Multiwaves, breathers, lump and other solutions for the Heimburg model in biomembranes and nerves

In this manuscript, a mathematical model known as the Heimburg model is investigated analytically to get the soliton solutions. Both biomembranes and nerves can be studied using this model. The cell membrane's lipid bilayer is regarded by the model as a substance that experiences phase transitions. It implies that the membrane responds to electrical disruptions in a nonlinear way. The importance of ionic conductance in nerve impulse propagation is shown by Heimburg's model. The dynamics of the electromechanical pulse in a nerve are analytically investigated using the Hirota Bilinear method. The various types of solitons are investigates, such as homoclinic breather waves, interaction via double exponents, lump waves, multi-wave, mixed type solutions, and periodic cross kink solutions. The electromechanical pulse's ensuing three-dimensional and contour shapes offer crucial insight into how nerves function and may one day be used in medicine and the biological sciences. Our grasp of soliton dynamics is improved by this research, which also opens up new directions for biomedical investigation and medical developments. A few 3D and contour profiles have also been created for new solutions, and interaction behaviors have also been shown.

Physicomechanical, Wettability, Corrosion, Thermal, and Microstructural Morphology Characteristics of Carbonized and Uncarbonized Bagasse Ash Waste-Reinforced Al-0.45Mg-0.35Fe-0.25Si-Based Composites: Fabrications and Characterizations

An effort was being made to incorporate waste bagasse ash (WBA) both in carbonized and uncarbonized form into the formulation of Al6063 matrix-based metal matrix composites (MMC's) by partially substituting ceramic particles for them. In the process of developing composites, comparative research on carbonized WBA and uncarbonized bagasse powder was carried out in the role of reinforcement. Microstructure investigations revealed that carbonized WBA particles were properly distributed throughout the aluminum-base metal matrix alloy. They also had the appropriate level of wettability. The reinforcement of carbonized WBA particles in AA6063-based matrix material had a maximum tensile strength of 110 MPa and a maximal hardness of 39 BHN when 3.75 wt % of the particles were used. The deterioration in tensile strength (6.25 wt % of WBA) and the appearance of porosity and blowholes can be enumerated by tensile fractography-based scanning electron microscopy (SEM) analysis. The reinforcement of carbonized WBA particles in AA6063-based matrix material was found to have a maximal percent elongation of 14.42% and the highest fracture toughness of 15 Joules when 1.25 wt % of the particles were employed. For AA6063/3.75 wt % carbonized WBA-based MMC's, the minimum percent porosity was determined to be 5.83, and the minimum thermal expansion was found to be 45 mm3. As the percentage of reinforcement in bagasse-reinforced composites increases, the density of the material, the amount of corrosion loss, and the cost all decrease gradually. The AA6063 matrix, with a composition of 3.75 wt % carbonized WBA-based MMC's, had satisfactory specific strength and corrosion loss. The AA6063 alloy composite's microstructure analysis revealed that carbonized WBA enhanced the material's mechanical characteristics, contributing to its excellent mechanical capabilities. The results of the corrosion test showed that carbonized WBA-reinforced composites exhibited reduced weight loss due to corrosion, whereas uncarbonized bagasse powder was an inappropriate reinforcement. The SEM analysis of AA6063 alloy/3.75 wt % carbonized WBA ash reinforcement-based MMC's exposed to a 3.5 wt % NaCl solution has exhibited the development of corrosion pits as a result of localized attack by the corrosive environment. The thermal expansion test showed that the composite with uncarbonized bagasse powder as reinforcement have a high shrinkage rate in comparison with the composite with 3.75 wt %. The composite's mechanical characteristics and thermal stability were enhanced by the presence of hard phases like SiO2, Al2O3, Fe2O3, CaO, and MgO, as revealed by X-ray diffraction analysis. This made it suitable for use in a variety of applications.

Exploring Microstructural, Interfacial, Mechanical, and Wear Properties of AlSi7Mg0.3 Composites with TiMOVWCr High-Entropy Alloy Powder

This study explored the impact of varying weight percentages of TiMoVWCr high-entropy alloy (HEA) powder addition on A356 composites produced using friction stir processing (FSP). Unlike previous research that often focused on singular aspects, such as mechanical properties, or microstructural analysis, this investigation systematically examined the multifaceted performance of A356 composites by comprehensively assessing the microstructure, interfacial bonding strength, mechanical properties, and wear behavior. The study identified a uniform distribution of TiMoVWCr HEA powder in the composition A356/2%Ti2%Mo2%V2%W2%Cr, highlighting the effectiveness of the FSP technique in achieving homogeneous dispersion. Strong bonding between the reinforcement and matrix material was observed in the same composition, indicating favorable interfacial characteristics. Mechanical properties, including tensile strength and hardness, were evaluated for various compositions, demonstrating significant improvements across the board. The addition of 2%Ti2%Mo2%V2%W2%Cr powder enhanced the tensile strength by 36.39%, while hardness improved by 62.71%. Similarly, wear resistance showed notable enhancements ranging from 35.56 to 48.89% for different compositions. Microstructural analysis revealed approximately 1640.59 grains per square inch for the A356/2%Ti2%Mo2%V2%W2%Cr processed composite at 500 magnifications. In reinforcing Al composites with Ti, Mo, V, W, and Cr high-entropy alloy (HEA) particles, each element imparted distinct benefits. Titanium (Ti) enhanced strength and wear resistance, molybdenum (Mo) contributed to improved hardness, vanadium (V) promoted hardenability, tungsten (W) enhanced wear resistance, and chromium (Cr) provided wear resistance and hardness. Anticipating the potential applications of the developed composite, the study suggests its suitability for the aerospace sector, particularly in casting lightweight yet high-strength parts such as aircraft components, engine components, and structural components, underlining the significance of the investigated TiMoVWCr HEA powder-modified A356 composites.

