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Ferrofluids are colloidal suspensions made of nanoscale ferromagnetic particles suspended in a base fluid. It has various medical applications like cell separation, drug targeting, magnetic resonance imaging, etc. Due to the wide range of uses for ferrofluids, this investigation looked into the way that how the fluid flows and transfers heat in the presence of thermal radiation, a porous medium, transverse magnetic field, and heat sink or source impacts. The current study examines a unique corner-heated enclosure problem with a quadratic Boussinesq approximation fluid flow and heat transfer under natural convection circumstances. The square cavity of length ( L ) is considered with hot and cold slits at the opposite–opposite corners, and it is filled with cobalt ferrite CoFe 2 O 4 –water H 2 O ferrofluid. Middle portion of the all the walls is adiabatic. The implications of magnetohydrodynamic are examined employing CoFe 2 O 4 nanoparticles. The Marker-And-Cell method with finite difference technique is employed for the solution of the dimensionless constitutive equations. In this regard, physics of the problem is well explored for the various parameters such as Rayleigh number (Ra), Darcy number (Da), Hartmann numbers (Ha), solid volume fractions ( φ ) , thermal radiation parameter (Rd), nonlinear Boussinesq approximation parameter ( δ ) , and inclination angle of the cavity ( ω ) and are graphically represented. Suspending the CoFe 2 O 4 ferroparticles in the base liquid water increases heat transmission by 14.5 % . A heat source or sink enhances the fluid velocity and boosts heat transmission by 91.93 % .

VANETs exchange data in highly dynamic open wireless access environments which are prone to security and privacy attacks. In order to safeguard the transmitted data, group key agreement authentication (GKAA) technique is used. Utilization of group key allows entities to corroborate a group key for secure VANET communication in an unsecure wireless communication channels. The traditional GKAA consumes a considerable amount of resources, verification delay is very high. Since the group key is computed and administered solely by TTA, it leads to central tendency. Additionally the communication delay soars high. To alleviate the problems of computational cost, communication cost, security, conditional privacy, central tendency, a Dickson polynomial based conditional privacy preservation authentication based on group key authentication (GKA) for VANETs has been proposed. The proposed work involves the use of Dickson polynomial to improve the security strength of TTA while authentication vehicles. Since it is based on chaotic mapping algorithm, wherein the chaotic map provides a one-way hash function; Dickson polynomial is used to corroborate a publicly distributed group key; it alleviates the complex modular or scalar multiplication performed using Elliptic curves. The group key gets computed in a distributed fashion by using the Chinese Remainder Theorem (CRT) and gets updated dynamically without the aid of TTA. Conditional Privacy has been ensured by the tracing back the pseudonyms in case of any illicit behavior exhibited by the vehicles. The proposed scheme is lightweight and lowers the communication, computation cost involved during authentication and verification. Performance analysis has been carried out by using BAN logic and ROR model thereby ensuring the security and efficiency. Thus the proposed authentication technique outperforms the traditional certificate-less and group key authentication schemes in terms of improvement in computation cost of 39%, communication cost of 672 bits for a single message with a less verification delay.

The current analysis executes a numerical exploration of Magnetohydrodynamic (MHD) free convective heat transmission in a square enclosure filled with copper–water nanofluid by considering the effect of heat absorption/generation. The left and right walls are kept as adiabatic, the top wall is presumed to be hot, and the bottom wall is adiabatic which has a cold slit in the center. Using a two-dimensional Navier–Stokes equation in Cartesian form, the present analysis is theoretically modeled. To solve the governing constitutive equations in non-dimensional form, the Marker and Cell (MAC) approach is used. The MAC method employs a staggered grid where velocity components are stored at the cell faces, and pressure is stored at the cell centers. This arrangement helps in accurately enforcing the incompressibility condition (i.e., the divergence-free condition of the velocity field). The impact of the Rayleigh number, Hartmann number, heat absorption/generation coefficient, and nanoparticle volume fraction are examined to explore the features of free convective heat transmission within the enclosure. Results of these parameters have been graphically visualized by means of streamlines, isotherms contours, as well as local and mean Nusselt numbers. According to findings, the performance of heat transmission within the cavity is substantially influenced by the variations in the magnetic field’s strength, the volume fraction of Cu-nanoparticles, and heat absorption/generation. The average rate of heat transmission within the enclosure can be magnified by replacing the internal heat absorption with internal heat generation. The suspension of 5% Cu nanoparticles into water boosts the mean rate of heat transmission of water by 19.314%.

