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Biomimetic artificial olfactory cilia have demonstrated potential in identifying specific volatile organic compounds linked to various diseases, including certain cancers, metabolic disorders, and respiratory conditions. These sensors may facilitate non-invasive disease diagnosis and monitoring. Cilia Motility is the coordinated movement of cilia, which are hair-like projections present on the surface of particular cells in different species. Cilia serve an important part in several biological functions, including motility, fluid movement, and sensory reception. Cilia motility is a complicated process that requires the coordinated interaction of structural components and molecular pathways. Cilia are made up of a highly structured structure known as the axoneme, which is made up of microtubules grouped in a unique pattern. The axoneme is made up of nine outer doublet microtubules and a core pair of singlet microtubules. This arrangement offers structural support and serves as a scaffold for the proteins involved in ciliary movement. Our latest endeavors investigate these Multiphysics phenomena in ciliary beating flows that are inspired by biology, utilizing copper, gold, and titania nanoparticles. We examine their functions in biological systems such as peristaltic transport computationally. Our models give precise two- and three-dimensional velocity, temperature, and concentration solutions by integrating transverse magnetohydrodynamics with laser heating. Furthermore, at the channel wall expressions, the skin friction coefficient, Sherwood number, Nusselt number and optimization of entropy generation are acquired and analyzed. Important properties of the velocity and scalar profiles are revealed by a thorough analysis of dimensionless parameters. The simplified examination provides more insight into the trapping patterns that result from the complex interaction between nanofluid rheology and optics. These findings greatly contribute to our knowledge and improvement of nanofluidic transport technologies in a variety of fields supporting industry, sustainability, and medicine. Our combined computational and experimental methodology clarifies the complex dynamics in these systems and provides design guidance for the engineering of improved fluidic devices that make use of multifunctional nanomaterial interfaces and peristaltic motion. [ABSTRACT FROM AUTHOR]

This study aims to propose a central composite design (CCD) combined with response surface methodology (RSM) to create a statistical experimental design. A new parametric optimization of entropy generation is presented. The flow behavior of magnetohydrodynamic hybrid nanofluid (HNF) flow through two flat contracting expanding plates of channel alongside radiative heat transmission was considered. The lower fixed plate was externally heated whereas the upper porous plate was cooled by injecting a coolant fluid with a uniform velocity inside the channel. The resulting equations were solved by the Homotopic Analysis Method using MATHEMATICA 10 and Minitab 17.1. The design consists of several input factors, namely a magnetic field parameter ( M ), radiation parameter ( N ) and group parameter ( B r / A 1 ). To obtain the values of flow response parameters, numerical experiments were used. Variables, especially the entropy generation ( N e ), were considered for each combination of design. The resulting RSM empirical model obtained a high coefficient of determination, reaching 99.97% for the entropy generation number ( N e ). These values show an excellent fit of the model to the data. [ABSTRACT FROM AUTHOR]

Multiphase fluids exhibit immiscible, heterogeneous structures like emulsions, foams, and suspensions. Their complex rheology arises from relative phase proportions, interfacial interactions, and component properties. Consequently, they demonstrate nonlinear effects—shear‐thinning, viscoelasticity, and yield stress. Peristalsis generates fluid flow by propagating contraction waves along channel walls. This mechanism can effectively transport multiphase and non‐Newtonian fluids in microsystems. Accurate modeling requires considering evolving phase relations, variable viscosity, slip, and particle migration anomalies, using approaches like homogenization theory or volume‐averaging. Applications include peristaltic pumping of emulsified biopharmaceuticals, microscale mixing/separating of multiphase constituents, and enhancing porous media fluid flow in oil reservoirs. Analytical and computational approaches to modeling multiphase fluid flows in peristaltic conduits provide an enhanced understanding of their complex dynamics, toward innovating engineering systems. An analytical approach is taken to model non‐Newtonian Ree‐Eyring fluid flows in asymmetric, peristaltic systems. Governing differential equations incorporate key parameters and yield closed‐form solutions for velocity, flow rate, and permeability. Suitable assumptions of long wavelength, and low Reynolds number provide accuracy. In parallel, an artificial neural network (ANN) is developed using supervised learning to predict permeability. The inputs consist of channel asymmetry, Reynolds number, amplitude ratio, and other physical factors. Outcomes validate both methodologies—analytical equations derive precise relationships from first principles, while ANNs reliably learn the system patterns from input‐output data. Additionally, ANNs can tackle more complex fluid dynamics problems with speed and adaptability. Their promising role is highlighted in developing new fluid models, improving the efficiency of simulations, and designing control systems. Side‐by‐side analytical and ANN simulation plots will further highlight ANN capabilities in emulating the system characteristics. This paves the path for employing machine learning to investigate multifaceted flows in flexible, peristaltic systems at scale. Performing a graphical examination of the engineering skin friction coefficient across a range of parameters, encompassing volume fraction, first and second order slip, Ree–Eyring fluid attributes, and permeability. [ABSTRACT FROM AUTHOR]