Fabrication and Characterization of Montmorillonite Clay/Agar-Based Magnetic Composite and Its Biological and Electrical Conductivity Evaluation

Montmorillonite clay and agar are naturally occurring materials of significant importance in designing biocompatible materials tailored for applications in biotechnology and medicine. The introduction of magnetic properties has the potential to significantly boost their characteristics and expand their applications. In this study, we have successfully synthesized highly intercalated magnetic composites, incorporating magnetic iron oxide nanoparticles (MNPs), montmorillonite clay (MMT), and agar (AG), through a thermo-physicomechanical method. Three samples of MMT-AG with 2, 1.5, and 0.5% MNPs and three sample composites of MNPs-AG with 2, 1, and 0.5% MMT clay are prepared. The synthesized composites were characterized by SEM, XRD, TGA, DTA, and FTIR. SEM analysis revealed a uniform dispersion of MNPs and MMT in the composite. The XRD pattern confirmed the presence of MNPs in the composite site. The TGA and DTA results demonstrated improved thermal stability due to the MNP incorporation. FTIR spectra showed all of the constituents of agar, MNPs, and MMT clay. The swelling ratio was observed to range from 835% to 1739%. The swelling study indicated an increased hydrophobicity with the addition of MNPs to the composite. Antibacterial activities revealed a significant inhibition of Escherichia coli (E. coli) growth by ranging from 10 to 19 nm in the composite. The composite also exhibited a considerable antioxidant action, with IC50 values of 7.96, 46.55, and 57.58 μg/mL, and electrical properties just like conductors.

A novel approach towards web browser using the concept of a complex spherical fuzzy soft information

The modern technology is the practical application of scientific knowledge, whether in industry or daily life, for goals or purposes. More quickly than any other technological advancement in human history, digital technologies have advanced. The technology sector is expanding and provides both new educational opportunities and innovative, exciting products. Right now, one of the most widely used and fascinating technologies is the web browser. This article introduced the novel concepts of complex spherical fuzzy soft relations (CSFSRs) by using the Cartesian Product (CP) of two complex spherical fuzzy soft sets (CSFSSs). Additionally, examples are used to clarify various types of relations. Because it discusses all levels of membership, abstinence, and non-membership with multidimensional variables, the CSFSRs have a detailed structure. The CSFSR-based modelling tools developed in this research, which primarily rely on the score function, can be used to choose the best Web browser. The transaction could be as easy as users sharing records via a functional web browser. Finally, the advantages of this suggested structure are illustrated by contrasting it with alternative structures.

Role of dietary fiber and lifestyle modification in gut health and sleep quality

Dietary fiber has an immense role in the gut microbiome by modulating juvenile growth, immune system maturation, glucose, and lipid metabolism. Lifestyle changes might disrupt gut microbiota symbiosis, leading to various chronic diseases with underlying inflammatory conditions, obesity, and its associated pathologies. An interventional study of 16 weeks examined the impact of psyllium husk fiber with and without lifestyle modification on gut health and sleep quality in people with central obesity (men = 60 and women = 60), those aged from 40 to 60 years, those having WC ≥ 90 cm (men) and WC ≥ 80 cm (women), and no history of any chronic disease or regular medication. The participants were subgrouped into three intervention groups, namely, the psyllium husk fiber (PSH) group, the lifestyle modification (LSM) group, and the LSM&PSH group and control group with equal gender bifurcation (men = 15 and women = 15). A 24-h dietary recall, gastrointestinal tract (GIT) symptoms, and sleep quality analysis data were collected on validated questionnaires. The analyses of variance and covariance were used for baseline and post-intervention, respectively. Student's t-test was applied for pre- and post-intervention changes on the variable of interest. The intervention effect on GIT health was highly significant (P < 0.001). The mean GIT scores of the LSM, PSH, and LSM&PSH groups were 2.99 ± 0.14, 2.49 ± 0.14, and 2.71 ± 0.14, respectively, compared to the mean GIT scores of the control group. No significant (P = 0.205) effect of either intervention was observed on sleep quality. The study concluded that psyllium husk fiber significantly improved the GIT symptoms, while no significant effect of the intervention was observed on sleep quality analysis.

