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Green Synthesis of Hydroxyapatite Nanoparticles Using Semecarpus anacardium Linn leaf Extract: Examination of Anticancer Activity and its Apoptosis Induction
Nanotechnology and biomedical sciences enable diverse molecular and cellular applications. Plant-mediated biosynthesis of nanoparticles, a green chemistry approach, offers a cost-effective, eco-friendly alternative to traditional methods. This study focuses on developing hydroxyapatite nanoparticles (HA-NPs) using Semecarpus anacardium Linn (SAL) leaf extract (termed SAL@HA-NPs) as a capping agent and reducing agent. The presence of needle-shaped nanostructures was verified using SEM and TEM investigation. The presence of well-defined rings in the selected area electron diffraction (SAED) patterns provided evidence for the polycrystalline nature of the SAL@HA-NPs). The XRD spectrum exhibited clear peaks that closely corresponded to the hexagonal patterns of HA, indicating a mean crystalline diameter of 54.25nm. The FTIR analysis revealed the presence of biomolecules from Semecarpus anacardium Linn leaves on the surface of the nanoparticles. The suspension of SAL@HA-NPs displayed a polydispersity index of 0.445 and demonstrated excellent stability, as indicated by the zeta potential of -32.2 mV, as observed in the DLS tests. The SAL@HA-NPs exhibited a harmful effect on the HeLa cervical and HepG2 liver cancer cells, with an IC50 value of 52g/mL. Fluorescence microscopy revealed the deformation of the damaged cell membrane, fragmentation, and cell death following treatment with SAL@HA-NPs. The Annexin V-FITC and PI staining confirmed the mode of apoptosis by flow cytometric analysis. Thus, the SAL@HA-NPs acquired in this study could have a crucial impact on the biomedical domain of cancer treatment. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
A study of the structure, luminescence and cytotoxicity of new green-emitting terbium-doped CaS nanophosphors
CaS nanoparticles have attracted significant attention due to their wide range of optoelectronic applications. The synthesis of CaS nanophosphors doped with different terbium concentrations through a simple wet chemical co-precipitation method using triethanolamine (TEOA) as a capping agent is reported here. X-ray diffractogram ensured that the nanoparticles were crystallized in the cubic phase with space groupFm3 m. Morphology and particle size of the TEOA-capped CaS:Tb nanophosphors were determined using scanning electron microscopy and transmission electron microscopy. The optical properties of the samples were studied by photoluminescence spectroscopy and UVVis absorption measurements. The prepared nanoparticles exhibited green luminescence, which is attributed to 5D47FJ transitions of terbium ions incorporated into the CaS lattice. The existence of various functional groups in the synthesized products was identified by Fourier transform infrared spectroscopy. The lifetime decay measurements showed that the lifetime of the samples was in the nanosecond range. Cytotoxicity analysis of the nanoparticles was carried out on L929 fibroblast cells, which confirmed that the nanoparticles are biocompatible across a wide range of concentrations. Our findings indicate that CaS:Tb nanophosphor could be a potential candidate as a green-emitting phosphor in optoelectronics and biomedical field. 2020, Springer Science+Business Media, LLC, part of Springer Nature. -
A comparison of in vitro cytotoxicity of undoped and doped surface modified CaS nanoparticles
In the present study we compare the cytotoxicity of undoped and doped surface modified CaS nanoparticles synthesized by wet chemical co-precipitation technique using L929 human fibroblasts cell lines. The toxicity was determined by evaluating the cell viability and changes in cell morphology. In addition, the half-maximal inhibitory concentration (IC50) values for all the samples were also compared. This analysis shows that undoped and terbium doped TEOA capped CaS nanoparticles are more biocompatible and will be better candidates for various applications in the biomedical field. 2021 Elsevier B.V. -
A Comparative Study of the Nonlinear Optical Properties of CaO Nanoparticles and rGO-CaO Nanocomposites
Recently, graphene-based materials decorated with metal/metal oxide nanoparticles have gained significant interest among researchers owing to their wide range of technological applications. In this study, we synthesized reduced graphene oxide-calcium oxide nanocomposites (rGO-CaO) using a one-pot solvothermal technique. The third-order nonlinear optical (NLO) properties of CaO nanoparticles and (rGO-CaO) nanocomposites were explored by performing a single-beam Z-scan experiment. Since the samples exhibited reverse saturation absorption behavior (RSA) and a negative nonlinear index of refraction, CaO nanoparticles are promising candidates for nonlinear optical limiting and optical switching applications. Indian Association for the Cultivation of Science 2024. -