A numerical investigation of hydromagnetic mixed convective heat transfer and fluid flow in a porous rectangular enclosure filled with water and kerosene-based ferro-nanofluids is presented in this study. This research incorporates nanoscale ferromagnetic cobalt particles. The right wall is adiabatic, the top and bottom walls are cold, and a hot slit is positioned in the centre of the left adiabatic wall. The dimensionless governing equations are numerically solved using the Marker-And-Cell (MAC) technique. The effects of uniform inclined magnetic field, inclination angle of the cavity, and internal heat generation/absorption are investigated. The effects of various relevant parameters such as Richardson number ( Ri ), Reynolds number ( Re ), Darcy number ( Da ), Hartmann number ( Ha ), internal heat generation/absorption coefficient ( Q ), magnetic field’s inclination angle ( ∅ ) and cavity’s inclination angle ( ω ) on the streamlines, isotherms, and local and average heat transfer rates have been graphically displayed. Spherical and non-spherical nanoparticles such as blades, platelets, cylinders, and bricks are dispersed in base fluids to study the fluid flow and heat transfer inside the enclosure, and the findings are visualized. Heat transmission improves with porous medium permeability, nanoparticles’ volume fraction, magnetic flux, and heat absorption/generation impacts. Compared to water, kerosene improves the mean heat transfer rate by 71 % when 5 % cobalt ferro-nanoparticles are added to the base fluid. Compared to the cobalt-water ferro-nanofluid which is prepared by suspending the spherical-shaped cobalt nanoparticles, the ferro-nanofluid prepared by suspending the blade-shaped cobalt nanoparticles increase the mean heat transmission rate by 16.47 % . Graphical abstract

Triphala is a traditional Ayurvedic herbal formulation composed of three fruits: amla ( Phyllanthus emblica ), bibhitaki ( Terminalia bellerica ), and haritaki ( Terminalia chebula ). Triphala is a potent Ayurvedic remedy that promotes digestion, detoxification, and overall wellness, while also providing antioxidant benefits through its trio of nutrient-rich fruits. In order to elucidate the individual contributions of the three ingredients of Triphala from molecular perspective, the individual ingredients were used for the untargeted LCMS/MS analysis. Fresh fruits (PE, TC, and TB) were collected, processed into coarse powders, and sequentially extracted {hexane, chloroform, and ethyl acetate}. LCMS/MS data analysis was performed on the resultant metabolites, with bioinformatics tools employed for pathway enrichment, target prediction, and classification of identified compounds. Additionally, polyphenols were identified as key compounds with potential health benefits. LCMS analysis of the individual extracts identified a total of 10227 features, resulting in 2515 annotated metabolites, with PE contributing the highest number at 1286. Comparative analysis revealed 408 non-redundant metabolites, with 74.2% being unique to individual fruits, underscoring the complementary phytochemical profiles. Pathway enrichment analysis highlighted dominant phenylpropanoid biosynthesis pathways across all extracts, while a comprehensive polyphenol classification identified 71 polyphenols, with significant interactions predicted between polyphenols and gut microbiota. Additionally, five common polyphenols showed potential human targets related to antioxidant activity. These findings provide a deeper understanding of the phytochemical diversity and potential health benefits of Triphala, supporting its traditional use in promoting health.

