community characterization. After preliminary surveys, the three sites representing the study area were selected for detailed field sampling (Community-1 at the lower end of the stream; Community-2 at the middle of the stream, and Community-3 at the upper end of the stream). Data on the communities were gathered using the T-transect vegetation sampling method, and indicator plant species along the riparian zones were identified using Analysis of Indicator Species (AIS). A total of 71 plant species were collected, which were divided into 64 genera and 38 families. With nine species, the Rosaceae family was the largest, followed by the Asteraceae with six species. Three community associations, Ulmus-Parrotiopsis-Oplismenus, Salix-Rosa-Oplismenus, Celtis-Viburnum-Fragaria, were identified on the basis of an important value index. Diversity indices show significant differences in riparian vegetation between the types of plant communities. Shannon Diversity was also found to be higher in communities 2 and 3, indicating that the vegetation there was more diverse. Ulmus villosa is a common indicator species in Community-1 and Community-2, while Prunus tomentosa is a common indicator species in Community-3 and Community-1. Rosa webbiana was found as an indicator species in Community-2 and Community-3 while Celtis australis and Viburnum grandiflorum were restricted to Community-3. The identified indicator species in the vegetation associations can be employed for restoring riparian zones because of their excellent ecological performance and capacity for regrowth in these environments.

Nanofluid thermophysical characteristics are critical for predicting heat transfer behavior. This attempt provides a computational assessment of boundary layer flow and heat transfer behavior of fractional Maxwell viscoelastic nanofluid and their hybrids over a permeable vertical surface. The effects of Lorents and buoyancy forces are also considered in the flow region. The flow problem is modeled with novel distributed order time fractional derivatives to achieve control of the flow and heat transfer. Mid-point quadrature approach is used to process the distributed order integrals, whereas nonlinear coupled time fractional derivatives are discretized through the finite difference method along withL1- algorithm. The results shows that heat transfer rate enhanced 56.51% by enhancing the thermal Grashof number. Further, increase in nanoparticles volume fraction causes enhancement in thermal conductivity. More effects of the flow characteristic on velocity and temperature fields are shown graphically and analyzed in detail. The involvement of novel distributed fractional order derivatives, and nanoparticles enhanced the importance of the simulated results, which can be helpful to effectively control related thermal engineering issues, like temperature management in internal combustion engines, cooling devices, and heat exchangers.

Silica nanoparticles (SiNPs) are used extensively in consumer products and biomedical research basically due to ease of production and low cost. However, insufficient literature is reported regarding the toxicity and biocompatibility of SiNPs. The present study aimed to investigate the potential role of amorphous SiNPs on survival, growth, behavioral alterations, hematology and serum biochemistry of mice at four concentrations (control, 50, 100 and 150 mg/kg/day) of an oral supplementation for a period of 3 months. Signs of toxicity (lethargy, nausea, coma, tremors, vomiting and diarrhea, etc.) were noted at 9:00 am and 9:00 pm (twice a day) and the body weight of each of these mice was measured every week. The data were subjected to mean, standard deviation (S.D). Moreover, One-Way Analysis of Variance (ANOVA) and Dunnett’s test were applied for analysis of statistical significance between groups by using SPSS software, version 20. All the mice survived with minor alterations in behavior and no significant weight changes were observed during the stipulated time period. Complete blood count (CBC) analysis indicated non-significant (P ≥ 0.05) systemic dysfunctions of organ systems. However, there was elevation in the level of AST and ALT in the analysis of serum biochemistry, while the values of all other examined parameters were not-significant (P ≥ 0.05). The study concluded that orally administered large silica nanoparticles up to the dose level of 150 mg/kg/day are nontoxic for the in vivo use in mice.

ABSTRACT: Composting is a common practice used for treating animal manures before they are used as organic fertilizers for crop production. Whether composting can effectively reduce microbial pathogens and antibiotic resistance genes remain poorly understood. In this study, we compared 3 different dairy manure composting methods—anaerobic fermentation (AF), static compost (SC), and organic fertilizer production (OFP)—for their effects on antibiotic-resistant bacteria, antibiotic resistance genes, and microbial community diversity in the treated manures. The 3 composting methods produced variable and distinct effects on antibiotic-resistant bacteria, zoonotic bacteria, and resistance genes, some of which were decreased and others of which showed no significant changes during composting. Particularly, SC and OFP reduced chloramphenicol resistance gene fexA and opportunistic pathogen Vibrio fluvialis, whereas AF significantly reduced tetracycline resistance gene tetB and opportunistic pathogens Enterococcus faecium and Escherichia fergusonii. The compositions of microbial communities varied significantly during the composting processes, and there were significant differences between the 3 composting methods. In all 3 composts, the dominant phyla were Firmicutes, Proteobacteria, Bacteroidetes, and Actinobacteria. Interestingly, Firmicutes, Proteobacteria, and Bacteroidetes remained stable in the entire AF process, whereas they were dominated at the beginning, decreased at the early stage of composting, and rebounded at the later stage during SC and OFP. In general, SC and OFP produced a more profound effect than AF on microbial community diversities, pathogens, and dominant species. Additionally, Enterococcus aquimarinus was isolated from AF for the first time. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States function prediction analysis indicated that the genes related to membrane transport and amino acid metabolism were abundant in the 3 composts. The metabolism of amino acids, lipids, and carbohydrates increased as composting progressed. The biosynthesis of antibiotics was enhanced after fermentation in the 3 composting methods, and the increase in the SC was the most obvious. These results reveal dynamic changes in antibiotic-resistant bacteria, antibiotic resistance genes, microbial community composition, and function succession in different dairy manure composts and provide useful information for further optimization of composting practices.