Dynamics of the time-fractional reaction-diffusion coupled equations in biological and chemical processes

This paper aims to demonstrate a numerical strategy via finite difference formulations for time fractional reaction-diffusion models which are ubiquitous in chemical and biological phenomena. The time-fractional derivative is considered in the Caputo sense for both linear and nonlinear problems. First, the Caputo derivative is replaced with a quadrature formula, then an implicit method is used for the remaining part. In the linear case, the proposed strategy reduces the time fractional models into linear simultaneous equations. In nonlinear cases, Quasilinearization is utilized to tackle the nonlinear parts. With this strategy, solutions of the fractional system transform into linear algebraic systems which are easy to solve. Next, the Von Neumann method is implemented to examine the stability of the scheme which discloses that the scheme is unconditionally stable. Further, the applicability of the presented scheme is tested with different linear and nonlinear models which include the one dimensional Schnakenberg and Gray-Scott models, and one and two dimensional Brusselator models. To analyze the accuracy of the present technique two norms namely, L ∞ and L 2 , and relative error are addressed. Moreover, the obtained outcomes are shown tabulated and graphically which identifies that the scheme properly works for the time fractional reaction-diffusion systems.

Investigation of surface hardness, thermostability, tribo-corrosion, and microstructural morphological properties of microwave-synthesized high entropy alloy FeCoNiMnCu coating claddings on steel

Deposition of high entropy alloy FeCoNiMnCu on SS-304 was carried out by microwave energy for application in "solid oxide fuel-cell (SOFC) interconnects". The ball-milling has been performed by taking "Fe, Co, Ni, Mn, and Cu" in equal 20 wt. % of before deposited on SS-304 substrate. The deposited steel with 20% Fe 20% Co 20% Ni 20% Mn 20% Cu high entropy alloy (HEA) was exposed to thermal-exposure in the air for up to 10 weeks at 800 °C. The uniform cladding distribution of 20% Fe 20% Co 20% Ni 20% Mn 20% Cu HEA particles can be apparently observed on SS-304 substrate by utilizing Scanning Electron Microscope (SEM), and Optical microscopy analysis. Homogeneity in the interfacial layer was evident by employing Scanning Electron Microscope (SEM) characterization. Results have indicated that after the thermal exposure of deposited steel with 20% Fe 20% Co 20% Ni 20% Mn 20% Cu in the air for up to ten weeks at 800 °C, a "protective Cr2O3 layer", and "high-entropy spinel coating" of (Fe, Co, Ni, Mn, Cu)3O4 have been formed. During microwave cladding, the emergence of harder-phases has contributed to the raised hardness. The wear behavior after coating of 20% Fe 20% Co 20% Ni 20% Mn 20% Cu HEA on SS-304 substrate has significantly enhanced due to the strengthened wear resistance and hardness of the coatings. Findings have exhibited that the formation of (Fe, Co, Ni, Mn, Cu)3O4 phase is a potential coating material for "SOFC interconnects" applications. Moreover, the cladding of SS304 with a composition of 20% Fe, 20% Co, 20% Ni, 20% Mn, and 20% Cu has demonstrated remarkable stability under thermal expansion studies. As the findings have revealed that the composite cladding has efficiently withstand significant variations in volume when subjected to elevated temperatures for a prolonged period of time, thus, exhibiting its superior thermal stability for SOFC-interconnect applications. Furthermore, the SEM images of the cladding surface, surface hardness, and tribocorrosion behavior of the coated material have been observed to identify the 20% Fe 20% Co 20% Ni 20% Mn 20% Cu HEA coating effect on SS-304 steel-substrate.

Enhancing tribo-mechanical, microstructural morphology, and corrosion performance of AZ91D-magnesium composites through the synergistic reinforcements of silicon nitride and waste glass powder

