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Sonographic measurement of placental volume: a comparative analysis of two-dimensional and three-dimensional sonographic techniques
To evaluate the correlation between a simple two-dimensional (2D) sonographic method and the gold-standard three-dimensional (3D) method for estimating placental volume during early to mid-pregnancy. Placental volume was measured using both 2D and 3D ultrasound techniques in 58 pregnant patients between 11 and 22 weeks of gestation. All participants had normal term pregnancy outcomes. The correlation between the two methods was analyzed using Pearson's correlation coefficient. A strong positive correlation was observed between the 2D and 3D placental volume estimates (Pearson's r=0.93, p<0.001), indicating high agreement between the two measurement approaches. The simpler 2D sonographic method shows excellent correlation with the 3D gold-standard technique and may serve as a feasible alternative for placental volume assessment, particularly in low-resource settings where access to advanced equipment is limited. 2026 the author(s) -
Sustainability assurance in Islamic finance: Improving sharia compliance and social responsibility
This study aims to explore the application of sustainability assurance in increasing sharia compliance and social responsibility of Islamic financial institutions. By highlighting methodologies, challenges, and best practices, this study evaluates how sustainability assurance can support sustainability reporting that is transparent, credible, and compliant with sharia principles. The results of the study show that sustainability assurance contributes significantly to sharia compliance by verifying sustainability reports involving environmental, social, and governance (ESG) aspects. The study also identified key challenges, including the lack of sustainability reporting standards specific to Islamic finance and the need to strengthen the capacity of Islamic financial institution practitioners regarding sustainability assurance. The implications of this study include practical guidance for Islamic financial institutions in formulating effective and sharia- based sustainability policies. 2025, IGI Global Scientific Publishing. All rights reserved. -
Integrating artificial intelligence in Islamic financial management: Opportunities and challenges in maintaining Shariah compliance
The objective of this study is to examine the potential incorporation of Artificial Intelligence (AI) technology into financial management practices that are based on Islamic principles, with a particular emphasis on ensuring Shariah compliance. The literature analysis methodological approach is employed to identify the opportunities and challenges associated with the adoption of AI in the Islamic Finance (IF) environment. The results indicate that the implementation of AI can enhance the efficacy, transparency, and precision of IF operations. However, there are numerous challenges associated with Shariah compliance and ethics. The findings of this study emphasize the importance of establishing a regulatory framework that is consistent with Shariah principles in order to ensure the successful implementation of AI in Islamic financial institutions (IFI). The need for collaboration between finance experts and academics to ensure that the technology is implemented in accordance with Shariah principles, as well as the expansion of training for IF (Islamic Finance) practitioners regarding the implications of AI, are among the recommendations. Future research should examine the influence of more specific AI implementation strategies on Islamic conformance and operational efficacy in the context of IF. 2025 Early Ridho Kismawadi, Mohammad Irfan and Isnaini Harahap. All rights reserved. -
Potassium tert-Butoxide-Mediated Synthesis of 2-Aminoquinolines from Alkylnitriles and 2-Aminobenzaldehyde Derivatives
KOtBu mediates the reaction between 2-amino arylcarbaldehydes and benzyl/alkyl cyanides toward the expeditious formation of 2-aminoquinolines under transition-metal-free conditions. The described transformation proceeds through in-situ generated enimine intermediate from benzyl/alkyl cyanides under KOtBu-mediated reaction conditions. The substituted 2-aminoquinolines were realized in excellent yields at room temperature and shorter reaction time. The designed process exhibits operational simplicity and broad functional group tolerance in delivering the products of high significance. 2022 Wiley-VCH GmbH. -
Deep Learning Driven Predictive Analytics Framework for Assessing Customer Satisfaction in Health Insurance Services
The paper aims to design a predictive analytics platform based on deep learning that can measure the satisfaction of health insurance policyholders with accuracy. One of the major challenges in predicting customer satisfaction is the use of multiple sources of data. These sources also include unstructured consumer sentiments and official demographic and policy measures. The system that is suggested integrates a 1-D Convolutional Neural Network with TabNet, a deep attention-based neural network that is specifically good at handling tabular data. The two are combined to deal with inputs that are highly sentiment-loaded. To enable joint feature learning while maintaining interpretability, the dual-path architecture leverages feature attribution. The suggested method surpasses standard machine learning and isolated deep learning baselines accuracy more than 97% through experimental evaluation on a real-world LIC health insurance dataset. The findings provide the basis for an interpretable and scalable customer-oriented decision-making framework in health insurance. 2025 IEEE. -
