Multimodal Face and Ear Recognition Using Feature Level and Score Level Fusion Approach
BSIF; Ear; Face; Feature level fusion; Multimodal biometrics; Score level fusion
Recent years have seen a significant increase in attention in multimodal biometric systems for personal identification especially in unconstrained environments. This paper presents a multimodal recognition system by combining feature level fusion of ear and profile face images. Multimodal biometric systems by combining face and ear can be used in an extensive range of applications because we can capture both the biometrics in a non-intrusive manner. Local texture feature descriptor, BSIF is used to extract discriminative features from biometric templates. Feature level and score level fusion is experimented to improve the performance of the system. Experimental results on different public datasets like GTAV, FEI, etc., show that the proposed method gives better performance in recognition results than individual modality. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-843, pp. 279-288.
Springer Science and Business Media Deutschland GmbH
Resmi K.R., CHRIST (Deemed to be) University, Bengaluru, India; Joseph A., Santhigiri College, Kerala, Thodupuzha, India; George B., Santhigiri College, Kerala, Thodupuzha, India
Multimodal learning for autonomous systems and robotics
advanced artificial intelligence (AI); Autonomous systems; generative adversarial networks (GANs); Kalman filters; LiDAR; multimodal learning; multimodal neural networks (MMNNs); Multisensor data fusion; robotics
The realm of autonomous systems and robotics is experiencing a paradigm shift driven by the integration of advanced artificial intelligence (AI) techniques and multimodal learning approaches. This abstract explores the latest advancements and research topics that are propelling the field toward more intelligent, efficient, and versatile autonomous systems. Multimodal learning leverages multiple sensory inputs to enhance the perception and decision-making capabilities of autonomous systems. This involves the integration of visual, auditory, tactile, and other sensory data to form a coherent understanding of the environment. Deep learning techniques, such as multimodal neural networks and crossmodal embeddings, play a pivotal role in this integration, enabling the system to learn joint representations and improve robustness in perception under varying conditions. Computer vision remains a cornerstone of autonomous systems, with advancements in techniques such as real-time object detection, tracking, and high-resolution image synthesis through generative adversarial networks. Vision-based reinforcement learning is also gaining traction, enabling systems to learn from visual inputs and improve their decision-making processes in dynamic environments. The integration of advanced sensors, including high-resolution light detection and ranging, radio detection and ranging, and event-based cameras, enhances the capability of autonomous systems to perceive their surroundings accurately. Multisensor data fusion, using methods like Kalman and particle filters, ensures robust perception even in adverse conditions, providing a comprehensive view of the environment. Innovations in actuation and control systems are fundamental for the development of responsive and adaptive robots. Soft robotics, inspired by biological systems, offers new possibilities in design, modeling, and control. Hybrid control systems facilitate the coordination of multimodal actuation, enhancing the robots versatility and performance. The deployment of high-performance embedded systems, incorporating heterogeneous computing architectures (CPU-GPU-FPGA integration), is vital for real-time data processing and decision-making. Neuromorphic computing and AI hardware accelerators provide low-power solutions that are crucial for the efficiency of autonomous systems. Techniques for uncertainty estimation, outlier detection, and anomaly detection are essential for maintaining system reliability. Advanced robotic perception and cognition, combined with cognitive architectures for autonomous reasoning, enable systems to operate safely in complex and dynamic environments. The interface between humans and robots is evolving, with a focus on multimodal human-robot interaction. Learning from human demonstrations and ensuring safety and trust in human-robot teams are critical areas of research, promoting effective collaboration between humans and robots. Advanced simulation techniques, including high-fidelity physics-based simulations and domain randomization, are employed to test and validate autonomous systems. Virtual reality and augmented reality provide immersive environments for training and testing. Real-time simulation and hardware-in-the-loop testing ensure the robustness and reliability of autonomous systems before deployment. Ethical AI and autonomous decision-making frameworks are being developed to address these issues. Privacy-preserving machine learning techniques and cybersecurity measures are essential for protecting sensitive data and ensuring the security of autonomous systems. This comprehensive overview underscores the rapid advancements and multifaceted nature of multimodal learning and autonomous systems, heralding a new era of intelligent and adaptive robotics capable of transforming numerous industries and improving the quality of human life. 2026 Elsevier Inc. All rights reserved.
J., Lekha; Meena Preethi, B.; Yuvaraj, K.P.; Chery, Angelo P.
