Radius: Journal of Science and Technology
An International Bi-Annual Peer-Reviewed Refereed Journal
About the Journal
Radius: Journal of Science and Technology is an open-access international bi-annual peer-reviewed refereed journal that publishes high-quality research dedicated to advancing the frontiers of Science and Technology. It serves as a platform for the propagation and discussion of significant research findings, cutting-edge innovations, …
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For Indian Authors: ₹ 3000
For others: US$ 100
Article Processing Charge
03 Days
Median time from submission to first editorial decision before peer review
15 Days
Median time from submission to first decision after peer review
25 Days
Median time from submission to final acceptance after peer review and any author revisions
Editor-in-Chief View Full Editorial Board
Editor-in-Chief
Prof. (Dr.) Ajay Singh Verma
Anand School of Engineering & Technology, Sharda University Agra, Uttar Pradesh, India
Articles
A conceptual framework for deep learning-based multimodal emotion detection using facial expressions and physiological signals
Open Access | Original Article | 05 June 2026 | Article Number: 261005
Abstract
There is increased interest in one aspect of this, namely the identification of emotion as an important field in affective computing, in which the ability of intelligent systems to analyze human emotions has been introduced and the efficiency of human–machine interactions has been improved. Facial expression recognition (FER) is the only modality used in traditional approaches to emotion recognition and consequently these approaches are less robust because of inter-personal and inter-environmental variability. In this paper, the recent advances in multimodal emotion recognition (MER) systems that involve combining the facial expression recognition through deep learning approaches with physiological signal-based emotion recognition (PSER) are reviewed. An overview of the reviewed works shows that a conceptual multimodal framework can be presented that consists of data acquisition, data preprocessing, feature extraction, multimodal fusion, and classification stages. Previous studies have extensively used Convolutional Neural Networks (CNNs) for facial feature extraction, while physiological signals like electrodermal activity (EDA) and heart rate variability (HRV) have been traditionally analyzed using statistical and frequency domain approaches. Emotional pattern learning across the various domains and fusion of heterogeneous multimodal representations can be achieved by feature-level fusion strategies, as reported in the literature. Recent studies in the literature have shown that temporal modeling approaches have proven to be more stable when facing dynamic environmental conditions. Architectures reviewed are typically modular and designed to be scalable and adaptable for future real-life implementation scenarios. The aim of the present work is primarily to review the latest emotion recognition systems based on multimodal approach and to present a conceptual framework based on the literature. Future research directions are suggested to be experimental implementation and quantitative validation. Read more
View PDFNumerical Investigation of Power-Law Non-Newtonian Fluid Flow in Concentric Annular Geometry Using Finite Difference Method with Convergence Analysis
Open Access | Original Article | 27 May 2026 | Article Number: 261004
Abstract
Through a detailed numerical study, the steady, laminar, incompressible power-law non-Newtonian fluid flow in concentric annular geometry is discussed. A FDM with Picard iterative scheme is used to solve the non-linear momentum equation in cylindrical coordinate under “No Slip” conditions to study the motion of the system. The computational domain is divided into 100 radial nodes, and has a tolerance of 10-6. The flow behaviour index is analysed for 0.6, 1.0 and 1.4 corresponding to shear-thinning fluid, Newtonian fluid & shear-thickening fluid respectively. A trend of increasing flow resistance is seen for shear-thickening fluids with the results showing a reduction in the maximum speed from 0.260 m/s to 0.138 m/s as flow behaviour index increases from 0.6 to 1.4. Just as in the previous example, the volumetric flow rate has a nonlinear decrease for an increasing n. The numerical solution of the Newtonian case (n=1) is able to confirm the accuracy of the numerical method proposed, indicating acceptable agreement with the analytical solution of the same. In addition, smooth and stable velocity and shear stress distributions indicate that the numerical scheme is robust. The influence of the rheological characteristics on the characteristics of flow is significant and hence the study clarifies the important role of the rheological parameters during analyzing the non-Newtonian annular flow for engineering applications. Read more