The growing need for sustainable energy sources and stricter environmental regulations necessitate the development of alternative fuels with lower emissions and improved performance. This study addresses these challenges by optimizing the performance and emission characteristics of a single-cylinder diesel engine powered by neem oil biodiesel blends enhanced with alumina nanoparticlesusing the powerful desirability-based optimization. Neem oil, a non-edible feedstock, was selected to avoid competition with food resources, while alumina nanoparticles were utilized for their catalytic properties to enhance combustion efficiency. The process involved experimental evaluation of biodiesel blends (B10, B20, and B30) combined with alumina nanoparticles at concentrations of 100 ppm, 150 ppm, and 200 ppm using a design of experiments approach. With the engine running at maximum load of 100% and an aluminum oxide concentration of 100 parts per million, the optimal fuel mix comprises of 89.85% diesel and 30% biodiesel. The lowest brake-specific fuel consumption of 0.45 kg per kilowatt-hour that the optimization produced points to effective fuel use. With a little variance of 3.33%, the brake thermal efficiency was maximized at 38.18%, quite near to the validation result of 37.89%. The alumina nanoparticles enhanced combustion through improved fuel atomization and oxidation due to their high surface area and catalytic effects. To further validate the effectiveness of RSM, the results are compared with the performance of several advance machine learning algorithms, including linear regression, decision tree, and random forest. The random forest model demonstrated the highest predictive accuracy for performance (test R 2  = 0.9620, Test MAPE = 3.6795%), making it the most reliable statistical approach for predicting BSFC compared to linear regression and decision Tree models. The random forest model also outperformed other approaches in predicting emissions, achieving the highest accuracy with a test R 2 of 0.9826 and the lowest test MAPE of 9.3067%.This integrated experimental and predictive approach provided a robust framework for optimizing biodiesel formulations, identifying the ideal combination of biodiesel blend ratio and nanoparticle concentration. The findings highlight the potential of neem oil biodiesel blends enhanced with alumina nanoparticles to achieve a sustainable balance between improved engine performance and reduced emissions in CI engines.

This study investigates the effectiveness of Abrasive Water Suspension Jet (AWSJ) Machining, a non-conventional erosion-based method, for machining carbon fiber-reinforced polymer (CFRP) composites. The focus was on analyzing key process parameters—abrasive size, feed rate, and standoff distance (SOD)—under submerged cutting conditions and their impact on material removal rate (MRR), kerf width, and surface roughness. Experimental trials were conducted, and advanced computational techniques, including Response Surface Methodology (RSM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks (ANN), were used for parameter optimization and predictive analysis. The results showed that submerged cutting significantly improved machining quality by reducing surface roughness and ensuring uniform kerf widths. Increasing the jet diameter in underwater conditions stabilized the nozzle, leading to smoother and more precise cuts. Among the predictive models, XGBoost demonstrated the highest accuracy and efficiency in forecasting MRR, while Random Forest and ANN provided competitive performance. The integration of RSM and machine learning (ML) techniques enabled effective optimization of machining parameters, showcasing the potential for cost-effective and high-precision CFRP machining. These findings are particularly relevant for industries like aerospace and automotive, where machining efficiency and precision are crucial.

Wireless sensor networks consist of many tiny sensor nodes which are deployed in various geographical locations for sensing the normal spectacles and also to transmit the collected information to the base station which is also named destination node through multiple nodes present in the network. Most of the existing heuristics algorithms used for finding the optimal routes have limitations in the provision of effective solutions for routing and clustering mechanisms in larger search spaces. Hence, when the search space increases exponentially, the chance of creating the optimal solution for clustering and routing is decreasing and ultimately an un-optimized process depletes the sensor node resources. In order to address the challenges and limitations present in the existing routing systems, two new heuristics algorithms namely gravitational approach based clustering method and a clustered gravitational routing algorithm have been proposed in this paper for providing an optimal solution for efficient clustering and effective routing. Moreover, a fuzzy logic based deductive inference system has been designed and used in this work for selecting the most appropriate nodes as cluster head nodes from the nodes present in each cluster. The simulation results obtained from this work show that the clustering accuracy and the network lifetime are increased and the energy consumption as well as delay are reduced with the application of these proposed algorithms.

This study investigates the energy absorption and damage prediction of banana fiber composite laminates under low-velocity impact using a combination of experimental testing, finite element analysis (FEA), and machine learning (ML). Banana fiber composites are a promising eco-friendly alternative to synthetic materials in structural applications due to their sustainability, high strength, and energy absorption properties. The laminates, fabricated using the hand layup technique, were subjected to low-velocity impact tests to measure their energy absorption, force-displacement behavior, and damage progression. FEA simulations were conducted to model the impact response, and ML models, including logistic regression and Naive Bayes, were developed to predict the impact behavior. The results show that banana fiber composites exhibit significant energy absorption, with an experimental value of 14.36 kJ at a drop height of 1.8 m. Both FEA and ML models closely predicted this energy absorption, with minor deviations, validating the robustness of the methodologies. The study highlights the integration of ML as a powerful tool for predicting composite material behavior, achieving an accuracy of 1.0 in predicting energy absorption and damage initiation. The findings provide valuable insights into the potential of banana fiber composites for use in lightweight, high-strength materials for the automotive and aerospace industries.