Multimodal Learning Using Heterogeneous Data;pp.193-215
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1016/B978-0-443-27528-9.00005-X https://www.scopus.com/pages/publications/105032948422?origin=resultslist
J. L., Department of Statistics and Data Science, Christ University, Maharashtra, Pune, India; Meena Preethi B., Department of Software Systems, Sri Krishna Arts and Science College, Tamil Nadu, Coimbatore, India; Yuvaraj K.P., Department of Mechanical Engineering, Sri Krishna College of Engineering and Technology, Tamil Nadu, Coimbatore, India; Chery A.P., Department of Statistics and Data Science, Christ University, Maharashtra, Pune, India
Multimodal Learning Using Heterogeneous Data
Multimodal Learning Using Heterogeneous Data is a comprehensive guide to the emerging field of multimodal learning, which focuses on integrating diverse data types such as text, images, and audio within a unified framework. The book delves into the challenges and opportunities presented by multimodal data and offers insights into the foundations, techniques, and applications of this interdisciplinary approach. It is intended for researchers and practitioners interested in learning more about multimodal learning and is a valuable resource for those working on projects involving data analysis from multiple modalities. The book begins with a comprehensive introduction, focusing on multimodal learning's foundational principles and the intricacies of heterogeneous data. It then delves into feature extraction, fusion techniques, and deep learning architectures tailored for multimodal data. It also covers transfer learning, pre-processing challenges, and cross-modal information retrieval. The book highlights the application of multimodal learning in specialized contexts such as sentiment analysis, data generation, medical imaging, and ethical considerations. Real-world case studies are woven into the narrative, illuminating the applications of multimodal learning in diverse domains such as natural language processing, multimedia content analysis, autonomous systems, and cognitive computing. The book concludes with an insightful exploration of multimodal data analytics across social media, surveillance, user behavior, and a forward-looking examination of future trends and practical implementations. As a collective resource, Multimodal Learning Using Heterogeneous Data illuminates the powerful utility of multimodal learning to elevate machine learning tasks while also highlighting the need for innovative solutions and methodologies. The book acknowledges the challenges associated with deep learning and the growing importance of ethical considerations in the collection and analysis of multimodal data. Overall, Multimodal Learning Using Heterogeneous Data provides an expansive panorama of this rapidly evolving field, its potential for future research and application, and its vital role in shaping machine learning's evolution. 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Eslamian, Saeid; Nanjundan, Preethi; George, Jossy; Eslamian, Faezeh
Multimodal Learning Using Heterogeneous Data;pp.1-290
https://doi.org/10.1016/C2024-0-01863-2 https://www.scopus.com/pages/publications/105032925564?origin=resultslist
Eslamian S., Department of Water Science and Engineering, College of Agriculture, Isfahan University of Technology, Isfahan, Iran; Nanjundan P., Department of Data Science, CHRIST University, Lavasa Campus, Maharashtra, Pune, India; George J., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India; Eslamian F., GHD, QC, Canada
Multimodal sentiment analysis: integrating text, image, and audio
audio analysis; fusion methods; image analysis; Multimodal sentiment analysis; text analysis
Multimodal sentiment analysis aims to integrate text, images, and audio information to provide a more comprehensive understanding of human emotions and opinions. This chapter reviews key aspects of multimodal sentiment analysis, including feature extraction techniques, fusion methods, modeling approaches, and applications. For feature extraction the chapter discusses lexical, syntactic, and semantic features for text; visual attributes and facial expressions for images; and acoustic properties for audio. Three primary fusion techniques are examined: early fusion, which combines features before classification; late fusion, which integrates outputs from unimodal models; and model-based fusion, which learns joint representations across modalities. The chapter explores traditional machine learning and deep learning modeling approaches, highlighting the effectiveness of neural architectures like CNNs and RNNs. Key application areas discussed include social media analysis, emotion recognition, intelligent transportation, and education. The chapter also outlines future research directions, such as crossmodal learning, multimodal pretraining, and explainable AI. As multimodal data increases, sentiment analysis techniques that can effectively integrate information across modalities will become increasingly crucial for understanding human emotions and opinions in diverse contexts. This review provides a comprehensive overview of current approaches and emerging trends in this rapidly evolving field. 2026 Elsevier Inc. All rights reserved.
Multimodal Learning Using Heterogeneous Data;pp.99-115
https://doi.org/10.1016/B978-0-443-27528-9.00017-6 https://www.scopus.com/pages/publications/105032930614?origin=resultslist
George J., Department of Computer Science, CHRIST University, Karnataka, Bengaluru, India
Multiobjective portfolio optimization using multilevel quantum inspired optimization algorithms: a comparative study
evolutionary algorithm; multilevel quantum-inspired techniques; Portfolio optimization; quantum-inspired ant colony optimization (QiACO); quantum-inspired particle swarm optimization (QiPSO)
The study of portfolio optimization has been a significant focus for computer science and finance researchers, with frequent publication of innovative methods. Numerous works have illustrated that conventional approaches like quadratic programming struggle with nonlinear constraints. This chapter compares ant colony optimization and particle swarm intelligence optimization within classical and quantum inspired frameworks, utilizing qubits and qutrits. This study analyzes benchmark datasets from the NASDAQ, Dow Jones, and BSE spanning over a decade. A pioneering effort has been made to develop a multiobjective portfolio optimization technique through a multilevel quantum inspired optimization algorithm. The experimental results demonstrate that the quantum inspired metaheuristic technique that utilizes qutrits slightly outperforms classical and qubit based quantum inspired methods. 2026 Elsevier Inc. All rights reserved.