Investigation on Preserving Privacy of Electronic Medical Record using Split Learning
Artificial Intelligence is deployed in multiple areas, including healthcare. Utmost research is done in AI enabled healthcare industry because of the demands like accurate result, data security, exact prediction, huge volume of data, etc. In conventional deep learning models, the training happens with the dataset that are stored in a single device. This requires a huge storage space and highly efficient machines to train the data. Usage of big data, demands for innovative models that can be deployed and used in confined storage. Split learning is one such collaborative distributed deep learning model that allows the data to be stored in a split fashion. Split learning supports desirable features like less storage, more privacy to raw data, ability to work with resource constraints, etc., making it suitable for storing electronic medical record of patients. This paper discusses the advantages of using split learning for healthcare, the possible configurations of split learning that supports data privacy in healthcare and finally discusses the open research challenges in implementing split learning for healthcare. 2024 The Authors. Published by Elsevier B.V. -
FADA: Flooding Attack Defense AODV Protocol to counter Flooding Attack in MANET
The intrinsic nature of a Mobile Ad hoc Network (MANET) makes it difficult to provide security and it is more vulnerable to network attacks. Denial of Service (DoS) attack can be executed using Flooding attack, that has the potential to bring down the entire network. This attack works by delivering an excessive number of unwanted packets that consumes too much battery life, storage space, and bandwidth, that eventually lowers the system's performance. In order to flood the network, the attacker injects fake packets into it. Both Control Packet flooding and Data flooding attacks are taken into account in this study. FADA (Flooding Attack Defense AODV) protocol is proposed to counter flooding attack that promotes greater utilization of existing resources. This research identifies the attack-causing node, isolates it and protects the network against flooding attack. Attack Detection Rate, Attack Detection Accuracy, End-to-end delay and Throughput are few metrics used for evaluation of the proposed model. NS-2.35 is used to demonstrate the efficiency of the suggested protocol and the results prove that the proposed model increases system's throughput while decreasing attack. The simulation results have shown that the proposed FADA protocol performs better than the existing models taken into consideration. 2023 IEEE. -
Anonymization Based Deep Privacy Preserving Convolutional Autoencoder Learning Technique for High Dimensional Data Clustering in Big Data Cloud
Data Clustering is a primary research focus in large data-driven application domains in the big data cloud as performance of the clustering dynamic data with high dimensionality is highly challenging due to major concern in the effectiveness and efficiency on data representation. Machine learning is a conventional approach to distribute the data into soft partition still it leads to increasing sparsity of data and increasing difficulties in distinguishing distance between data points. In addition, securing the personnel and confidential information of the user is also becoming vital. In order to tackle those issues, a new anonymization based deep privacy preserving learning paradigm has been presented in this paper. The proposed model is represented as deep privacy preserving convolutional auto encoder learning architecture for secure high dimensional data clustering on inferring the distribution of the data over time. Initially dimensionality reduction and feature extraction is carried out and those extracted feature has been taken for clustering on generation of objective function to produce maximum margin cluster. Those clusters are further fine tuned to feature refinement on the hyper parameter of various layers of deep learning model network to establish the minimum reconstruction error by feature refinement. Softmax layer minimizes the intra cluster similarity and inter cluster variation in the feature space for cluster assignment. Hyper parameter tuning using stochastic gradient descent has been enabled in the output layer to make the data instance in the cluster to be close to each other by determining the affinity of the data on new representation. It results significant increase in the clustering performance on the discriminative informations. Detailed experimental analysis has been performed on benchmarks datasets to compute the proposed model performance with conventional approaches. The performance outcome represents that anonymization based deep privacy preserving clustering learning architecture can produce good scalability and effectiveness on high dimensional data. 2023 American Institute of Physics Inc.. All rights reserved. -