The present investigation has employed recycled waste glass powder (WGP) and silicon nitride (Si3N4) as reinforcing-agents within AZ91D-matrix composites. The composites were fabricated by employing the vacuum stir casting technique to mitigate the effects of oxidation and to ensure homogeneity, uniformity, and superior wettability among the AZ91D-matrix and reinforcements. A microscopic study provided confirmation of a uniform dispersion of WGP and Si3N4 particles throughout the AZ91D-matrix. The tensile strength of the AZ91D/WGP/Si3N4 composites rise with the inclusion of WGP particulates by up to 1.5 percent in AZ91D/7.5% Si3N4. However, the tensile strength of the AZ91D/9%Si3N4 composite have showed maximum value as compared to other chosen formulations/combinations in the current investigation. The tensile strength of AZ91D/1.5% WGP/7.5% Si3N4 composites has strengthened up to 12.13 percent with the comparison of base alloy AZ91D-matrix. In A1 formulated composite, the amount of WGP particulate has enhanced the hardness of the AZ91D-alloy by up to 1.5 percent. Findings, nevertheless has exhibited that the A6 formulated composite had superior outcomes in terms of hardness. The incorporation of "reinforcing-constituent particulates" with 1.5%WGP + 7.5%Si3N4 combination within the AZ91D-matrix, has further increased fatigue-strength by around 57.84 percent. A weight-loss of 0.312 mg was being unveiled for the A1 formulated fabricated composite. The weight-loss for the A6 formulated fabricated composite, however, was reported to be 0.294 mg. At 5 N loads, 2 m/s sliding speed, and 1000 m of sliding distance, the developed 1.5%WGP/7.5%Si3N4/AZ91D composites was reported to have a rate of wear, and frictional coefficient of 0.0025 mm3/m and 0.315, respectively. The investigation employing scanning electron microscopy (SEM) identified the presence of corrosion pits on the surfaces that had undergone corrosion. These pits were found to be a result of localised surface assaults occurring in corrosive environments. Additionally, SEM pictures of the worn surfaces indicated the emergence of microcracks, which may be associated to the conditions of cyclic loading. Moreover, the tensile-fractography examination for the developed 1.5%WGP/7.5%Si3N4/AZ91D composites has exhibited the brittle fracture failure, including cracks and debonding phenomena. In addition, the EDS spectra-analysis have revealed an apparent existence of the observed Mg-peak, Si-peak, Al-peak, Ca-peak, and O-peak for the 1.5%WGP/7.5%Si3N4/AZ91D composites. Furthermore, the utilisation of X-ray diffraction analysis effectively determined the existence of hard phases inside the AZ91D-matrix, which significantly contributed to the reported enhancement in wear resistance. The development of harder-phases has included, α-Mg, Al12Mg17, SiO2, Si3N4, MgO, and CaO phases within the composite has been accountable for the enhancement of the tribomechanical, and wear-resistance characteristics of the AZ91D/WGP/Si3N4 composites. The Si3N4 has been discovered to have a substantial impact on enhancing mechanical performance and raising the resistance to wear.

Memory type Max-EWMA control chart for the Weibull process under the Bayesian theory

The simultaneous monitoring of both process mean and dispersion, particularly in normal processes, has garnered significant attention within the field. In this article, we present a new Bayesian Max-EWMA control chart that is intended to track a non-normal process mean and dispersion simultaneously. This is accomplished through the utilization of the inverse response function, especially in cases where the procedure follows a Weibull distribution. We used the average run length (ARL) and the standard deviation of run length (SDRL) to assess the efficacy of our suggested control chart. Next, we contrast our suggested control chart's performance with an already-existing Max-EWMA control chart. Our results show that compared to the control chart under consideration, the proposed control chart exhibits a higher degree of sensitivity. Finally, we present a useful case study centered around the hard-bake process in the semiconductor manufacturing sector to demonstrate the performance of our Bayesian Max-EWMA control chart under different Loss Functions (LFs) for a Weibull process. The case study highlights how flexible the chart is to various situations. Our results offer strong proof of the outstanding ability of the Bayesian Max-EWMA control chart to quickly identify out-of-control signals during the hard-bake procedure. This in turn significantly contributes to the enhancement of process monitoring and quality control.

Utilization of discarded face masks in combination with recycled concrete aggregate and silica fume for sustainable civil construction projects

The coronavirus (COVID-19) pandemic has not only had a severe impact on global health but also poses a threat to the environment. This research aims to explore an innovative approach to address the issue of increased waste generated by the pandemic. Specifically, the study investigates the utilization of discarded face masks in combination with recycled concrete aggregate (RCA) and Silica Fume (SFM) in civil construction projects. The disposable face masks were processed by removing the ear loops and nose strips, and then cutting them into small fibers measuring 20 mm in length, 5 mm in width, and 0.46 mm in thickness, resulting in an aspect ratio of 24. Various proportions of SFM and RCA were incorporated into the concrete mix, with a focus on evaluating the compressive strength, split tensile strength, and durability of the resulting material. The findings indicate that the addition of SFM led to improvements in both compressive and split tensile strength, while no significant impact on durability was observed.

Magnetohydrodynamics and viscosity variation in couple stress squeeze film lubrication between rough flat and curved circular plates

A simplified mathematical model has been developed for understanding combined effects of surface roughness, viscosity variation and couple stresses on the squeeze film behaviour of a flat and a curved circular plate in the presence of transverse magnetic field. The Stokes (1966) couple stress fluid model is included to account for the couple stresses arising due to the presence of microstructure additives in the lubricant. In the context of Christensen's (1969) stochastic theory for the lubrication of rough surfaces, two types of one-dimensional roughness patterns (radial and azimuthal) are considered. The governing modified stochastic Reynolds type equations are derived for these roughness patterns. Expressions for the mean squeeze film characteristics are obtained. Numerical computations of the results show that the azimuthal roughness pattern on the curved circular and flat plate results in more pressure buildup whereas performance of the squeeze film suffers due to the radial roughness pattern. Further the Lorentz force characterized by the Hartmann number, couple stress parameter and viscosity variation parameter improve the performance of the squeeze film lubrication as compared to the classical case (Non-magnetic, Newtonian case and non-viscous case).