Gunjan, Abhishek; Bhattacharyya, Siddhartha
Multilevel Quantum Metaheuristics: Applications in Data Exploration;pp.155-204
https://doi.org/10.1016/B978-0-443-33136-7.00007-4 https://www.scopus.com/pages/publications/105032958834?origin=resultslist
Gunjan A., Christ (Deemed to be) University, Karnataka, Bangalore, India; Bhattacharyya S., VSB Technical University of Ostrava, Ostrava, Czech Republic, Algebra Bernays University, Zagreb, Croatia
Multiple Approaches in Retail Analytics to Augment Revenues
Association mining; ECLAT; Product recommender; Skip-gram; word2vec
Knowledge is power. The retail sector has been revolutionized around the clock by the plentiful product knowledge available to customers. Today, customers can use the knowledge available online at any time to study, compare and purchase products from anywhere. Retail companies can stay ahead of shopper trends by using retail information analytics to discover and analyze online and in-store shopper patterns. A product recommender will suggest products from a wide selection that would otherwise be very difficult to locate for the customer. The algorithm would recommend various products, increase the sales of items that would otherwise be difficult to sell. Market basket analysis is a common use scenario for the search for frequent patterns, which involves analyzing the transactional data of a retail store to decide which items are bought together. To do so data from online resource has been taken, which is analyzed and several conclusions were made. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Lecture Notes in Networks and Systems, Vol-290, pp. 276-284.
Banik H., Department of School of Business and Management, CHRIST (Deemed to be University), Bangalore, India; Iyer L.S., Department of School of Business and Management, CHRIST (Deemed to be University), Bangalore, India
Multiple Safety Equipment's Detection at Active Construction sites Using Effective Deep Learning Techniques
Computer Vision; Deep Learning; Labour Workers Safety; Machine Learning; Single Shot Detection; You Only Look Once
The safety of human labour is the most important thing in this era no matter where the labour force works. Governments and various NGOs focus on ensuring the delivery of the top safety to the labor class of the country. One such example is the working of the labour force at huge construction sites. For them a lot of work includes a huge amount of risks hence following full safety is the need of the hour for the workers working at construction sites. In order to deal with proper monitoring of the safety being followed at Construction sites. In order to make use of the latest technologies in this field also some of the good object detection models can be used for detecting the safety equipment of the workers which include things like Hard Hats, Masks, Vest, Boots. A lot of research is going on in improving the detection speed and accuracy of objects using state-of-the-art techniques in Computer Vision and this could lead to providing better results. Based on the available research and compute resources future work can be done to improve the results in this specific domain also. 2022 IEEE.
2022 6th International Conference on Trends in Electronics and Informatics, ICOEI 2022 - Proceedings, pp. 1271-1276.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICOEI53556.2022.9777164 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131930299&doi=10.1109%2fICOEI53556.2022.9777164&partnerID=40&md5=f083a8221fdb5ab9c4cc038b27d681b5
Tyagi R., CHRIST University, Data Science Department, Bangalore, India; Thomas K.T., CHRIST University, Data Science Department, Bangalore, India
Multiple slip effects on MHD non-Newtonian nanofluid flow over a nonlinear permeable elongated sheet: Numerical and statistical analysis
Brownian motion; Casson fluid; Multiple slip effects; Nanofluids; Nonlinear stretching sheet; Statistical analysis
Purpose: The purpose of this paper is to examine the interaction effects of a transverse magnetic field and slip effects of Casson fluid with suspended nanoparticles over a nonlinear stretching surface. Mathematical modeling for the law of conservation of mass, momentum, heat and concentration of nanoparticles is executed. Design/methodology/approach: Governing nonlinear partial differential equations are reduced into nonlinear ordinary differential equations and then shooting method is employed for its solution. The slope of the linear regression line of the data points is calculated to measure the rate of increase/decrease in the reduced Nusselt number. Findings: The effects of magnetic parameter (0=M=4), Casson parameter (0.1=?<8), nonlinear stretching parameter (0=n=3) and porosity parameter (0=P=6) on axial velocity are shown graphically. Numerical results were compared with another numerical approach and an excellent agreement was observed. This study reveals the fact that the Brownian motion parameter and boundary layer thickness have a direct relationship with temperature. Also, Brownian motion and thermophoresis contribute to an increase in the thermal boundary layer thickness. Originality/value: Despite the immense significance and repeated employment of non-Newtonian fluids in industry and science, no attempt has been made up till now to inspect the Casson nanofluid flow with a permeable nonlinear stretching surface. 2019, Emerald Publishing Limited.