An enhanced biometric attendance monitoring system using queuing petri nets in private cloud computing with playfair cipher
Every educational institutions needs to analyse and monitor participation. Educationists believe that there should be a fair number of students available in the majority of their classes. In colleges participation is used a measure of consistency. To deal with this kind of a challenging situation, biometric based participation monitoring framework is being proposed. This proposed method with the assistance of face recognition will help in maintaining every detail about the present students in a classroom save the same in the class database. The camera captures the image of students and compares them with the existing visual data available in the database. In case, the software is not able to find a match for the captured data in the student database, the particular student is marked as absent. Queuing Petri nets help in fulfilling customised demands of various institutions along with providing better performance in terms of hold up time. With the application of this technology, classroom participation is recorded and saved every hour. The database is accessed and maintained using cloud services and necessary security measured are incorporated as provided by major private cloud service providers with playfair cipher technique. 2020, Institute of Advanced Scientific Research, Inc. All rights reserved. -
Data encryption and decryption using graph plotting
Cryptography plays a vital role in today's network. Security of data is one of the biggest concern while exchanging data over the network. The data need to be highly confidential. Cryptography is the art of hiding data or manipulating data in such a way that where no third party can understand the original data while transmission from source to destination. In this paper, a modified affine cipher algorithm has been used to encrypt the data. The encrypted data will be plot onto a graph. Later, graph will be converted into image. This system allows sender to select his/her own keys to encrypt the original data before plotting graph. Then, Receiver will use the same key to decrypt the data. This system will provide the better security while storing the data in cloud in the form of secret message embedding in graphical image file in network environment. IAEME Publication. -
Calculator using brain computer interface
This paper is undertaken with an aspiration to provide a new way to calculate that can be availed by exploiting BCI. Often it's said things moved at the blink of eye, now the time has come to make it true. This project is developed to ease the efforts in two different ways. First a mind controlled image viewer is build which can be used to change images at the blink of an eye. Second is a simple single digit calculator which lets the user choose the number and the operators just by focusing.Brain-Computer Interface aims to improve the detection and decoding of brain signals acquired by electroencephalogram (EEG). IAEME Publication. -
Duplex functionally graded and multilayered thermal barrier coatings based on 8 % yttria stabilized zirconia and pyrochlores
Thermal Barrier Coatings (TBCs) protect gas turbine engine metal components while they serve in a high temperature environment (upto 1200?). 8% Yttria- Stabilized Zirconia (8YSZ) is the current state of the art material for TBCs. Typically, 250 to 500 ?m (upto 2 mm) thick TBCs can lower the metal temperature by upto 150C than the service temperature and thereby enhance life to the components. 8YSZ TBCs however, suffer from (a) increased sinterability, (b) phase de-stabilization and (c) poor adhesion with time in service at high temperature. In order to facilitate longer engine running time, research is being directed towards finding (i) newer materials that do not possess these deficiencies or (ii) configurations that can overcome them. In order to further improve the performance efficiency of the engines, TBC materials with extended thermal fatigue life at higher than current service temperatures (>1100?) are also being actively investigated. In the same area of research, this thesis presents the findings of work on air plasma sprayed (i) duplex, (ii) functionally graded and (iii) multilayered configurations of TBCs synthesized from commercial 8YSZ and lab synthesized pyrochlore (lanthanum zirconate, lanthanum cerate and lanthanum cerium zirconate) compositions with NiCrAlY bond coat. Duplex i.e., 2-layered TBCs, synthesized by depositing commercial 8YSZ ceramic topcoat (METCO 204 NS) and NiCrAlY bond coat (AMDRY 962) plasma spray powders on Inconel 718 and/or stainless-steel substrates were used for benchmarking purpose (designated as conventional 8YSZ TBC). Next, TBCs were prepared by using these two powders in blended form (8YSZ+NiCrAlY) to serve as a third intermediate layer between the duplex TBC layers in functionally graded material (FGM) configurations. The role of he third intermediate layer is to minimize the thermal expansion mismatch between the ceramic and bond coat layers at elevated temperatures. 