Background: Etiology of chronic liver disease (CLD) has an impact on the profile of spontaneous bacterial peritonitis (SBP) in terms of infection acquisition, resistance pattern, and treatment outcome. Aims and Objectives: This study aimed to study infection acquisition, antibiotic resistance pattern, and treatment outcome in patients with non-alcoholic fatty liver disease (NAFLD) cirrhosis with SBP. Materials and Methods: This observational prospective study was carried out in Government Medical College; Srinagar: over a period of 2 years from 2017 onward. We did analysis of patients with NAFLDs cirrhosis. We studied infection acquisition, antibiotic resistance pattern, and treatment outcome in this group of patients. Results: Over 2-year period, 246 patients were enrolled. The mean age of patients was 57.09±13.90 years. Hepatitis B virus and NAFLD were the major etiological contributors to the burden of CLD amounting to 51.20% in Kashmir. SBP was present in 33/57 (57.90%) of NAFLD cirrhosis patients. Child-Turcotte-Pugh Class C was 63.60%. Patients with culturepositive SBP (CP-SBP) were 45.45%. In patients of SBP with etiology other than NAFLD, CP-SBP was 61% whereas culture-negative SBP was 38.77%. Conclusion: In our region, NAFLD is one of the leading contributors to CLD. In this study, the trend toward worse treatment outcome and mortality in the patients with NAFLD SBP was observed. [ABSTRACT FROM AUTHOR]

Abstract A novel adaptive wideband beamforming method is proposed, where beamforming is achieved by LMS based space-time adaptive filtering algorithm. Conventional broadband beamforming requires desired signal to be incident from the broadside i.e. direction normal to the array. The new method overcome this constraint by make use of digital delay filters which compensate the delay of receiving data, so that the signal of interest can be treated as if it had arrived from the broadside. Then LMS based space-time adaptive filtering algorithm is applied to achieve beamforming. Another advantage of the proposed methodology is its low computational complexity while sustaining high resolution. The effectiveness and advantage of the proposed methodology is theoretically investigated, and computational complexity is also addressed. To verify the theoretical analysis, computer simulations are implemented and comparisons with other algorithms are made.

Abstract Space-frequency DORT (SF-DORT) is an effective time-reversal (TR) imaging method due to its immunity to the noise and adaptability of complex environment. However, some potential drawbacks, such as low range and co-range resolutions, make SF-DORT inferior to the Space-frequency MUSIC (SF-MUSIC). In this paper, we propose a novel high-resolution imaging method utilizing SF-DORT combining an extrapolated virtual array (EASF-DORT). The virtual extrapolated TR array is created by autoregressive vector extrapolation (ARVE). In this way, the aperture of the TR array is extended significantly and the drawbacks associated with SF-DORT can be improved. Simulation results demonstrate that SF-DORT combining the extrapolated virtual array achieve much higher range and co-range resolutions than conventional SF-DORT.