Raza J.; Farooq M.; Mebarek-Oudina F.; Mahanthesh B.
Multidiscipline Modeling in Materials and Structures, Vol-15, No. 5, pp. 913-931.
https://doi.org/10.1108/MMMS-11-2018-0190 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85069509741&doi=10.1108%2fMMMS-11-2018-0190&partnerID=40&md5=a104d4596976dd8f32dcd819412f1d05
Raza J., School of Quantitative Sciences, Universiti Utara Malaysia, Sintok, Malaysia; Farooq M., University of Lahore, Pakpattan Campus, Pakpattan, Pakistan; Mebarek-Oudina F., Department of Physics, Faculty of Sciences, University of 20 ao 1955-Skikda (Skikda University), Skikda, Algeria; Mahanthesh B., Department of Mathematics, Christ University, Bangalore, India
Multiple solutions and stability analysis in MHD non-Newtonian nanofluid slip flow with convective and passive boundary condition: Heat transfer optimization using RSM-CCD
This study explores the effect of Williamson nanofluid in the presence of radiation and chemical reaction caused by stretching or shrinking a surface with convective boundary conditions. After implementing two-component model and Lie group theory, the transformed ODEs are solved using the RungeKutta DormandPrince (RKDP) shooting approach technique. The dual solutions are predicted for certain range of physical nanofluid parameters, such as Williamson parameter ((Formula presented.)), stretching/shrinking parameter ((Formula presented.)), and suction parameter ((Formula presented.)) with different slip (Formula presented.) and magnetic M parameters. Contour plots are generated for the stable branch of the Nusselt number ((Formula presented.)) for different combinations, providing insights into the heat transfer characteristics. The eigenvalue problem is solved in order to predict flow stability. The optimization of heat transfer in nanoliquid is conducted by RSM-CCD. The resulting quadratic correlation enables the prediction of the optimal Nusselt number for (Formula presented.), (Formula presented.), and (Formula presented.). This investigation is motivated by various applications including manufacturing processes, thermal management systems, energy conversion devices, and other engineering systems where efficient heat transfer iscrucial. 2023 Wiley-VCH GmbH.
Rana P.; Sharma P.K.; Kumar S.; Makkar V.; Mahanthesh B.
ZAMM Zeitschrift fur Angewandte Mathematik und Mechanik, Vol-104, No. 1
https://doi.org/10.1002/zamm.202200145 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85169929423&doi=10.1002%2fzamm.202200145&partnerID=40&md5=7eda2f673b9f2213e0100540ed2af1af
Rana P., School of Mathematical Sciences, College of Science, Mathematics and Technology, Wenzhou-Kean University, Zhejiang, Wenzhou, China; Sharma P.K., Department of Applied Sciences, KIET Group of Institutions, Uttar Pradesh, Ghaziabad, India; Kumar S., Department of Applied Sciences, KIET Group of Institutions, Uttar Pradesh, Ghaziabad, India; Makkar V., Department of Basic and Applied Sciences, School of Engineering and Sciences, GD Goenka University, Haryana, Gurgaon, India; Mahanthesh B., Centre for Mathematical Needs, Department of Mathematics, CHRIST (Deemed to be University), Karnataka, Bangalore, India
Multiplier-free Realization of High throughout Transpose Form FIR Filter
block filtering; common sub-expression elimination; Transpose structure
This paper presents a multiplier-free realization of the block finite impulse response (FIR) filter in transpose form configuration using binary constant shifts method (BCSM). The proposed architecture is synthesized using Xilinx Vivado and Cadence RTL Encounter compiler for the area and power analysis and is compared with the existing works in the literature. The comparison highlights the advantages of the proposed architecture in terms of power, hardware complexity and throughput for realizing reconfigurable high throughput block FIR filters. 2020 IEEE.