8YSZ FGM TBCs were prepared from three different blends of plasma spray powders of NiCrAlY and 8YSZ (i .e., 25%NiCrAlY +75%8YSZ, 50% NiCrAlY + 50% 8YSZ and 60% NiCrAlY + 40% 8YSZ). The development of newer ceramic TBC materials and configurations was achieved by the synthesis of novel pyrochlores and FGM TBCs from them. The Rare-earth pyrochlores and Rare-earth zirconate pyrochlores studied were (i) Lanthanum Zirconate (La2Zr2O7), (ii) Lanthanum Cerium Zirconate (La2 (Zr0.7Ce0.3)2O7 and (iii) Lanthanum Cerate (La2Ce2O7). Plasma sprayable powders of these compositions were synthesized in the laboratory via a solid-state method. They were spray-coated by Atmospheric Plasma Spray (APS) method in duplex layers by using three different spray parameters on NiCrAlY bond coated substrates. The spray parameter that provided the best TBC for each composition was identified based on preliminary thermal fatigue tests. FGM TBCs with (50% NiCrAlY+ 50% 8YSZ) blend as intermediary layer exhibited significantly improved thermal fatigue resistance (life) over conventional 8YSZ TBC (up to 1400?). Hence, in the FGM pyrochlore system too, further studies were restricted to TBCs with (50%NiCrAlY+50% pyrochlore) blend layers to serve as the intermediate FGM layers. Further studies involved the synthesis of multilayered TBCs: two types of systems have been experimented (a) FGM with commercial 8YSZ integrated with the pyrochlores - here the intermediary blend layer was (50% NiCrAlY+ 50% 8YSZ), and lab synthesized pyrochlores were the topcoats and (b) 8YSZ as an intermediary layer and pyrochlores as the topcoats. Identical (to the extent possible) characterization methods were employed to study and evaluate all TBCs synthesized in this research work. They were (1) thermal fatigue tests between high temperature & ambient by using (a) gas flame (1200? & 1400?) and (b) furnace (1150C) (2) oxidation stability tests (at 800?,1000? and 1150?) (3) structural phase analysis (XRD) and (4) microstructure with chemical composition analysis (SEM/EDS). The work was aided by studies on adhesion strength test (ASTM C633 standard), residual stress analysis and assessment of thermal barrier effect (temperature drop across TBC) in chosen few TBCs. TBCs fabricated from three pyrochlores exhibited significant improvements in terms of thermal fatigue resistance at 1200? and 1400?. In duplex, Multilayer (ML) FGM and Multilayer (ML) configurations, La2Ce2O7 (LC) TBC performance was exemplary in all configurations studied in this research work. XRD analysis of pyrochlores in duplex, ML-FGM and ML configurations TBCs evaluated for thermal fatigue at 1200? and 1400? (gas flame heating) exhibited no phase destabilization in the failed specimen, confirming the thermal stability of the TBC system within the coated layers. The trend of improved thermal fatigue resistance of lanthanum cerate TBCs continued when studied via high-temperature furnace heating at 1150? as well. The experimental research work with details of TBC systems, processing, characterizations, and discussion based on findings and published literature to explore the prospective TBC material system and configuration with the potential to serve as an alternative to conventional 8YSZ TBC, in terms of life and thermal fatigue resistance, comprise the main contents of this thesis. -
Is asteroid 33 Polyhymnia a dark matter (DM) degenerate object?
Polyhymnia (33 Polyhymnia) is a main belt asteroid in our solar system with a diameter around 54km. The density of asteroid 33 Polyhymnia, located in the main asteroid belt, is calculated to be 75g/cc. Researchers have speculated the possibility that Polyhymnia could be composed of high-density superheavy elements near atomic number 164. Here, we propose that Polyhymnia could be an asteroid composed of degenerate dark matter (DM) and there could be many such asteroids in our solar system. (This is following our earlier work suggesting that Planet Nine could be such an object.) The Author(s), under exclusive licence to SocietItaliana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
Primordial Planets with an Admixture of Dark Matter Particles and Baryonic Matter
It has been suggested that primordial planets could have formed in the early universe and the missing baryons in the universe could be explained by primordial free-floating planets of solid hydrogen. Many such planets were recently discovered around the old and metal-poor stars, and such planets could have formed in early epochs. Another possibility for missing baryons in the universe could be that these baryons are admixed with DM particles inside the primordial planets. Here, we discuss the possibility of the admixture of baryons in the DM primordial planets discussed earlier. We consider gravitationally bound DM objects with the DM particles constituting them varying in mass from 20 to100 GeV. Different fractions of DM particles mixed with baryonic matter in forming the primordial planets are discussed. For the different mass range of DM particles forming DM planets, we have estimated the radius and density of these planets with different fractions of DM and baryonic particles. It is found that for heavier-mass DM particles with the admixture of certain fractions of baryonic particles, the mass of the planet increases and can reach or even substantially exceed Jupiter mass. The energy released during the process of merger of such primordial planets is discussed. The energy required for the tidal breakup of such an object in the vicinity of a black hole is also discussed. 2023 by the authors. -