Foreground algorithms for detection and extraction of an object in multimedia
Background Subtraction of a foreground object in multimedia is one of the major preprocessing steps involved in many vision-based applications. The main logic for detecting moving objects from the video is difference of the current frame and a reference frame which is called "background image" and this method is known as frame differencing method. Background Subtraction is widely used for real-time motion gesture recognition to be used in gesture enabled items like vehicles or automated gadgets. It is also used in content-based video coding, traffic monitoring, object tracking, digital forensics and human-computer interaction. Now-a-days due to advent in technology it is noticed that most of the conferences, meetings and interviews are done on video calls. It's quite obvious that a conference room like atmosphere is not always readily available at any point of time. To eradicate this issue, an efficient algorithm for foreground extraction in a multimedia on video calls is very much needed. This paper is not to just build Background Subtraction application for Mobile Platform but to optimize the existing OpenCV algorithm to work on limited resources on mobile platform without reducing the performance. In this paper, comparison of various foreground detection, extraction and feature detection algorithms are done on mobile platform using OpenCV. The set of experiments were conducted to appraise the efficiency of each algorithm over the other. The overall performances of these algorithms were compared on the basis of execution time, resolution and resources required. 2020 Institute of Advanced Engineering and Science. -
Depression, anxiety, stress and marital adjustment among women
Marriage, especially for women in a patriarchal society involves a huge transition process. The struggle with new responsibilities and expectations is overwhelming in itself. But with the feelings of worthlessness and feeling trapped and bound in a loveless and thankless bond, come distress and adjustment issues. According to a recent Nielsen survey on 'Women of Tomorrow', out of 21 nations and 6500 women, India is a leading nation when it comes to stress in women. About 87% of women were stressed most of the time and 82% claimed that they did not find time to relax. Women in the age range from 22 years to 55 years are the most stressed and are struggling hard to strike a balance between their home lives, social activities and jobs. The present study aims to examine depression, stress, anxiety and adjustment issues among women. A total of 80 married women were selected for the study with 40 working and 40 non-working women. The Revised Dyadic Adjustment Scale and Depression Anxiety Stress Scales were administered to collect data. Negative relationship was obtained between stress, anxiety depression and marital adjustment among married women. Anxiety and Marital adjustment are moderately correlated (-.346) while Stress (-.454) and Depression (-0.487) are highly correlated with marital adjustment. 2020 Journal of International Women's Studies. -
Unique synergism in flame retardancy in ABS based composites through blending PVDF and halloysite nanotubes
This study demonstrates flame retardant materials designed using bi-phasic polymer blends of acrylonitrile butadiene styrene (ABS) and polyvinylidene fluoride (PVDF) containing halloysite nanotubes (HNTs) and Cloisite 30B nanoclay. The prepared blends with and without nanoparticles were extensively characterized. The nanoparticles were added in different weight concentrations to improve the flame retardancy. It was observed that prepared ABS/PVDF blends showed better flame retardancy than ABS based composites. The flame resistance was further improved by the addition of nanoparticles in the blends. The microscale combustion calorimetry (MCC) test showed better flame resistance in ABS/PVDF blends filled with 5 wt% HNTs than other composites. The total heat release of ABS/PVDF blend filled with 5 wt% HNTs decreased by 31% and also the heat of combustion decreased by 26% as compared to neat ABS. When compared with nanoparticles, the addition of PVDF reduced the peak heat release rate (PHRR) and increased the char residue more effectively. A synergistic improvement was observed from both PVDF and HNTs on the flame resistance properties. 2017 IOP Publishing Ltd. -
Dynamics of public debt sustainability in major Indian states
This study empirically tests whether the public debt is sustainable or not at 22 major Indian states during 200607 to 201516. It employs the Bohn model for panel data, five alternative specifications and p-spline technique to analyze the issue at aggregate and disaggregate levels. While the results indicate that the debt is sustainable at the aggregate level, it is sustainable only in about 11 states. The results suggest that the fiscal reaction function is linear and the central grant-in aid is an important and a significant undermining factor of sustainability. If the grant-in-aid is excluded from the primary balance, there remain significant positive responses at the aggregate level. However, at the disaggregate level it is significant in only 11 states. Further, the most sustainable states fail to meet the no-Ponzi condition and so the policy intervention is required to improve the debt situation of the states where debt is unsustainable. 2019, 2019 Informa UK Limited, trading as Taylor & Francis Group. -