2020 12th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2020
Sudharman S., Christ (Deemed to Be University), Department of Electronics and Communication Engineering, Bangalore, Karnataka, India; Bindiya T.S., National Institute of Technology, Department of Electronics and Communication Engineering, Calicut, Kerala, India
Multitask EfficientNet affective computing for student engagement detection
Affective computing; Behavioral analysis; Deep learning; Emotional analysis; Engagement analysis; Face detection; Multi task-EfficientNEtB0; Multi-modal student analysis
In the realm of education, feedback emerges as a pivotal component, serving to foster engagement and interaction while also facilitating the refinement of teaching methods to capture and maintain student attention. Traditional classroom assessment methods often struggle to accurately gauge the degree of comprehension among students during lectures, relying on manual comment collection that inherently carries the risk of inaccuracies. In response to this challenge, a novel system has been proposed, harnessing the power of Facial Emotion Recognition (FER) technology to capture student feedback. Within this framework, students are given a unique avenue to convey their emotions and reactions, employing facial expressions and gestures as the means to communicate. This innovative approach enables the analysis of students emotional responses and thereby provides invaluable insights into their comprehension levels, as well as the overall quality and engagement experienced during lectures. The approach takes shape through the utilization of Computer Vision techniques, with a particular focus on an unobtrusive methodology for assessing students overall engagement. Overcoming limitations of traditional assessment, our approach integrates compound scaling, employing the proposed Multitask EfficientNetB0 model recognized for its proved accuracy in emotion recognition (95.7%) and behavior analysis (96.3%) across diverse datasets (DAiSEE, iSED, iSAFFE). The behavioral classification system categorizes students into Engaged and Disengaged classes within a multi-class framework, providing nuanced insights into comprehension and Student engagement. Assessment metrics, including ROC Curves, Precision, Recall, and F1-Score, ensure a thorough evaluation. Our systems adaptability is demonstrated across varied educational environments, showcasing real-world efficacy in classrooms, laboratories, and seminar halls. The inclusion of MTCNN enhances face detection capabilities, facilitating robust analysis in dynamic scenarios. Expanding its applicability, the model has been put to the test in a range of educational settings, including classrooms, laboratory environments, and seminar halls, offering dual-capability analysis of both emotions and behavior. This comprehensive approach yields nuanced insights into student engagement and interaction, and its performance has been validated through real-world deployment within classrooms and seminars The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Thiruthuvanathan M.M., Computer Science and Engineering, CHRIST University, Kanmanike, Karnataka, Bengaluru, 560029, India; Krishnan B., Computer Science and Engineering, CHRIST University, Kanmanike, Karnataka, Bengaluru, 560029, India
Thiruthuvanathan, Michael Moses; Krishnan, Balachandran
Multimedia Tools and Applications;Volume;84;Issue;18;pp.19039-19063
https://doi.org/10.1007/s11042-024-19815-3 https://www.scopus.com/pages/publications/85198561288?origin=resultslist
Multivariate Forecasting of Co2 Emissions Using Hybrid Machine Learning Models Based on Energy Consumption and Renewable Adoption
Affordable and Clean Energy; Climate Action; CO2 Emission Forecasting; Hybrid Machine Learning; Multivariate Time Series; Renewable Energy
The study presents a machine learning approach to predict carbon dioxide (CO2) emissions by analysing key factors such as energy consumption, renewable energy adoption, and economic growth (GDP). Traditional forecasting methods struggle to capture the complex and nonlinear patterns of emissions. To overcome the limitations and improve the accuracy, research combines classical statistical models like ARIMA and VAR with advance techniques, including deep learning (LSTM) and ensemble methods (XGBoost, stacking). The models are trained on a global dataset of energy and economic records. The results shows that the hybrid models, particularly the LSTM + XGBoost and stacked approaches, have outperformed the traditional methods by obtaining a lower Root Mean Square Error (RMSE) and a higher coefficient of determination (R2). Apart from advancing environmental data science, the research offers a solid predictive framework to support policy initiatives related to the Sustainable Development Goals, specifically SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action). 2025 IEEE.