Alpha decay favoured isotopes of some superheavy nuclei: Spontaneous fission versus alpha decay
Spontaneous fission and alpha decay are the main decay modes for superheavy nuclei. The superheavy nuclei which have small alpha decay half-life compared to spontaneous fission half-life will survive fission and can be detected in the laboratory through alpha decay. We have studied the alpha decay half-life and spontaneous half-life of some superheavy elements in the atomic range Z = 100-130. Spontaneous fission half-lives of superheavy nuclei have been calculated using the phenomenological formula and the alpha decay half-lives using Viola-Seaborg-Sobiczewski formula (Sobiczewski et al. 1989), semi empirical relation of Brown (1992) and formula based on generalized liquid drop model proposed by Dasgupta-Schubert and Reyes (2007). The results are reported here. -
Evolution of primordial dark matter planets in the early Universe
In a recent paper we had discussed possibility of DM at high redshifts forming primordial planets composed entirely of DM to be one of the reasons for not detecting DM (as the flux of ambient DM particles would be consequently reduced). In this paper we discuss the evolution of these DM objects as the Universe expands. As Universe expands there will be accretion of DM, helium and hydrogen layers (discussed in detail) on these objects. As they accumulate more and more mass, the layers get heated up leading to nuclear reactions which burn H and He when a critical thickness is reached. In the case of heavier masses of these DM objects, matter can be ejected explosively. It is found that the time scale of ejection is smaller than those from other compact objects like neutron stars (that lead to x-ray bursts). These flashes of energy could be a possible observational signature for these dense DM objects. 2021 COSPAR -
Alpha Decay Favoured Isotopes of Some Superheavy Nuclei: Spontaneous Fission Versus Alpha Decay
Romanian Journal of Physics, Vol-57 (9-10), pp. 1335-1345. -
Forecasting Prices of Black Pepper in Kerala and Karnataka using Univariate and Multivariate Recurrent Neural Networks
Our country has a high level of agricultural employment. Price swings harm the economy of our country. To combat this impact, forecasting the selling price of agricultural products has become a need. Forecasts of agricultural prices assist farmers, government officials, businesses, central banks, policymakers, and consumers. Price prediction can then assist in making better selections in this area. Black pepper, sometimes known as the "King of Spices, " is a popular spice farmed and exported in India. The largest producers of black pepper are Karnataka and Kerala. For black pepper in Kerala and Karnataka, this study provides a univariate and multivariate price prediction model using Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The data is denoised using Singular Spectral Analysis (SSA). The most accurate method is the multivariate variate LSTM technique, which uses macroeconomic variables. It has a Mean Absolute Percentage Error (MAPE) of 0.012 and 0.040 for Kerala and Karnataka, respectively. Grenze Scientific Society, 2022. -
A Deep Learning Approach to Phishing Detection Using BiLSTM with an Attention Mechanism
Phishing sites are a serious cyber threat as they trick users into revealing sensitive personal data. Conventional detection techniques, including rule-based systems and blocklists, cannot cope with changing phishing tactics. In this paper, a new approach to phishing detection is introduced using a Bidirectional Long Short-Term Memory (BiLSTM) network with an attention mechanism. The suggested model learns and examines URL-based features and identifies forward and backward relationships in data, enhancing classification accuracy. A 30,000 URL-tagged dataset is utilized to train the model, which is then optimized with the help of methods like sequence tokenization, embedding layers, dropout regularization, and class weight balancing to counter data imbalance issues. The BiLSTM layer processes sequential information about URLs in a bidirectional manner, whereas the attention mechanism applies weights to important features differently to ensure the model pays attention to the most critical elements of phishing URLs. The model was tested based on standard performance metrics and has attained an astounding accuracy of 99.22%, precision of 99.1%, recall of 99.3%, and an F1-score of 99.2%, surpassing the traditional approach like Logistic Regression. The model indicates good generalization ability and is possible to be applied in real-time in web security systems. In the future, the use of dynamic data analysis and large datasets will be applied to improve further the detection efficiency and responsiveness against the new emerging phishing attacks. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Study of magnetoconvection with maxwell cattaneo law /
This thesis deals with the study of Rayleigh-Bénard-convection in a Newtonian fluid and micropolar fluid by replacing the classical Fourier law by non-classical Maxwell-Cattaneo heat flux law. The effects of second sound, non-uniform basic temperature gradients, suctioninjection-combination, temperature modulation and gravity modulation in