Comparing the Accuracy of CNN Model with Inception V3 for Music Instrument Recognition
Identification of music instruments from an audio signal is a complex but useful task in music information retrieval. Deep Learning and traditional machine learning models are extremely very useful in many music related tasks such as music genre classification, recognizing music similarity, identifying the singer etc. Music Instrument recognition and classification would be helpful in categorizing different categories of music. Many researchers have proposed models for classifying western music instruments. But very little research has been done in identifying instruments accompanied with South Indian music. This research aims at identifying string instrument such as violin and woodwind instrument such as flute accompanied in a Carnatic music concert and also in other categories of music. In order to identify the instruments accompanied, Convolutional Neural Network model and Inception V3 models were used. The Mel Frequency Cepstral Coefficients images were extracted from the audio input and fed in to the neural network model. The model has been trained for the above mentioned instruments, tested and validated on different types of audio input. This research also evaluates the performance of Inception V3 transfer learning model with CNN model in recognizing the instruments used in different categories of music. 2024, Ismail Saritas. All rights reserved. -
Influence of grandparents on the emotional intelligence of early adolescents in Kerala
Children find unique acceptance in their relationships with grandparents, which benefits them emotionally and mentally. The presence of grandparents in the family can be a source of great support for other family members, especially children and adolescents. They are often role models, playmates and mentors for younger generations. The aim of the study is to compare emotional intelligence of adolescents with regard to the influence of grandparents through a quantitative research design. The sample taken for this research comprised of 427 adolescents of VIII to XII standards, among which 278 were from nuclear families and 149 from three generation families. They belonged to ten different government aided urban state syllabus English medium schools in Kerala. Mangal Emotional Intelligence Inventory was used to yield the total score and four dimensional scores in areas of Intrapersonal awareness, Interpersonal awareness, Intrapersonal management and Interpersonal management in adolescents. An independent sample t-test between two types of families indicated that grandparents have an influence on the emotional intelligence of adolescents. Journal of the Indian Academy of Applied Psychology. -
An efficient privacy-preserving model based on OMFTSA for query optimization in crowdsourcing
Crowdsourcing is now one of the most important and transformative paradigms, with great success in a variety of application tasks. Crowdsourcing obtains knowledge and information to solve cognitive or intelligence-intensive tasks from an evolving group of participants via the Internet. Unfortunately, providing a hard privacy guarantee and query optimization is incompatible when a higher task acceptance rate needs to be accomplished and this case is common in most existing crowdsourcing solutions. The state of art systems suffered from different complexities such as lack of crowdsourcing optimization techniques, increased cost, latency, security, and scalability issues. In this paper, we have proposed a crowdsourcing model to optimize the cost and latency, issues that occur while query optimization using the Moth Flame and Tunicate Swarm Algorithm (MF-TSA). The TSA algorithm is added to the MF algorithm to enhance its exploitation capability and yield fast convergence. The data privacy concerns of the worker and the requestor are addressed using homomorphic encryption that simultaneously enhances the efficiency of the crowdsourcing framework. The main aim of this work is to optimize the cost and latency for query plan selection along with security. Initially, the homomorphic encryption model is used to encrypt the data. In query design, two kinds of crowd-controlled administrators, that is, Crowd Powered Selection (CSelect) and Crowd Powered Join (CJoin) are connected for assessing query. The proposed framework utilizes MF-TSA to optimize the selection and join queries with low cost and latency. Finally, the experimental results demonstrate better query optimization performance than other existing algorithms such as sequential, parallel, and CrowdOp. 2021 John Wiley & Sons Ltd. -