Lakshmi, Elluri Sri; Kavitha, R.; Vinoth, Dalvin
Proceedings of 2025 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2025;
https://doi.org/10.1109/IConSCEPT66142.2025.11436999 https://www.scopus.com/pages/publications/105036573475?origin=resultslist
Lakshmi E.S., CHRIST (Deemed to Be University), Department of Statistics and Data Science, Bangalore, India; Kavitha R., CHRIST (Deemed to Be University), Department of Statistics and Data Science, Bangalore, India; Vinoth D., CHRIST (Deemed to Be University), Department of Statistics and Data Science, Bangalore, India
Multivariate optimization of electrochemical sensing parameters for bisphenol a detection using an AgNPs/g-C3N4/IL@GCE via box-behnken design
We developed an electrochemical sensor for detecting BPA using AgNPs/g-C3N4/IL@GCE. Graphitic carbon nitride was synthesized by the calcination of melamine at 550 C. In contrast AgNPs were synthesized via a green tea extract method, and the nanocomposite was dropcast onto the GCE surface along with 1-butyl-3-methylimidazolium methyl sulfate (BMIM-MeSO4) ionic liquid as a binder. Morphology was characterized, and response surface methodology (RSM) using Box-Behnken Design (BBD) was used as a concurrent strategy to optimize pH, scan rate, and deposition time. Independent validation experiments conducted at multiple interior points within the design space showed good agreement between predicted and experimental responses, with prediction errors of 6.29%-10.11% for oxidation peak current and 1.63%-5.24% for oxidation potential. Applying the optimized conditions, the sensor linearized BPA detection over the concentration range of 1-10 ?M, with a limit of detection of 0.66 ?M, and a limit of quantification of 2.20 ?M. The sensor showed excellent recovery (99.16%-102.26%) of BPA present in real water samples. The improved electrocatalytic activity of the sensor interfaces was due to the synergistic effects of AgNPs and g-C3N4. The novelty of this research was the use of an RSM-BBD approach to systematically optimize a green synthesized AgNPs/g-C3N4 nanocomposite electrode, enabling predictive modelling to show reasonable electrochemical sensitivity towards BPA detection. 2026 The Electrochemical Society ("ECS"). Published on behalf of ECS by IOP Publishing Limited. All rights.
Farale, Harish; Sreevidhya, K.B.; Manuel, Manju; Kanchi, Suvardhan
Journal of the Electrochemical Society;Volume;173;Issue;4;Article No.;47504;
https://doi.org/10.1149/1945-7111/ae42f5 https://www.scopus.com/pages/publications/105033901792?origin=resultslist
Farale H., Department of Chemistry, CHRIST University, Bengaluru, 560029, India, Centre for Renewable Energy and Environmental Sustainability, CHRIST University, Bengaluru, 560029, India; Sreevidhya K.B., Department of Chemistry, CHRIST University, Bengaluru, 560029, India, Centre for Renewable Energy and Environmental Sustainability, CHRIST University, Bengaluru, 560029, India; Manuel M., Department of Chemistry, CHRIST University, Bengaluru, 560029, India, Centre for Renewable Energy and Environmental Sustainability, CHRIST University, Bengaluru, 560029, India; Kanchi S., Department of Chemistry, CHRIST University, Bengaluru, 560029, India, Centre for Renewable Energy and Environmental Sustainability, CHRIST University, Bengaluru, 560029, India
Multivariate statistical optimization of phenolics and antioxidants from nutmeg seeds (Myristica fragrans Houtt)
ANOVA; Antioxidant; Nutmeg; Optimization; Response surface methodology
The present study aimed to optimize the phenolic and antioxidant-rich extract from the nutmeg (Myristica fragrans Houtt) by using a two-factor 26-run central composite design-based response surface methodology tool. The selected parameters were extraction period (2 to 5days), solvent-to-water ratio (v/v) (50100%), and type of solvent (acetone or ethanol). The optimized extract at conditions of 3.14days incubation and 68% (v/v) acetone showed total phenolic content (TPC), total flavonoid content (TFC), and DPPH antioxidant assay as 376.38mg GAE/g DW, 34.40mg QUE/g DW and 842.46mg AAE/g DW, respectively. Among the nineteen (19) compounds identified by the LCMS, myristicin (37.74%) was found to be the highest. Nine (9) alkane-fatty acyl compounds were determined by the GCMS analysis, as well. Additionally, SEM and XRD revealed sheet-like anatomy with the presence of Carbon (C), Oxygen (O) and Potassium (K). The study presented a unique approach to optimizing phenolic-rich antioxidant extracts from nutmeg using response surface methodology, offering valuable insights for more efficient extraction of bioactive compounds with minimal resource waste and potentially enhancing the utilization of nutmeg's nutraceutical properties. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Journal of Food Measurement and Characterization
Chacko A.M., Department of Life Sciences, Christ University, Karnataka, Bangalore, 560029, India; Nag A., Department of Life Sciences, Christ University, Karnataka, Bangalore, 560029, India
Journal of Food Measurement and Characterization;Volume;19;Issue;2;pp.1154-1166
https://doi.org/10.1007/s11694-024-03031-6 https://www.scopus.com/pages/publications/85212692201?origin=resultslist
Multiwavelength spectral modelling of the candidate neutrino blazar PKS 0735+178
galaxies: active; galaxies: BL Lacertae objects: individual: PKS 0735+178; galaxies: jets
The BL Lac object PKS 0735+178 was in its historic ?-ray brightness state during 2021 December. This period also coincides with the detection of a neutrino event IC 211208A, which was localized close to the vicinity of PKS 0735+178. We carried out detailed ?-ray timing and spectral analysis of the source in three epochs: (a) quiescent state (E1), (b) moderate-activity state (E2), and (c) high-activity state (E3) coincident with the epoch of neutrino detection. During the epoch of neutrino detection (E3), we found the largest variability amplitude of 95 per cent. The ?-ray spectra corresponding to these three epochs are well fit by the power-law model and the source is found to show spectral variations with a softer when brighter trend. In epoch E3, we found the shortest flux doubling/halving time of 5.75 h. Even though the spectral energy distribution in the moderate-activity state and in the high-activity state could be modelled by the one-zone leptonic emission model, the spectral energy distribution in the quiescent state required an additional component of radiation over and above the leptonic component. Here, we show that a photomeson process was needed to explain the excess ?-ray emission in the hundreds of GeV that could not be accounted for by the synchrotron self-Compton process. 2024 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society.