presence of external constraints like magnetic field and rotation are studied. The problems investigated in this thesis throw light on externally controlled convection in Newtonian and micropolar fluids in the presence of Maxwell-Cattaneo law. The problems investigated in this thesis deal with practical problems with very large heat fluxes and/or short time duration. With this motivation, we investigate in this thesis five problems and their summary is given below. (i) Effects of Coriolis force and non-uniform basic temperature gradients on the onset of Rayleigh-Bénard-Chandrasekhar convection with Maxwell-Cattaneo law The effect of non-uniform temperature gradient on RayleighBénard-Chandrasekhar convection in a rotating Newtonian fluid with
Maxwell-Cattaneo law is studied using the Galerkin technique. The eigenvalues is obtained for free-free, rigid-free and rigid-rigid velocity boundary combinations with isothermal and adiabatic boundaries. A linear stability analysis is performed. The influence of various parameters on the onset of convection has been analyzed. One linear and fiveix non-linear temperature profiles are considered and their comparative influence on onset is discussed. It is found that the results are noteworthy
at short times and the critical eigenvalues are less than the classical ones. It is shown that the system having magnetic field will delay in the onset of instability. In general, it is observed that step function and inverted parabolic temperature profile are the most destabilizing and stabilizing profiles. The range of values of the parameters of the problem for which oscillatory convection in the case of free-free isothermal boundary exists is also discussed. (ii) The effect of temperature modulation on the onset of RayleighBénard-Chandrasekhar convection using Maxwell-Cattaneo law The effect of imposed time-periodic boundary temperature (ITBT, also called temperature modulation) and magnetic field at the onset of Rayleigh-Bénard convection is investigated by making a linear analysis. The classical Fourier heat law is replaced by the non-classical MaxwellCattaneo law. The venezian approach is adopted in arriving at the critical Rayleigh number and wave number for small amplitude of ITBT. Three
cases of oscillating temperature field are examined: (a) symmetric, so that the wall temperatures are modulated in-phase, (b) asymmetric, corresponding to out-of-phase modulation and (c) only the lower wall is modulated. The temperature modulation is shown to give rise to sub-critical motion. The shift in the critical Rayleigh number is calculated
as a function of frequency and it is found that it is possible to advance or delay the onset of convection by time modulation of the wall temperatures. It is shown that the system is more stable when the boundary temperatures are modulated out of phase.x
(iii) The effect of gravity modulation on the onset of RayleighBénard-Chandrasekhar convection using Maxwell-Cattaneo law The effect of gravity modulation and magnetic field at the onset of Rayleigh-Bénard-Chandrasekhar convection is investigated by making a regular perturbation technique. The stability of the horizontal fluid layer heated from below is examined by assuming time-periodic body acceleration called g-jitter, which normally occurs in satellites and in vehicles connected with microgravity simulation studies. The venezian
approach is adopted in arriving at the critical Rayleigh number and wave number for small amplitude of gravity modulation. The shift in the critical Rayleigh number is calculated as a function of frequency of modulation. It is observed that gravity modulation leads to delayed convection. (iv) The effect of suction-injection-combination (SIC) on the onset of Rayleigh-Bénard-Chandrasekhar convection in a micropolar fluid with Maxwell-Cattaneo law The effect of suction-injection-combination (SIC) on the onset of Rayleigh-Bénard-Chandrasekhar convection in a micropolar fluid with Maxwell-Cattaneo law is studied using the Galerkin technique. The eigenvalue is obtained for free-free, rigid-free and rigid-rigid velocity boundary combinations with isothermal and adiabatic on the spinvanishing boundaries. A linear stability analysis is performed. The influence of various micropolar fluid parameters on the onset of convection has been analyzed. It is found that the effect of Prandtl number on the stability of the system is dependent on the SIC beingxi pro-gravity or anti-gravity. A similar Pe-sensitivity is found in respect of the critical wave number. The problem suggests an elegant method of external control of internal convection. (v) The effect of non-uniform temperature gradients on RayleighBénard-Chandrasekhar convection in a micropolar fluid with
Maxwell-Cattaneo law The effect of non-uniform temperature gradient on RayleighBénard-Chandrasekhar convection in a micropolar fluid with MaxwellCattaneo law is studied using the Galerkin technique. The eigenvalue is obtained for free-free, rigid-free and rigid-rigid velocity boundary combinations with isothermal and adiabatic on the spin-vanishing boundaries. A linear stability analysis is performed. The influence of various micropolar fluid parameters on the onset of convection has been
analyzed. Six different non-uniform temperature profiles are considered their comparative influence on onset is discussed. It is observed that the micropolar fluid layer heated from below is more stable compared to the classical Newtonian fluid layer.