Brain image classification using time frequency extraction with histogram intensity similarity
Brain medical image classification is an essential procedure in Computer-Aided Diagnosis (CAD) systems. Conventional methods depend specifically on the local or global features. Several fusion methods have also been developed, most of which are problem-distinct and have shown to be highly favorable in medical images. However, intensity-specific images are not extracted. The recent deep learning methods ensure an efficient means to design an end-to-end model that produces final classification accuracy with brain medical images, compromising normalization. To solve these classification problems, in this paper, Histogram and Time-frequency Differential Deep (HTF-DD) method for medical image classification using Brain Magnetic Resonance Image (MRI) is presented. The construction of the proposed method involves the following steps. First, a deep Convolutional Neural Network (CNN) is trained as a pooled feature mapping in a supervised manner and the result that it obtains are standardized intensified pre-processed features for extraction. Second, a set of time-frequency features are extracted based on time signal and frequency signal of medical images to obtain time-frequency maps. Finally, an efficient model that is based on Differential Deep Learning is designed for obtaining different classes. The proposed model is evaluated using National Biomedical Imaging Archive (NBIA) images and validation of computational time, computational overhead and classification accuracy for varied Brain MRI has been done. 2022 CRL Publishing. All rights reserved. -
Ultraviolet Flux and Spectral Variability Study of Blazars Observed with UVIT/AstroSat
Blazars, the peculiar class of active galactic nuclei, are known to show flux variations across the accessible electromagnetic spectrum. Though they have been studied extensively for their flux variability characteristics across wavelengths, information on their ultraviolet (UV) flux variations on timescales of hours is very limited. Here, we present the first UV flux variability study on intraday timescales of a sample of ten blazars comprising two flat-spectrum radio quasars (FSRQs) and eight BL Lacertae objects (BL Lacs). These objects, spanning a redshift (z) range of 0.034 ? z ? 1.003, were observed in the far-UV (1300?1800 and near-UV (2000?3000 wavebands using the ultraviolet imaging telescope on board AstroSat. UV flux variations on timescales of hours were detected in nine sources out of the observed ten blazars. The spectral variability analysis showed a bluer-when-brighter trend with no difference in the UV spectral variability behavior between the studied sample of FSRQs and BL Lacs. The observed UV flux and spectral variability in our sample of both FSRQs and BL Lacs revealed that the observed UV emission in them is dominated by jet synchrotron process. 2024. The Author(s). Published by the American Astronomical Society. -
Efficient feature fusion model withmodified bidirectional LSTM for automatic Parkinson's disease classification
The majority of people affected by Parkinsons disease (PD) are middle-aged and older. The condition causes a variety of severe symptoms, including tremors, limited flexibility, and slow movements. As Parkinsons disease develops with changing symptoms and growing severity, the importance of computer-aided diagnosis based on algorithms cannot be highlighted. Gait recognition technology appears to be a potential path for Parkinson's disease identification since it captures unique properties of a persons walking pattern without requiring active participation, providing stability and non-intrusiveness. To begin,the median filter is used to remove noise from the input images received during data collection. This paper describes a new method for finding local and global features in gait images to assess the severity of Parkinsons disease.Local features are extracted using a stacked autoencoder, and global features are obtained using an Improved Convolutional Neural Network (ICNN). The Enhanced Sunflower Optimisation (ESO) technique is used to improve the CNN model's performance by optimizing hyperparameters such as batch size, learning rate, and number of convolutional layers. To classify PD severity, a modified bidirectional LSTM (MBi-LSTM) classifier receives input in the form of a combination of local and global features. The proposed model's performance is completely evaluated with the GAIT-IT and GAIT-IST datasets, which include key measures such as accuracy, precision, recall, and the F-measure. This study improves the diagnosis of Parkinsons disease by introducing a non-intrusive real-time monitoring system capable of early detection and prevention. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
Electrical and Mechanical Properties of Vapour Grown Gallium Monotelluride Crystals
International Journal of Minerals, Metallurgy and Materials, Vol-20 (10), pp. 967-971. ISSN-1674-4799 -
Vapour Growth and Characterization of Beta Indium Sesquitelluride Crystals
