Gour Mahavidyalaya

This study investigates the flow and thermal dynamics of Casson milk enhanced with silver-magnesium oxide hybrid nanoparticles within a rapidly activated electromagnetically actuated conduit under quadratic thermal ramping and oscillatory pressure forcing. A physics-based model incorporating thermal radiation, volumetric heat absorption, and Darcy porous drag is solved analytically using the Laplace transform technique, with predictions validated by a Python-based artificial neural network (ANN). The electromagnetic conduit flow is mathematically modeled, with solutions obtained via Laplace transform analysis. Results reveal that nanoparticle inclusion significantly improves effective thermal conductivity and alters viscosity, enhancing heat transfer efficiency while modifying velocity profiles. Key parametric trends show that the modified Hartmann number amplifies flow momentum, whereas wider electrode spacing attenuates it. Increased thermal radiation reduces fluid temperature, while a larger Casson parameter abates shear stress (SS). The radiation parameter positively augments the rate of heat transfer (RHT). The developed ANN model demonstrates exceptional predictive accuracy, achieving over 99.93% agreement with analytical results across training, validation, and test datasets for both SS and RHT predictions. These findings highlight the synergistic potential of hybrid nanofluids and AI-driven modeling for optimizing thermal processing, improving energy efficiency, and advancing sustainable practices in the dairy industry.

Effective delivery of therapeutic agents to the central nervous system is severely limited by rapid cerebrospinal fluid (CSF) clearance and lack of spatial targeting, hindering treatment of neurodegenerative diseases. Electromagnetic control of nanoparticle-enhanced CSF presents a non-invasive solution, yet fundamental transport mechanisms remain uncharacterized. This work establishes a unified theoretical model for electromagnetically-actuated transport of Casson hybrid nano-CSF containing gold and maghemite nanoparticles in bio-reactor channels with realistic thermal-hydrodynamic boundary conditions, toward optimizing magnetophoretic neurotherapeutic delivery. Coupled momentum-energy equations incorporating Casson rheology, nanoparticle-enhanced properties (Maxwell-Garnett theory), electromagnetic forcing, thermal radiation (Rosseland approximation), porous resistance (Darcy model), and non-uniform heating are solved analytically via Laplace transforms, providing closed-form velocity and temperature solutions. Electromagnetic actuation increases flow velocity by 42% with maghemite nanoparticles showing superior magnetophoretic response; Casson yield stress suppresses backflow, enhancing forward transport by 27%; thermal radiation strengthens convective mixing while asymmetric heating enables targeted deposition; heat sinks reduce wall heat transfer by 35%, limiting thermal drug release. These quantitative insights provide design criteria for magnetically-guided neurotherapeutic systems capable of overcoming biological clearance barriers.

Maternal mortality and infant mortality are still highly accepted in many countries like lower- and middle-income countries which currently act as serious global health concern and which also indicate the improper use of utilization of maternal health care services, lack of proper maternal knowledge. The United Nations also gave more concern on maternal as well as newborn health on SDG-3 (Good Health and Well-being) and MDG-5 (Improve maternal health). The degree of proper utilization of maternal health care services depends on different determining factors and presence of maternal knowledge also act as an important contributing factor which help to understand the importance of MHC services for both mother and newborn baby, help to reduce the complications during pregnancy as well as during the delivery and it helps to go for healthy newborn. This study examined the role of maternal knowledge on the utilization of maternal health care services (antenatal phase, delivery phase, and postnatal phase) among the Muslim women of Maldah district of West Bengal. The entire study has been done with the help of primary survey among the 916 Muslim women aged 15–49 years old and who and at least one live birth within the 5 years old. The bivariate analysis (Phi-coefficient) and multivariate analysis (binary logistic regression) has been used to proper depiction of the result. The result shows that there is significant association between the level of maternal knowledge and the degree of utilization of maternal health care services among the women.

Missing values (MVs) have been a persistent challenge in real-world datasets. In many prior studies, MVs have been removed without considering their underlying patterns, potentially discarding important information. This study has investigated seven machine learning-based MV handling methods and compared them with approaches reported in the literature, analyzing research published between 2019 and 2024. Using stratified ten-fold cross-validation, twelve classification algorithms, including standalone and ensemble models, have been evaluated on a binary classification problem with eleven performance metrics. The results have shown that combining missing value imputation (MVI) with data balancing (DB) substantially improved model performance. Accuracy increased from 0.9095 (without MVI and DB) to 0.9964 (with both), while the meta-learning metric Pmean rose from 0.6232 to 0.9954. These findings have demonstrated the critical role of effective data preparation, particularly MVI and DB, in enhancing predictive accuracy.

An analytical and computational model is developed for the electro-osmotic peristaltic transport of pentahybrid nanoparticle-infused Phan-Thien-Tanner viscoelastic blood through a heated, cilia-lined human fallopian tube, integrating metachronal ciliary wave dynamics, electric double layer forcing, shear-thinning rheology, viscous dissipation, and Joule ohmic heating within a unified framework. Closed-form solutions are derived under the Debye-Hückel linearisation and long-wavelength approximations for the transport profiles and metrics across six blood formulations of progressively increasing nanoparticle enrichment. Key results show that electro-osmotic forcing consistently augments axial velocity and reduces the pressure gradient burden, while a co-directional electric field enhances and an opposing field suppresses net peristaltic flux; bulk temperature rises with Joule heating and viscous dissipation but falls with increasing viscoelasticity and cilia density; and streamline bolus morphology is sensitively governed by the electro-osmotic parameter, electric field polarity, Weissenberg number, and flow rate, each producing distinct recirculation cell structures with implications for near-wall drug mixing. Levenberg-Marquardt trained artificial neural network surrogate models for wall shear stress and heat transfer coefficient achieve near-unity regression coefficients, Gaussian zero-centred error distributions, and statistically white residuals, establishing their reliability as computationally efficient surrogate predictive model for parametric design for parametric design of electro-osmotically driven reproductive drug delivery and electrostimulation therapy systems.