View PDFA numerical scheme for fluid-surfactant systems with data assimilation: Stability and energy behaviour analysis
Open Access | Original Article | 26 May 2026 | Article Number: 261003
Abstract
This study introduces a numerical implementation of fluid-surfactant systems that is based on a coupled phase-field model, combined with a nudging-based data assimilation mechanism. Governing equations are expressed on the basis of the Cahn–Hilliard formulation, along with a transport equation of a surfactant, and the velocity of the fluid is simplified through the use of a gradient-driven model to decrease the computational cost. A finite difference discretization with explicit time integration is used to discretize the system. Simulations are done to study the energy evolution, error dynamics, interface behavior and the surfactant distribution. The findings indicate that the total free energy grows and levels off, not due to energy dissipation but due to limited numerical behavior. The L2-error between the simulated and observed phase-field shows rapid early growth and then levels off, showing little efficiency of the data assimilation with the selected parameters. The analysis of the phase-field shows that explicit discretization causes checkerboard-type numerical oscillations that cause the loss of interface smoothness. Additionally, the surfactant distribution remains nearly uniform, indicating diffusion-dominated dynamics with weak coupling to the interface. Overall, the proposed framework provides a computationally efficient approach for modelling fluid–surfactant systems; however, the findings highlight the need for improved numerical stability and enhanced data assimilation strategies to achieve physically consistent and accurate simulations. Read more
View PDFPhysicochemical Comparison of Rambutan Nephelium lappaceum Seed Fat and Dairy Butter Fat for Alternative Use
Open Access | Original Article | 04 May 2026 | Article Number: 261002
Abstract
Rambutan (Nephelium lappaceum) seed fat is an underexplored plant-based lipid which shows potential for both functional and nutritional applications. In this study, its physicochemical properties along with its fatty acid profile were compared with those of the dairy butter. The Rambutan seed fat showed a melting point of 28.5°C, which is about 3–6°C lower than butter (32–35°C) indicating a softer consistency. Saponification value of Rambutan seed fat (201.54 mg KOH/g) was also lower than that of butter (225–240 mg KOH/g), suggesting the presence of longer-chain fatty acids. The peroxide value was less than the quantitation limit of 0.2 meq O2/kg of fat, reflecting minimal primary oxidation at the time of analysis. The fatty acid composition was dominated by monounsaturated fatty acids (74.3%), mainly oleic and eicosenic acids, which is substantially higher than in butter (28–30%). In contrast, saturated fatty acids were markedly lower (15.2% vs. 63–65%), while polyunsaturated fatty acids were relatively higher (10.5% vs. 2–3%). These differences help explain the softer texture and lower melting behaviour of Rambutan seed fat. Overall, the findings suggest that it could serve as a viable alternative fat, although further studies on stability and application are needed. Read more
View PDFStudy of the Wear & Tear Behavior of Resin-based Composites Reinforced with Al2O3 Ultrafine nanoparticles and Metal Wires applying DOE Techniques
Open Access | Original Article | 30 March 2026 | Article Number: 261001
Abstract
The scientists investigate the mechanical characteristics of an epoxy-based biodegradable composite material reinforced with Al2O3 ultrafine particles and metal wires, and uses design of experiments (DOE) and simulation techniques to optimize its attributes. The Al2O3 nano powder was characterized by SEM and used in weight percentage of 0-5% to create composites with 50-100 nm nanoparticles. ANN models were trained on the resulting data to estimate the tensile strength and moisture absorbing behavior of the composite under different conditions, with mean absolute errors of 5% and 10% for the training and test sets respectively. The data demonstrate the effectiveness of computational learning in predicting the material properties of natural/jute composite materials and optimizing their design. Overall, this research paper provides valuable observation into the use of Al2O3 ultrafine nanoparticles and metal wire reinforcement for enhancing the overall material properties of biodegradable epoxy-based composites. Read more
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