In this work, the influence of thermal energy in term of heat source, thermal radiation and chemical reaction on magneto hydrodynamic Casson fluid flow model (MHD-CFM) over a nonlinear slanted extending surface with slip velocity in a Forchheimer permeable medium is numerically studied using the Levenberg Marquardt methodology with backpropagated learning mechanism. It is valuable to evaluate the flow of Cason fluids based on materials (such as drilling muds, clay coatings, various suspensions and certain lubricating oils, polymeric melts, and a wide range of colloids) in the occurrence of heat transfer. Using efficient data, PDEs of (MHD-CFM) were converted to ordinary differential equations. These obtained non-linear ODEs are then rectified using the computational power of the Lobatto IIIA approach to obtain a dataset of Levenberg Marquardt algorithm based trained neural networks (LMA-TNN) for six scenarios of this presented model, which were graphically represented using nftool to obtain regression, efficiency, fit curve, error bars, and trained state analysis. The velocity, temperature, and concentration profiles were computed, and the findings were presented. Additionally, the skin friction coefficient, Nusselt number, & local Sherwood number are explored. The graphs show that when values of radiation parameter and the Forchheimer porous media parameter increase, the temperature of the plate drops. In the existence of a chemical process and a high Schmidt number, the concentration drops. The accuracy achieved in terms of relative error demonstrates the validity and significance of the solution process.

Scraped surface heat exchanger (SSHE) is vibrantly utilized in various industries. The heat exchanger devices play pivotal role in the energy transfer phenomenon between the fluids, and it improves the efficient use of energy. The steady isothermal flow of a rheological aqueous ionic solution in a narrow gap scraped surface heat exchanger is explored. The mathematical model is established with the aid of lubrication approximation theory and extracted analytical solution. Exact analytical expressions are gathered for velocities, volume fluxes, pressure gradients, pressure at the edges of the blades and stream functions in the various stations of SSHE. Strength of significant flow parameters on the velocities, volume fluxes, pressure gradients, pressure at the edges of the blades and stream functions is revealed with aid of various plots. Significant enhancement in the velocity profile is observed in main domain of the flow with increasing ratio of relaxation to retardation time parameter and Helmholtz Smoluchowski velocity. While, it is observed that electro-osmotic parameter creates impediments to the flow.

A new model of fractional for the influence of consciousness initiatives on epidemic incidences (ICIEI) is described in this research. The fractional Caputo operator (FCO) is used to expand this system to the typical awareness program model (ICIEI-FCO). Analytically and quantitatively, the properties of the new system are investigated. This article presents an artificial neural network-based fractional model (ANN-FM) for estimating the efficacy of epidemic outbreak awareness initiatives. The reliability, resolution, durability, and robustness of the proposed model are examined using the suggested ANN-BLM approach for five distinct situations. The population of the model is created by utilizing the power of the explicit Runge-Kutta numerical approach, and it is represented by a system of nonlinear ordinary differential equations. The Grunwald–Letnikov (GL) technique is used to numerically evaluate the modeled differential system of the physical issue for multiple scenarios to anticipate numerical data, and these results are utilized as a reference dataset of the networks. The data needed to answer the fractional model's questions on how awareness campaigns affect epidemic outbreaks is broken down as follows: training takes up 80% of the time, testing 10%, and authorization 10%. There are two aspects to the strategy: First, the fundamental ANN-BLM operator performances are displayed. In the meantime, the ANN-BLM execution approach is used to address the fractional-order problem. The GL-mathematical system was used to compare the numerical findings. The TSs, regression, correlation, EHs, and MSE are used to demonstrate the dependability and competency of ANN-BLM as well as their numerical performances in the presented numerical findings, which were developed using ANN-BLM to lower the MSE. The Levenberg-Marquart training (LMT) algorithm is used to optimize network results in terms of mean-square errors, training states graphs, errors of the histogram, recession analysis, auto-correlation and time series responses, which demonstrates the system's accurate and proficient trend acknowledgment. The mean-square error fitness analysis in the ranges of 10-6 to 10-11 validates the authenticity and effectiveness of the designed solver. The suggested AI-based study is expected to pave the way for new, creative approaches to fractional order modeling and analysis of naturally unpredictable dynamic systems.