AI and machine learning for soil analysis: an assessment of sustainable agricultural practices

Sustainable agricultural practices help to manage and use natural resources efficiently. Due to global climate and geospatial land design, soil texture, soil-water content (SWC), and other parameters vary greatly; thus, real time, robust, and accurate soil analytical measurements are difficult to be developed. Conventional statistical analysis tools take longer to analyze and interpret data, which may have delayed a crucial decision. Therefore, this review paper is presented to develop the researcher's insight toward robust, accurate, and quick soil analysis using artificial intelligence (AI), deep learning (DL), and machine learning (ML) platforms to attain robustness in SWC and soil texture analysis. Machine learning algorithms, such as random forests, support vector machines, and neural networks, can be employed to develop predictive models based on available soil data and auxiliary environmental variables. Geostatistical techniques, including kriging and co-kriging, help interpolate and extrapolate soil property values to unsampled locations, improving the spatial representation of the data set. The false positivity in SWC results and bugs in advanced detection techniques are also evaluated, which may lead to wrong agricultural practices. Moreover, the advantages of AI data processing over general statistical analysis for robust and noise-free results have also been discussed in light of smart irrigation technologies. Conclusively, the conventional statistical tools for SWCs and soil texture analysis are not enough to practice and manage ergonomic land management. The broader geospatial non-numeric data are more suitable for AI processing that may soon help soil scientists develop a global SWC database.

Impact of chemical reaction on Eyring-Powell fluid flow over a thin needle with nonlinear thermal radiation

The thin needle is viewed as a revolutionary object since it has a thinner thickness than a boundary layer. As a consequence, scientific and engineering applications for instance electrical equipment, hot wire anemometers and geothermal power generation are significantly impacted by the flow deformed by a thin moving needle. MHD Eyring-Powell fluid flow over a thin needle perceiving heat source, chemical reaction and nonlinear thermal radiation is the subject of the current investigation. In addition, the present study utilizes the Buongiorno model to examine the special effects of the fluid's Brownian and thermophoretic forces. The solution of the dimensionless form of ODEs is produced by applying exact renovations to the given problem, which is determined by the structure of PDEs. The bvp4c algorithm, based on the finite difference approach is utilized to numerically solve such modified ODEs. For validation, the results obtained indicate good agreement when compared to the literature. Finally, a detailed graphical analysis of key parameters is shown and explained while keeping in mind the physical significance of flow parameters. The results show that as magnetic and fluid parameter values improve, the velocity gradient falls. Increasing heat source and radiation parameters optimises heat transfer rate. The augmentation of the Lewis number and chemical reaction accelerates the rate of mass transfer on the surface. Brownian motion and thermophoresis provide enhanced thermal performance for the fluid temperature. Growing the thermophoresis parameter from 0.1 to 0.3 upsurges the Nusselt number by 5.47% and the Sherwood number by 12.26%.

An efficient framework for obtaining the initial cluster centers

Clustering is an important tool for data mining since it can determine key patterns without any prior supervisory information. The initial selection of cluster centers plays a key role in the ultimate effect of clustering. More often researchers adopt the random approach for this purpose in an urge to get the centers in no time for speeding up their model. However, by doing this they sacrifice the true essence of subgroup formation and in numerous occasions ends up in achieving malicious clustering. Due to this reason we were inclined towards suggesting a qualitative approach for obtaining the initial cluster centers and also focused on attaining the well-separated clusters. Our initial contributions were an alteration to the classical K-Means algorithm in an attempt to obtain the near-optimal cluster centers. Few fresh approaches were earlier suggested by us namely, far efficient K-means (FEKM), modified center K-means (MCKM) and modified FEKM using Quickhull (MFQ) which resulted in producing the factual centers leading to excellent clusters formation. K-means, which randomly selects the centers, seem to meet its convergence slightly earlier than these methods, which is the latter's only weakness. An incessant study was continued in this regard to minimize the computational efficiency of our methods and we came up with farthest leap center selection (FLCS). All these methods were thoroughly analyzed by considering the clustering effectiveness, correctness, homogeneity, completeness, complexity and their actual execution time of convergence. For this reason performance indices like Dunn's Index, Davies-Bouldin's Index, and silhouette coefficient were used, for correctness Rand measure was used, for homogeneity and completeness V-measure was used. Experimental results on versatile real world datasets, taken from UCI repository, suggested that both FEKM and FLCS obtain well-separated centers while the later converges earlier.

Monitoring the process mean under the Bayesian approach with application to hard bake process

This study introduces the Bayesian adaptive exponentially weighted moving average (AEWMA) control chart within the framework of measurement error, examining two separate loss functions: the squared error loss function and the linex loss function. We conduct an analysis of the posterior and posterior predictive distributions utilizing a conjugate prior. In the presence of measurement error (ME), we employ a linear covariate model to assess the control chart's effectiveness. Additionally, we explore the impacts of measurement error by investigating multiple measurements and a method involving linearly increasing variance. We conduct a Monte Carlo simulation study to assess the control chart's performance under ME, examining its run length profile. Subsequently, we offer a specific numerical instance related to the hard-bake process in semiconductor manufacturing, serving to verify the functionality and practical application of the suggested Bayesian AEWMA control chart when confronted with ME.