The efficient operation of power-electronic-based systems heavily relies on the reliability and longevity of battery-powered systems. An accurate prediction of the remaining useful life (RUL) of batteries is essential for their effective maintenance, reliability, and safety. However, traditional RUL prediction methods and deep learning-based approaches face challenges in managing battery degradation processes, such as achieving robust prediction performance, to ensure scalability and computational efficiency. There is a need to develop adaptable models that can generalize across different battery types that operate in diverse operational environments. To solve these issues, this research work proposes a TransRUL model to enhance battery RUL prediction. The proposed model incorporates advanced approaches of a time series transformer using a dual encoder with integration positional encoding and multi-head attention. This research utilized data collected by the Centre for Advanced Life Cycle Engineering (CALCE) on CS_2-type lithium-ion batteries that spanned four groups that used a sliding window technique to generate features and labels. The experimental results demonstrate that TransRUL obtained superior performance as compared with other methods in terms of the following evaluation metrics: mean absolute error (MAE), root-mean-squared error (RMSE), and R 2 values. The efficient computational power of the TransRUL model will facilitate the real-time prediction of the RUL, which is vital for power-electronic-based appliances. This research highlights the potential of the TransRUL model, which significantly enhances the accuracy of battery RUL prediction and additionally improves the management and control of battery-based systems. [ABSTRACT FROM AUTHOR]

There are several uses for bio-convection mechanisms, such as in sedimentary pools, fuel models, and microbially accelerated oil restoration. These are the basis of motile microorganisms which are applied to improve the mixability of fluid and tiny nanoparticles because they are accountable for biological transmission procedure. This work examines the bio-convective nanoparticles fluid flow and electrically conducting incompressible magneto-hydrodynamic flow of nanofluid towards a chemically reactive stretchable surface. Buongiorno model is considered here, which incorporates Brownian motion and thermophoretic diffusion, is employed in this study to explain how nanofluids improve heat transfer. The controlling boundary layer nonlinear PDEs are converted into ODEs by utilizing certain suitable similarity approaches. Numerical scheme is adopted to solve the transformed ordinary differential equations. The main findings of this research are that the magnetic field slows motion of the fluid when the thermal and mass buoyancy forces speed it up, and that thermophoretic diffusion increases the temperature and concentration of dimensionless fluids, resulting in stronger concentration and thermal boundary layers. The Brownian motion, concentration exponent, and chemical reaction parameters show an inverse relationship between temperature and concentration. Additionally, the first-order chemical reaction creates a lighter concentration boundary layer while the magnetic field's presence due to impact of thermal and mass buoyancies helps to lower the heat transfer rate and shear stress. The findings show that while the Nusselt number and wall shear stress values are declining, the microorganism number is increasing. [ABSTRACT FROM AUTHOR]

Since heterogeneous wireless sensor networks consist of sensor nodes of varying capacity and energy-constrained, effective routing techniques are essential to ensure the proper functioning of the systems. Most traditional routing techniques fail to dynamically adjust to varying network conditions, leading to ineffective use of energy and poor performance. Therefore, deep Q-Networks implementation of reinforcement learning provides a beneficial approach to the problem due to adaptive routing decisions depending on the environmental signals and systems' performance. Therefore, the suggested approach integrates Deep Q-Network into the data routing framework for different Wireless Sensor Networks to improve energy-efficiency and ensure data delivery. The DQN agent is designed to pick routing functions that maximize total rewards which depend on energy consumption, packet delivery, and network stability. Hence, the decentralized learning allows each sensor node to develop its routing policy based on the local environment under the interactions with their neighbors. Therefore, the approach promotes the ability to adapt and learn, crucial for changing network conditions. Thus, extensive simulation was conducted to assess the applicability of the DQNbased routing for different WSNs, proving the significant reducing of energy consumption compared to traditional routing approaches with an average of 25% regardless of the network formation and traffic conditions. This approach also demonstrates lower packet loss of 15%, revealing enhanced data transfer reliability. In particular, the existing on demand routing protocols, only forward the request that arrives first from each route discovery process. The attacker exploits this property of the operation of route discovery. The network lifetime was extended by 30% showing growing energy efficiency for a long-term run. In general, the integration of Deep Q-Networks into data routing provides an excellent opportunity to improve energy-efficiency in different types of wireless sensor networks. Hence, the proposed approach effectively optimizes the routing solutions in real-time, using adaptive lenience, also showing enhancing data delivery, and improving the systems' lifetime. Hence, the presented results prove the capability of reinforcement learning methods to address the growing challenges of WSNs and leave space for further research in autonomous WSN improvement. [ABSTRACT FROM AUTHOR]