Assessments of forest ecosystem health for use in adaptive management need an integrative multi-indicator examination at the stand scale. To assess forest health, we exained multiple forest indicators including diversity, age structure, regeneration, and edaphic factors of the dominant and associated tree species in their natural forest habitats. A stratified random cluster sampling strategy was used to gather vegetation samples from the five main forest types in the Zabarwan Mountain Range—Acacia forest (ACFT), Broad leaved forest (BLFT), Oak forest (OKFT), Pinus wallichiana forest (PWFT), and Scrub forest (SRFT). The Pearson method and canonical correspondence analysis (CCA) were used to investigate the relationship between tree species and edaphic factors. A total of 22 tree species were found, of which 13 were exotic and 9 were native. The proportion of exotic species was highest in OKFT (85%), followed by BLFT (75%), and the least (50%) SRFT. The BLFT forest type had the highest Shannon diversity while the lowest was the SRFT. ACFT and BLFT forest types have significantly higher Shannon diversity indexes than other forest types. Based on the density-girth class distribution, ACFT & SRFT forest types showed an Inverse-J distribution pattern, indicating a stable population structure. The dominant tree species, such as Populus alba in BLFT, demonstrated comparatively no regeneration, whereas Parrotiopsis jacquemontiana in SRFT, Pinus wallichiana in PWFT, Quercus robur in OKFT, and Robinia pseudoacacia demonstrated adequate regeneration performance. Overall exotic tree species such as Robinia pseudoacacia, Prunus cerasifera, Celtis australis, and Ailanthus altissima showed high/sufficient regeneration performance. The average seedling/tree value for all forest types in the area was 2.14, with the highest value at BLFT (3.61) and the lowest value at SRFT (0.71). In the CCA it showed that SRFT forests were greatly influenced by salinity and organic carbon, whereas ACFT and OKFT forests had comparable habitat preferences and were mutually influenced by electrical conductance and phosphorus availability. Prunus cerasifera was the only species positively associated with available calcium. By combining the data of numerous field-based indicators into a single integrated study, our research will give decision-makers an update on a forest's current and anticipated health.

A novel theoretical model is devised for electro osmotic flow of Williamson fluid model by incorporating heat transfer phenomena in a micro ciliated channel. The physical fluid model is designed by exploiting the renowned Debye–Huckel, long wavelength small Reynolds number approximation. The exact expression for Poisson–Boltzmann equation is obtained to find the analytical solution for axial velocity, pressure gradient, temperature and stream functions comprehensively by using perturbation technique. Whereas, transverse velocity and pressure rise profile are explored numerically through the MATHEMATICA software infrastructure. The variations of paramount significant parameters on the velocity distribution, temperature, pressure gradient and pressure rise per wavelength are demonstrated graphically. It has been investigated that axial velocity profiles escalates with enhancing Weissenberg, electro-osmotic, mobility of medium parameter, while dual behavior is recorded for the case of traverse component of velocity. Cilia plays a crucial role in ductus efferentes and assumed to transfer sperm through the rete testis to the epididymis. Besides above mentioned application cilia like actuators serve in micro-mixers to regulate the flow in bio-sensors and in various drug-delivery mechanism.

In recent years, hybrid genetic algorithms have attracted a lot of attention and are being utilized more frequently to address real-world issues. A genetic algorithm can combine multiple approaches within its framework to create a hybrid that benefits the most from the combination. In this article, a new methodology of hybrid genetic algorithm using sequential quadratic programming (HGASQP) is presented to optimize the transmission dynamics of the Ebola virus disease model (EVDM) by implementing the intelligent paradigm of feed-forward neural networks. The Ebola virus, also known as Ebola haemorrhagic, is a transmitter virus that was initially transferred to humans by wild and domestic animals, and then it spread from human to human. To control this spread, a mathematical model is proposed consist of susceptible-S, exposed-E, infected-I, quarantined-Q and recovered-R classes. Coupling GA with local search SQP take the advantages of both for determining the global optimum as well as local optimum while at the same time also providing fast convergence. The mean squared error (MSE) is minimized as fitness objective function to optimize HGASQP. The optimized solutions of HGASQP are compared with the numerical results of the Adam approach in order to validate the effectiveness and robustness of the proposed algorithm. Furthermore, statistical and quantitative analysis are also established to authenticate the performance of HGASQP. It has been demonstrated that the suggested optimization approach is precise and can boost optimization effectiveness.