Rheological study of Hall current and slip boundary conditions on fluid-nanoparticle phases in a convergent channel

Purpose: the purpose of this theoretical study was to analyze the heat transfer in the fluid-particle suspension model under the effects of a porous medium, magnetic field, Hall effects, and slip boundary conditions in a convergent channel with the addition of electrokinetic phenomena. The Darcy-Brinkman (non-Darcy porous medium) model was used to assess the effects of the porous medium. Methodology: the rheological equations of both models were transformed into a dimensionless form to obtain the exact solutions of the fluid and particle phase velocities, pressure gradient, volumetric flow rate, stream function, temperature distribution, and heat-transfer rate. To obtain an exact solution to the models, the physical aspects of the parameters are discussed, analyzed, and reported through graphs, contour plots, and in tabular form. Findings: mixing in hafnium particles in a viscous fluid provide 1.2% more cooling compared to with a regular fluid. A reduction of the streamlines was observed with the contribution of the slip condition. The utilization of the Darcy parameters upgraded both the fluid flow and temperature profiles, while the heat-transfer rate decreased by up to 3.3% and 1.7% with the addition of a magnetic field and porous medium, respectively. Originality: the current study is an original work of the authors and has not been submitted nor published elsewhere.

Exploring the relationship between air quality index and lung cancer mortality in India: predictive modeling and impact assessment

The Air Quality Index (AQI) in India is steadily deteriorating, leading to a rise in the mortality rate due to Lung Cancer. This decline in air quality can be attributed to various factors such as PM 2.5, PM 10, and Ozone (O3). To establish a relationship between AQI and Lung Cancer, several predictive models including Linear Regression, KNN, Decision Tree, ANN, Random Forest Regression, and XGBoost Regression were employed to estimate pollutant levels and Air Quality Index in India. The models relied on publicly available state-wise Air Pollution Dataset. Among all the models, the XGBoost Regression displayed the highest accuracy, with pollutant level estimations reaching an accuracy range of 81% to 98% during training and testing. The second-highest accuracy range was achieved by Random Forest. The paper also explores the impact of increasing pollution levels on the rising mortality rate among lung cancer patients in India.

Bayesian AEWMA control chart under ranked set sampling with application to reliability engineering

The article introduces a novel Bayesian AEWMA Control Chart that integrates different loss functions (LFs) like the square error loss function and Linex loss function under an informative prior for posterior and posterior predictive distributions, implemented across diverse ranked set sampling (RSS) designs. The main objective is to detect small to moderate shifts in the process mean, with the average run length and standard deviation of run length serving as performance measures. The study employs a hard bake process in semiconductor production to demonstrate the effectiveness of the proposed chart, comparing it with existing control charts through Monte Carlo simulations. The results underscore the superiority of the proposed approach, particularly under RSS designs compared to simple random sampling (SRS), in identifying out-of-control signals. Overall, this study contributes a comprehensive method integrating various LFs and RSS schemes, offering a more precise and efficient approach for detecting shifts in the process mean. Real-world applications highlight the heightened sensitivity of the suggested chart in identifying out-of-control signals compared to existing Bayesian charts using SRS.

Empirical models for compressive and tensile strength of basalt fiber reinforced concrete

When molten magma solidifies, basalt fiber (BF) is produced as a byproduct. Due to its remaining pollutants that could affect the environment, it is regarded as a waste product. To determine the compressive strength (CS) and tensile strength (TS) of basalt fiber reinforced concrete (BFRC), this study will develop empirical models using gene expression programming (GEP), Artificial Neural Network (ANN) and Extreme Gradient Boosting (XG Boost). A thorough search of the literature was done to compile a variety of information on the CS and TS of BFRC. 153 CS findings and 127 TS outcomes were included in the review. The water-to-cement, BF, fiber length (FL), and coarse aggregates ratios were the influential characteristics found. The outcomes showed that GEP can accurately forecast the CS and TS of BFRC as compared to ANN and XG Boost. Efficiency of GEP was validated by comparing Regression (R2) value of all three models. It was shown that the CS and TS of BFRC increased initially up to a certain limit and then started decreasing as the BF % and FL increased. The ideal BF content for industrial-scale BF reinforcement of concrete was investigated in this study which could be an economical solution for production of BFRC on industrial scale.

A Weibull process monitoring with AEWMA control chart: an application to breaking strength of the fibrous composite

In recent times, there has been a growing focus among researchers on memory-based control charts. The Exponentially Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) charts and the adaptive control charting approaches got the attention. Control charts are commonly employed to oversee processes, assuming the monitored variable follows a normal distribution. However, it's worth noting that this assumption does not hold true in many real-world situations. The use of the algebraic expression for normalization, which can be used for all kinds of skewed distributions with a closed-form distribution function, using the proposed continuous function to adapt a smoothing constant, motivates this study. In the present manuscript, we design an EWMA statistic-based adaptive control chart to monitor the irregular variations in the mean of two parametric Weibull distribution and use Hasting approximation for normalization. The adaptive control charts are used to update the smoothing constant according to the estimated shift. Here we use the proposed continuous function to adapt the smoothing constant. The average run length and standard deviation of run length are calculated under different parameter settings. The effectiveness of the proposed chart is argued in terms of ARLs over the considered EWMA chart through Monte-Carlo (MC) simulation method. The proposed chart is examined, followed by a real data set to demonstrate the design and application procedures.