Bharathan A.M.; Stalin C.S.; Sahayanathan S.; Bhattacharyya S.; Mathew B.
Monthly Notices of the Royal Astronomical Society, Vol-529, No. 4, pp. 3503-3510.
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.1093/mnras/stae296 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189176809&doi=10.1093%2fmnras%2fstae296&partnerID=40&md5=3ea90ebb8f1aff0614da54f207c05289
Bharathan A.M., Department of Physics and Electronics, Christ (Deemed to Be University), Bangalore, 560029, India; Stalin C.S., Indian Institute of Astrophysics, Block II, Koramangala, Bangalore, 560034, India; Sahayanathan S., Astrophysical Sciences Division, Bhabha Atomic Research Centre, Mumbai, 400085, India, Homi Bhabha National Institute, Mumbai, 400094, India; Bhattacharyya S., Astrophysical Sciences Division, Bhabha Atomic Research Centre, Mumbai, 400085, India, Homi Bhabha National Institute, Mumbai, 400094, India; Mathew B., Department of Physics and Electronics, Christ (Deemed to Be University), Bangalore, 560029, India
Multiway Relay Based Framework for Network Coding in Multi-Hop WSNs
Multi-hop wireless sensor networks; multi-server multi-class queuing models; multiway relay node; network coding; throughput
In todays information technology (IT) world, the multi-hop wireless sensor networks (MHWSNs) are considered the building block for the Internet of Things (IoT) enabled communication systems for controlling everyday tasks of organizations and industry to provide quality of service (QoS) in a stipulated time slot to end-user over the Internet. Smart city (SC) is an example of one such application which can automate a group of civil services like automatic control of traffic lights, weather prediction, surveillance, etc., in our daily life. These IoT-based networks with multi-hop communication and multiple sink nodes provide efficient communication in terms of performance parameters such as throughput, energy efficiency, and end-to-end delay, wherein low latency is considered a challenging issue in next-generation networks (NGN). This paper introduces a single and parallels stable server queuing model with a multi-class of packets and native and coded packet flow to illustrate the simple chain topology and complex multiway relay (MWR) node with specific neighbor topology. Further, for improving data transmission capacity in MHWSNs, an analytical framework for packet transmission using network coding at the MWR node in the network layer with opportunistic listening is performed by considering bi-directional network flow at the MWR node. Finally, the accuracy of the proposed multi-server multi-class queuing model is evaluated with and without network coding at the network layer by transmitting data packets. The results of the proposed analytical framework are validated and proved effective by comparing these analytical results to simulation results. 2023 Tech Science Press. All rights reserved.
Menaria V.K.; Nayyar A.; Kumar S.; Kotecha K.
Computers, Materials and Continua, Vol-74, No. 1, pp. 1199-1216.
https://doi.org/10.32604/cmc.2023.032162 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139753367&doi=10.32604%2fcmc.2023.032162&partnerID=40&md5=b4488f31221890e1ea2d0d23f3e597db
Menaria V.K., Department of IT & C (DoIT & C), Govt of Rajasthan, Jaipur, India; Nayyar A., Graduate School, Faculty of Information Technology, Duy Tan University, Da Nang, Viet Nam; Kumar S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India; Kotecha K., Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International University, Pune, India
Murraya koenigii extract blended nanocellulose-polyethylene glycol thin films for the sustainable synthesis of antibacterial food packaging
Antimicrobial; Antioxidant; Food packaging; Murraya koenigii; Nanocellulose
Non-biodegradable plastics are a worldwide problem that have a negative impact on all living things, including humans. Nanocellulose, an excellent biopolymer is known for their increasing uses in food, healthcare, cosmetics, and various other fields. Nanocellulose is readily biodegradable, bioderived, and useful for creating innovative bioplastics that are employed in the production of food packaging and wound dressing. Curry leaves (Murraya koenigii) belongs to the rutaceae family and has many health benefits. Synthesis of Murraya koenigii incorporated nanocellulose thin films, and its characterisation using FT-IR, and XRD is discussed in detail. The source of nanocellulose in this study is sugar cane bagasse, an easily available agricultural residue in Kerala. Also, a biocompatible plasticizer is utilised to produce antibacterial packaging for food. The synthesised nanocomposites showed non-toxicity against THP1-derived macrophage cells and significant antibacterial activity against gram positive and gram-negative bacteria suggesting the possible application as a viable alternative for food packaging materials. 2023 Elsevier B.V.