Journal of Crystal Growth, Vol-394, pp. 1-6. ISSN-0022-0248 -
Electrical and mechanical properties of vapour grown gallium monotelluride crystals
The physical vapour deposition (PVD) of gallium monotelluride (GaTe) in different crystalline habits was established in the growth ampoule, strongly depending on the temperature gradient. Proper control on the temperatures of source and growth zones in an indigenously fabricated dual zone furnace could yield the crystals in the form of whiskers and spherulites. Optical and electron microscopic images were examined to predict the growth mechanism of morphologies. The structural parameters of the grown spherulites were determined by X-ray powder diffraction (XRD). The stoichiometric composition of these crystals was confirmed using energy dispersive analysis by X-rays (EDAX). The type and nature of electrical conductivity were identified by the conventional hot probe and two probe methods, respectively. The mechanical parameters, such as Vickers microhardness, work hardening index, and yield strength, were deduced from microindentation measurements. The results show that the vapour grown p-GaTe crystals exhibit novel physical properties, which make them suitable for device applications. 2013 University of Science and Technology Beijing and Springer-Verlag Berlin Heidelberg. -
Spherulitic crystallization of ?-In2Te3 by physical vapour deposition
Different morphologies of indium telluride (In2Te3) including novel spherulites were crystallized using the physical vapour deposition (PVD) method, by varying the difference in the growth and source zone temperature (?T) of a dual zone horizontal furnace assembled indigenously. Whiskers and kinked needles of In2Te3were grown at ?T = 250 K and 300 K respectively, maintaining the growth zone at 500 C. At high supersaturation (? T = 400 K), spherulitic crystals were obtained. The stoichiometric composition of these crystals has been confirmed using energy dispersive analysis by x-rays (EDAX). The structure of ?-In2Te3 spherulitic crystals is identified as zinc blende with lattice parameter a = 6.159 from x-ray diffraction (XRD) studies. The scanning electron microscope (SEM) images revealed the radial structure of the grown spherulites. The growth mechanism for the spherulitic crystallization of ?-In2Te3 crystals has been discussed based on the theoretical models. Copyright 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. -
Vapour growth and characterization of beta indium sesquitelluride crystals
Physical Vapour Deposition (PVD) provides stoichiometric crystals of different morphology, depending upon the materials, geometry of ampoules, temperature profiles, growth parameters and kinetics of crystallization. The crystal forms such as needles, platelets and spherulites of beta indium sesquitelluride (?-In2Te3) were produced by controlling the temperature of source and growth zones. The X-Ray Diffraction (XRD) and chemical analysis of the spherulitic crystals confirmed zinc blende structure with beta phase. Their resistivity (135.16 ? cm) at room temperature (300 K) was determined by van der Pauw method. The temperature dependence of DC conductivity was investigated using the conventional two-probe technique. The variation of dielectric constant (?1) and dielectric loss (tan ?) with temperature has been studied for different frequencies (1 kHz-1 MHz). The AC conductivity, ?ac(?) was found to vary with angular frequency as ?s, where s is the frequency exponent. The values of s lie very close to unity and show a slight decrease with increase in temperature, which indicate a Correlated Barrier Hopping (CBH) between centres forming Intimate Valence Alternation Pairs (IVAP). The activation energy for conduction ranges from 0.187 eV to 0.095 eV. The microhardness of ?-In2Te3 spherulites is found to be 353.5 kg/mm2, which is higher than that of other semiconducting chalcogenides. The results thus obtained on crystals grown from vapour phase open up ample possibilities for radiation detector applications. 2014 Elsevier B.V. -
Ear Recognition Using Rank Level Fusion of Classifiers Outputs
An individuals authentication plays a vital role in our daily life. In the last decade, biometric-based authentication has become more prevalent than traditional approaches like passwords and pins. Ear recognition has gained attention in the biometric community in recent years. Researchers defined several features for the identification of a person from ear image. The features play a vital role in the success of classification models. This paper considers an ensemble of features for designing a new classification model. The features are assessed in isolation as well as through feature-level fusion. Subsequently, a rank-level fusion for classification is introduced. The experiments are conducted on both constrained and unconstrained ear datasets. The results are promising and open up new possibilities in machine learning-based ear recognition 2023, International journal of online and biomedical engineering.All Rights Reserved.