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Compressed unfired blocks made with iron ore tailings and slag
Growing demand for houses in urban India has increased the requirements for construction materials such as clay fired bricks and cement blocks. At the same time, conventional practice of brick manufacturing is not environment friendly due to high energy consumption and CO2 emissions during various stages of its production. Therefore, recent trend in research has been directed towards utilization of various industrial wastes and methods, which emerge as sustainable alternatives for environmental concerns arising in the construction industry. This study focused on utilizing mining waste, namely iron ore tailing (IOT) in development of stable blocks. It has reported various properties of compressed unfired blocks formed by IOT and ground granulated blast furnace slag (GGBS) in varying proportions and with a fixed amount of lime. The combination of GGBS and lime was found to be suitable in stabilizing IOT towards block production. Furthermore, a maximum compressive strength of 7.7 MPa was achieved for blocks after 28days of air curing. Also, the addition of GGBS has reduced the water absorption and apparent porosity of the IOT blocks, confirming the positive interaction between IOT, GGBS and lime. It also indicates the prospective of blended binders in improving the compactness of the blocks, which will have direct influence on the durability and service life of the blocks. Finally, the results show that most of the developed blocks satisfy the requirement of IS 1077 specification and can be used in various applications such as load and non-load bearing walls, framed structures, foundations and pedestrian walkways. 2022 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
SECURE DOMINATION IN TRANSFORMATION GRAPH Gxy+
In this paper, we characterize graphs for which the secure domination number of the transformation graph Gxy+ is 1 or 2. Also we prove that for any connected graph G with at least 4 pendant vertices, the secure domination number is greater than or equal to the secure domination number of the transformation graph G-++. We also find a bound for the secure domination number of G-+ when G is a tree. 2024 Jangjeon Research Institute for Mathematical Sciences and Physics. All rights reserved. -
Optimising QoS with load balancing in cloud computing applying dual fuzzy technique
Cloud computing has become a necessity when the internet usage has increased drastically. This research paper objective is to optimise quality of service in cloud computing using dual fuzzy technique. With the competition to provide the best quality service at cloud data centre, we are analysing the parameters of average response time, average completion time, average CPU utilisation and job success. Cloud-sim simulator along with the mathematical model is used to provide reliable and valid result. To achieve the best result, the load in data centre needs to be efficiently distributed, so that it is managed to process maximum service requests with the best service response time and very few failures. In this paper, we applied dual fuzzy technique for the load balancing in the cloud data centre and the findings were extensive and support the proposed technique. With this technique, cloud computing service provider can provide better quality service. Copyright 2021 Inderscience Enterprises Ltd. -
Workflow Scheduling Using Heuristic Scheduling in Hadoop
In our research study, we aim at optimizing multiple load in cloud, effective resource allocation and lesser response time for the job assigned. Using Hadoop on datacenter is the best and most efficient analytical service for any corporates. To provide effective and reliable performance analytical computing interface to the client, various cloud service providers host Hadoop clusters. The previous works done by many scholars were aimed at execution of workflows on Hadoop platform which also minimizes the cost of virtual machines and other computing resources. Earlier stochastic hill climbing technique was applied for single parameter and now we are working to optimize multiple parameters in the cloud data centers with proposed heuristic hill climbing. As many users try to priorities their job simultaneously in the cluster, resource optimized workflow scheduling technique should be very reliable to complete the task assigned before the deadlines and also to optimize the usage of the resources in cloud. The Korea Institute of Information and Communication Engineering. -
Hybrid scheme image compression using DWT and SVD