This research presents a novel conformable-Caputo fractional non-polynomial spline method for solving the time-fractional Korteweg-de Vries (KdV) equation. Emphasizing numerical analysis and algorithm development, the method offers enhanced precision and modeling capabilities. Evaluation via the Von Neumann method demonstrates unconditional stability within defined parameters. Comparative analysis, supported by contour and 2D/3D graphs, validates the method's accuracy and efficiency against existing approaches. Quantitative assessment using L2 and L∞ error norms confirms its superiority. In conclusion, the study proposes a robust solution for the time-fractional KdV equation. [ABSTRACT FROM AUTHOR]

We conducted a systematic review and meta-analysis to examine the protective effects of botulinum toxin-A (Botox-A) on spasticity and nociceptive pain in individuals with spinal cord injuries (SCIs). PubMed, Embase, and Cochrane Library databases were searched from inception to July 2023. The primary outcome of interest was spasticity and nociceptive pain. We pooled the available data using the generic inverse variance method, and we used a fixed-effect/random-effects model. We then calculated standardized mean difference (SMD) and 95% confidence intervals (95% CIs) to estimate the effect size. A total of fourteen studies meeting the inclusion criteria comprised two randomized controlled trials, five pre-post studies, and seven case reports. Across the various study designs, the majority of trials were assessed to have fair to high quality. The meta-analysis shows that Botox-A significantly decreased spasticity (SMD, -1.73; 95% CI, -2.51 to -0.95; p<0.0001, I2=48%) and nociceptive pain (SMD, -1.79; 95% CI, -2.67 to -0.91; p<0.0001, I2=0%) in SCI patients. Furthermore, Botox-A intervention improved motor function, activities of daily living (ADL), and quality of life. Our study suggests that Botox-A may alleviate spasticity and nociceptive pain in SCI patients. Moreover, the observed improvements in motor function, ADL, and overall quality of life following Botox-A intervention underscore its pivotal role in enhancing patient outcomes. [ABSTRACT FROM AUTHOR]

The hepatobiliary system is vital for the biotransformation and disposition of endogenous molecules. Any impairment in the normal functioning of the hepatobiliary system leads to a spectrum of hepatobiliary diseases (HBDs), such as liver cirrhosis, fatty liver, biliary dyskinesia, gallbladder cancer, etc. Especially in pregnancy, HBD may result in increased maternal and fetal morbidity and mortality. Maternal HBD is a burden to the fetus's growth, complicates fetal development, and risks the mother's life. In fetal programming, the maternal mechanism is significantly disturbed by multiple factors (especially diet) that influence the development of the fetus and increase the frequency of metabolic diseases later in life. Additionally, maternal under-nutrition or over-nutrition (especially in high-fat, high-carbohydrate, or protein-rich diets) lead to dysregulation in gut hormones (CCK, GLP-1, etc.), microbiota metabolite production (SCFA, LPS, TMA, etc.), neurotransmitters (POMC, NPY, etc.), and hepatobiliary signaling (insulin resistance, TNF-a, SREBPs, etc.), which significantly impact fetal programming. Recently, biotherapeutics have provided a new horizon for treating HBD during fetal programming to save the lives of the mother and fetus. This review focuses on how maternal impaired hepatobiliary metabolic signaling leads to disease transmission to the fetus mediated through the gut–brain axis. [ABSTRACT FROM AUTHOR]

This paper presents a novel robust output feedback control that simultaneously performs both stabilization and trajectory tracking for a class of underactuated nonholonomic systems despite model uncertainties, external disturbance, and the absence of velocity measurement. To solve this challenging problem, a generalized normal form has been successfully created by employing an input–output feedback linearization approach and a change in coordinates (diffeomorphism). This research mainly focuses on the stabilization problem of nonholonomic systems that can be transformed to a normal form and pose several challenges, including (i) a nontriangular normal form, (ii) the internal dynamics of the system are non-affine in control, and (iii) the zero dynamics of the system are not in minimum phase. The proposed scheme utilizes combined backstepping and sliding mode control (SMC) techniques. Furthermore, the full-order high gain observer (HGO) has been developed to estimate the derivative of output functions and internal dynamics. Then, full-order HGO and the backstepping SMC have been integrated to synthesize a robust output feedback controller. A differential-drive type (2,0) the wheeled mobile robot has been considered as an example to support the theoretical results. The simulation results demonstrate that the backstepping SMC exhibits robustness against bounded uncertainties. [ABSTRACT FROM AUTHOR]