Madhavan A.; Reshmy R.; Arun K.B.; Philip E.; Sindhu R.; Nair B.G.; Awasthi M.K.; Pandey A.; Binod P.
Sustainable Chemistry and Pharmacy, Vol-32
https://doi.org/10.1016/j.scp.2023.101021 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149967433&doi=10.1016%2fj.scp.2023.101021&partnerID=40&md5=6a6f5e39d08b5d1c5d4703a317e3a9a1
Madhavan A., School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, Kollam, 690525, India; Reshmy R., Post Graduate and Research Department of Chemistry, Bishop Moore College, Kerala, Mavelikara, 690 110, India, Department of Science and Humanities, Providence College of Engineering, Kerala, Chengannur, 689122, India; Arun K.B., Department of Life Sciences, CHRIST (Deemed to be University), Karnataka, Bengaluru, 560029, India; Philip E., Post Graduate and Research Department of Chemistry, Bishop Moore College, Kerala, Mavelikara, 690 110, India; Sindhu R., Department of Food Technology, TKM Institute of Technology, Kollam, 689 122, India; Nair B.G., School of Biotechnology, Amrita Vishwa Vidyapeetham, Amritapuri, Kerala, Kollam, 690525, India; Awasthi M.K., College of Natural Resources and Environment, Northwest A & F University, Yangling, Shaanxi, 712 100, China; Pandey A., Centre for Energy and Environmental Sustainability, Uttar Pradesh, Lucknow, 226 029, India, Centre for Innovation and Translational Research, CSIR- Indian Institute for Toxicology Research (CSIR-IITR), 31 MG Marg, Lucknow, 226 001, India; Binod P., Microbial Processes and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology, Trivandrum, 695 019, India
Musculoskeletal Disorders and Psychological Well-being among Indian Nurses: A Narrative Review of Impacts and Interventions (2024)
India; Musculoskeletal Disorder; Nurses; Prevalence; Risk-Factors
Background: A prevalent occupational health issue that may have a detrimental effect on nurses' mental health and general well-being is musculoskeletal problems. This narrative review aimed to explore the social, economic, and personal implications of Musculoskeletal Disorder on nurses in India, and examine support, and intervention strategies available for them. Material & Methods: A comprehensive literature search was conducted in electronic databases, including PubMed, Scopus, and Google Scholar, using relevant keywords related to Musculoskeletal Disorder, mental health, nurses, social, personal, support, and intervention. The inclusion criteria were articles published in English and focused the nursing workforce in India. Results: A total of 15 articles were selected for review synthesis. According to the summary, nurses in India who suffer from musculoskeletal disorders deal with serious social and personal repercussions that impact their everyday life and general well-being. Musculoskeletal Disorder can lead to decreased social connections, reduced job satisfaction, and physical and emotional distress. However, limited interventions are available that address Musculoskeletal Disorder and the mental health of nurses in India. Conclusion: There is a significant effect of Musculoskeletal Disorder on the mental health, quality of life, and economic well-being of nurses in India. However, limited scientific research exists exploring the prevalence and psychosocial implications of Musculoskeletal Disorder in the Indian nursing population. Consequently, additional research is essential to comprehend the scope and ramifications of this occupational health concern. To create interventions and support systems that are effective in the unique cultural and occupational context of nursing in India, it is imperative to engage in interdisciplinary collaboration. 2024 The Author(s); Published by Rafsanjan University of Medical Sciences.
Journal of Occupational Health and Epidemiology, Vol-13, No. 3, pp. 215-224.
Rafsanjan University of Medical Sciences
https://doi.org/10.61186/johe.13.3.215 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211813479&doi=10.61186%2fjohe.13.3.215&partnerID=40&md5=9d5f467ef54238787aae1b3b71ac5fda
Bishal P., Dept. of Psychology, School of Social Sciences, CHRIST (Deemed to be University), Bengaluru, India; Priyadarshini M.S., School of Psychological Sciences, CHRIST (Deemed to be University), Bengaluru, India