Image compression is process of reducing data size to represent an image by removing redundant data. Hybrid scheme image compression is combination of methods performed in order or as an amalgam to form a new technique. In this paper, we proposed a new approach to compress the image by collaborating Discrete Wavelet Transformation (DWT) and Singular Value Decomposition (SVD). Image is decomposed into wavelets using DWT and approximate wavelet is subsequently transformed into four bands. Different wavelet filters are implemented for transformation namely Haar, Daubechies, Biorthogonal and Coiflets. Apart from approximate image, SVD is applied on remaining wavelets (Horizontal, Vertical and Diagonal Details) at each decomposition level. On reconstruction, various singular values are selected depending on the level transformation. The performance of the proposed method is compared and evaluated with SVD, DCT-SVD and DWT-DCT-SVD. Evaluation is carried out based on Compression Ratio (CR), Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) index. From the experimental results, it is observed that proposed method yields better MSE, PSNR and SSIM compared to state-of-the-art methods. 2017, Institute of Advanced Scientific Research, Inc. All rights reserved. -
Analysis of zoochemical from Meretrix casta (Mollusca: Bivalvia) extracts, collected from Rameswaram, Tamil Nadu, India and their pharmaceutical activities
The marine ecosystem's diverse animal species offer a unique opportunity to discover marine-derived natural products. While numerous invertebrates have been studied, research on Indian marine invertebrates, especially Meretrix casta, remains limited. This study explores the zoochemical composition of ethyl acetate and methanolic extracts from Meretrix casta off Rameswaram, Tamil Nadu, India, and evaluates their bioactive potential, focusing on antioxidant properties, glucose uptake in yeast cells, and alpha-amylase activity. The results reveal the presence of alkaloids, flavonoids, polyphenols, sterols, terpenoids, and cardiac glycosides in both extracts, highlighting their bioactive potential. Although their antioxidant capacity is slightly lower than ascorbic acid, the extracts demonstrated significant alpha-amylase inhibition, suggesting their potential in blood sugar regulation and diabetes management. These findings underscore the therapeutic potential of M. casta in developing anti-diabetic compounds, warranting further pharmacological exploration. Authors. -
Studies on the characterisation of thiophene substituted 1,3,4-oxadiazole derivative for the highly selective and sensitive detection of picric acid
A novel thiophene substituted 1,3,4-oxadiazole based chemosensor namely 2-(4-(5-(5-hexylthiophen-2-yl) thiophen-2-yl)phenyl) -5-(5-(5-(5-hexylthiophen-2-yl) thiophen-2-yl)thiophen-2-yl)-1,3,4-oxadiazole [TKO] has been characterised for the efficient detection of picric acid (PA) based on fluorescence quenching mechanism. In this regard, the electronic absorption spectra, fluorescence spectra, and fluorescence lifetime of TKO are recorded in the presence of different nitroaromatic compounds (NACs) in ethanol at room temperature. The absorption studies exhibited a blue shift in the absorption maxima with the increase in the concentration of PA. In the fluorescence titration studies, TKO shows a remarkable fluorescence quenching with picric acid as compared to other nitroaromatic compounds. Using the Benesi-Hildebrand plot, the binding constant value of PA with TKO is determined and is of the order of 6.467 104 M?1. Job's plot analysis confirms the 1:1 binding stoichiometry ratio between TKO and PA and is supported by the 1H NMR studies. The detection limit is determined and is of the order of 10.08 M. The competitive studies revealed that TKO is highly selective for recognizing PA without the interference of other NACs. The theoretical studies were also carried out to understand the binding mechanisms of PA with TKO. The fluorescence quenching of TKO by PA may be attributed to photo induced electron transfer (PET). Overall, the experimental findings suggest that, the novel probe TKO may be used as a highly selective and sensitive chemosensor for the detection of explosives like picric acid. 2022 Elsevier B.V. -
Phytofabricated bimetallic synthesis of silver-copper nanoparticles using Aerva lanata extract to evaluate their potential cytotoxic and antimicrobial activities
In this study, we demonstrate the green synthesis of bimetallic silver-copper nanoparticles (AgCu NPs) using Aerva lanata plant extract. These NPs possess diverse biological properties, including in vitro antioxidant, antibiofilm, and cytotoxic activities. The synthesis involves the reduction of silver nitrate and copper oxide salts mediated by the plant extract, resulting in the formation of crystalline AgCu NPs with a face-centered cubic structure. Characterization techniques confirm the presence of functional groups from the plant extract, acting as stabilizing and reducing agents. The synthesized NPs exhibit uniform-sized spherical morphology ranging from 7 to 12nm. They demonstrate significant antibacterial activity against Staphylococcus aureus and Pseudomonas aeruginosa, inhibiting extracellular polysaccharide secretion in a dose-dependent manner. The AgCu NPs also exhibit potent cytotoxic activity against cancerous HeLa cell lines, with an inhibitory concentration (IC50) of 17.63gmL?1. Additionally, they demonstrate strong antioxidant potential, including reducing capability and H2O2 radical scavenging activity, particularly at high concentrations (240gmL?1). Overall, these results emphasize the potential of A. lanata plant metabolite-driven NPs as effective agents against infectious diseases and cancer. 2024, The Author(s). -