Predictors of mental health problems during the COVID-19 outbreak in Egypt in 2021

Background

With the widespread outbreak of the coronavirus (COVID-19) pandemic, many countries, including Egypt, have tried to restrict the virus by applying social distancing and precautionary measures. Understanding the impact of COVID-19-induced risks and social distancing measures on individuals' mental health will help mitigate the negative effects of crises by developing appropriate mental health services. This study aimed to investigate the most contributing factors that affected individuals' mental health and how individuals' mental health has changed over the lockdown period in Egypt in 2021.

Methods

The study draws on a nationally representative sample from the combined COVID-19 MENA Monitor Household Survey conducted by the Economic Research Forum. The data were collected in Egypt by phone over two waves in February 2021 and June 2021. The total number of respondents is 4,007 individuals. The target population is mobile phone owners aged 18-64 years. The 5-item World Health Organization Well-Being Index (WHO-5) is used to assess the individuals' mental health over the past 2 weeks during the pandemic. Penalized models (ridge and LASSO regressions) are used to identify the key drivers of mental health status during the COVID-19 pandemic.

Results

The mean value of mental health (MH) scores is 10.06 (95% CI: 9.90-10.23). The average MH score for men was significantly higher than for women by 0.87. Rural residents also had significantly higher MH scores than their urban counterparts (10.25 vs. 9.85). Middle-aged adults, the unemployed, and respondents in low-income households experienced the lowest MH scores (9.83, 9.29, and 9.23, respectively). Individuals' mental health has deteriorated due to the negative impacts of the COVID-19 pandemic. Regression analysis demonstrated that experiencing food insecurity and a decrease in household income were independent influencing factors for individuals' mental health (p < 0.001). Furthermore, anxiety about economic status and worrying about contracting the virus had greater negative impacts on mental health scores (p < 0.001). In addition, women, middle-aged adults, urban residents, and those belonging to low-income households were at increased risk of poor mental health (p < 0.05).

Conclusion

The findings reveal the importance of providing mental health services to support these vulnerable groups during crises and activating social protection policies to protect their food security, incomes, and livelihoods. A gendered policy response to the pandemic is also required to address the mental pressures incurred by women.

Fault tolerant trust based task scheduler using Harris Hawks optimization and deep reinforcement learning in multi cloud environment

Cloud Computing model provides on demand delivery of seamless services to customers around the world yet single point of failures occurs in cloud model due to improper assignment of tasks to precise virtual machines which leads to increase in rate of failures which effects SLA based trust parameters (Availability, success rate, turnaround efficiency) upon which impacts trust on cloud provider. In this paper, we proposed a task scheduling algorithm which captures priorities of all tasks, virtual resources from task manager which comes onto cloud application console are fed to task scheduler which takes scheduling decisions based on hybridization of both Harris hawk optimization and ML based reinforcement algorithms to enhance the scheduling process. Task scheduling in this research performed in two phases i.e. Task selection and task mapping phases. In task selection phase, all incoming priorities of tasks, VMs are captured and generates schedules using Harris hawks optimization. In task mapping phase, generated schedules are optimized using a DQN model which is based on deep reinforcement learning. In this research, we used multi cloud environment to tackle availability of VMs if there is an increase in upcoming tasks dynamically and migrate tasks to one cloud to another to mitigate migration time. Extensive simulations are conducted in Cloudsim and workload generated by fabricated datasets and realtime synthetic workloads from NASA, HPC2N are used to check efficacy of our proposed scheduler (FTTHDRL). It compared against existing task schedulers i.e. MOABCQ, RATS-HM, AINN-BPSO approaches and our proposed FTTHDRL outperforms existing mechanisms by minimizing rate of failures, resource cost, improved SLA based trust parameters.

Numerical assessment of irreversibility in radiated Sutterby nanofluid flow with activation energy and Darcy Forchheimer

Entropy generation is a concept that is primarily associated with thermodynamics and engineering, and it plays a crucial role in understanding and optimizing various processes and systems. Applications of entropy generation can be seen in turbo machinery, reactors, chillers, desert coolers, vehicle engines, air conditioners, heat transfer devices and combustion. Due to industrial applications entropy generation has gained attention of researchers. Owing such applications, current communication aims to model and analyzed the irreversibility in Sutterby nanoliquid flow by stretched cylinder. Momentum equation is reported by considering porosity, Darcy Forchheimer and magnetic field. While in energy equation radiation and Joule heating effects are accounted. Activation energy impact is accounted in the modeling of concentration equation. Thermodynamics second law is utilized for physical description of irreversibility analysis. Through similarity transformations dimensional equations representing flow are transformed to dimensionless ones. Numerical solution for ordinary system is obtained via Runge-Kutta-Fehlberg scheme in Mathematica platform through NDsolve code. Influence of prominent variables on velocity, entropy, temperature, Bejan number and concentration are graphically analyzed. Coefficient of skin friction, gradient of temperature and Sherwood number are numerically analyzed. The obtained results show that velocity field decreases through higher porosity and Forchheimer variables. Velocity and temperature curves shows an opposite trend versus magnetic parameter. A decay in concentration distribution is noticed through larger Schmidt number. Entropy generation amplifies against magnetic parameter and Brinkman number.

Enhancing medical ultrasound imaging through fractional mathematical modeling of ultrasound bubble dynamics

The classical mathematical modeling of ultrasound acoustic bubble is so far using to improve the medical imaging quality. A clear and visible medical ultrasound image relies on bubble's diameter, wavelength and intensity of the scattered sound. A bubble with diameter much smaller than the sound wavelength is regarded as highly efficient source of sound scattering. The dynamical equation for a medical ultrasound bubble is primarily modeled in classical integer-order differential equation. Then a reduction of order technique is used to convert the modeled dynamic equation for the bubble surface into a system of incommensurate fractional-orders. The incommensurate fractional-order values are calculated directly, by using Riemann stability region. On the basis of stability the convergence and accuracy of the numerical scheme is also discussed in detail. It has been found that the system will remain stable and chaotic for the incommensurate values α1<0.737 and α2<2.80, respectively.

Monitoring of manufacturing process using bayesian EWMA control chart under ranked based sampling designs

Control charts, including exponentially moving average (EWMA) , are valuable for efficiently detecting small to moderate shifts. This study introduces a Bayesian EWMA control chart that employs ranked set sampling (RSS) with known prior information and two distinct loss functions (LFs), the Square Error Loss function (SELF) and the Linex Loss function (LLF), for posterior and posterior predictive distributions. The chart's performance is assessed using average run length (ARL) and standard deviation of run length (SDRL) profiles, and it is compared to the Bayesian EWMA control chart based on simple random sampling (SRS). The results indicate that the proposed control chart detects small to moderate shifts more effectively. The application in semiconductor manufacturing provides concrete evidence that the Bayesian EWMA control chart, when implemented with RSS schemes, demonstrates a higher degree of sensitivity in detecting deviations from normal process behavior. Comparison to the Bayesian EWMA control chart using SRS, it exhibits a superior ability to identify and flag instances where the manufacturing process is going out of control. This heightened sensitivity is critical for promptly addressing and rectifying issues, which ultimately contributes to improved quality control in semiconductor production.

Behavior of higher-order MDD on energy ratios at the interface of thermoelastic and piezothermoelastic mediums

This paper investigates the intricate energy distribution patterns emerging at an orthotropic piezothermoelastic half-space interface by considering the influence of a higher-order three-phase lags heat conduction law, accompanied by memory-dependent derivatives (referred to as HPS) within the underlying thermoelastic half-space (referred to as TS). This study explores the amplitude and energy ratios of reflected and transmitted waves. These waves span various incident types, including longitudinal, thermal, and transversal, as they propagate through the TS and interact at the interface. Upon encountering the interface, an intriguing dynamic unfolds: three waves experience reflection within the TS medium, while four waves undergo transmission into the HPS medium. A graphical representation effectively illustrates the impact of higher-order time differential parameters and memory to offer comprehensive insights. This visual representation reveals the nuanced fluctuations of energy ratios with the incidence angle. The model astutely captures diverse scenarios, showcasing its ability to interpret complex interface dynamics.

Classification of Parkinson's Disease in Patch-Based MRI of Substantia Nigra

Parkinson's disease (PD) is a chronic and progressive neurological disease that mostly shakes and compromises the motor system of the human brain. Patients with PD can face resting tremors, loss of balance, bradykinesia, and rigidity problems. Complex patterns of PD, i.e., with relevance to other neurological diseases and minor changes in brain structure, make the diagnosis of this disease a challenge and cause inaccuracy of about 25% in the diagnostics. The research community utilizes different machine learning techniques for diagnosis using handcrafted features. This paper proposes a computer-aided diagnostic system using a convolutional neural network (CNN) to diagnose PD. CNN is one of the most suitable models to extract and learn the essential features of a problem. The dataset is obtained from Parkinson's Progression Markers Initiative (PPMI), which provides different datasets (benchmarks), such as T2-weighted MRI for PD and other healthy controls (HC). The mid slices are collected from each MRI. Further, these slices are registered for alignment. Since the PD can be found in substantia nigra (i.e., the midbrain), the midbrain region of the registered T2-weighted MRI slice is selected using the freehand region of interest technique with a 33 × 33 sized window. Several experiments have been carried out to ensure the validity of the CNN. The standard measures, such as accuracy, sensitivity, specificity, and area under the curve, are used to evaluate the proposed system. The evaluation results show that CNN provides better accuracy than machine learning techniques, such as naive Bayes, decision tree, support vector machine, and artificial neural network.