Identification of potential ZIKV NS2B-NS3 protease inhibitors from Andrographis paniculata: An insilico approach
Andrographis paniculata is a widely used medicinal plant for treating a variety of human infections. The plant's bioactives have been shown to have a variety of biological activities in various studies, including potential antiviral, anticancer, and anti-inflammatory effects in a variety of experimental models. The present investigation identifies a potent antiviral compound from the phytochemicals of Andrographis paniculata against Zika virus using computational docking simulation. The ZIKV NS2B-NS3 protease, which is involved in viral replication, has been considered as a promising target for Zika virus drug development. The bioactives from Andrographis paniculata, along with standard drugs as control were screened for their binding energy using AutoDock 4.2 against the viral protein. Based on the higher binding affinity the phytocompounds Bisandrographolide A (-11.7), Andrographolide (-10.2) and Andrographiside (-9.7) have convenient interactions at the binding site of target protein (ZIKV NS2B-NS3 protease) in comparison with the control drug. In addition, using insilico tools, the selected high-scoring molecules were analysed for pharmacological properties such as ADME (Absorption, Distribution, Metabolism, and Excretion profile) and toxicity. Andrographolide was reported to have strong pharmacodynamics properties and target accuracy based on the Lipinski rule and lower binding energy. The selected bioactives showed lower AMES toxicity and has potent antiviral activity against zika virus targets. Further, MD simulation studies validated Bisandrographolide A & Andrographolide as a potential hit compound by exhibiting good binding with the target protein. The compounds exhibited good hydrogen bonds with ZIKV NS2B-NS3 protease. As a result, bioactives from the medicinal plant Andrographis paniculata can be studied in vitro and in vivo to develop an antiviral phytopharmaceutical for the successful treatment of zika virus. Communicated by Ramaswamy H. Sarma. 2021 Informa UK Limited, trading as Taylor & Francis Group. -
Effect of Mentha arvensis enriched diet to promote the growth and immune response of Clarias batrachus against Aeromonas hydrophila challenge
The study was conducted to investigate the effects of fish fed diet Mentha arvensis extract on growth performance, non-specific immunity and expression of some immune-related genes and resistance to Aeromonas hydrophila in Clarias batrachus. Five diets were formulated with 0, 1, 2, 3, and 4% of M. arvensis leaf extract. The results indicated that, compared to the control groups, 2-4% dietary inclusion increased growth and feed consumption. In the dietary inclusion of 3-4% M. arvensis extract groups were increased relative on weight gain, specific growth rate, RBC, WBC, total hemocyte counts, total protein, globulin than control. Fed diet supplements with 3% mint-extract increased the total protein, WBC and globulin and phagocytic indexes and lysozyme activity increased at the 2, 3 and 4% of mint groups relative to the control. The PCR analysis showed that TNF, IL-1, MyD88, and TLRs were increased in the 2-4% fed diet M. arvensis extract groups than the control. These results suggest that 3% of M. arvensis extract significantly influences the immunomodulatory activity and immune-specific genes of C. batrachus. 2024 The Authors -
Multitask EfficientNet affective computing for student engagement detection
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. -
Multimodal emotional analysis through hierarchical video summarization and face tracking
The era of video data has fascinated users into creating, processing, and manipulating videos for various applications. Voluminous video data requires higher computation power and processing time. In this work, a model is developed that can precisely acquire keyframes through hierarchical summarization and use the keyframes to detect faces and assess the emotional intent of the user. The key-frames are used to detect faces using recursive Viola-Jones algorithm and an emotional analysis for the faces extracted is conducted using an underlying architecture developed based on Deep Neural Networks (DNN). This work has significantly contributed in improving the accuracy of face detection and emotional analysis in non-redundant frames. The number of frames selected after summarization was less than 30% using the local minima extraction. The recursive routine introduced for face detection reduced false positives in all the video frames to lesser than 2%. The accuracy of emotional prediction on the faces acquired through the summarized frames, on Indian faces achieved a 90%. The computational requirement scaled down to 40% due to the hierarchical summarization that removed redundant frames and recursive face detection removed false localization of faces. The proposed model intends to emphasize the importance of keyframe detection and use them for facial emotional recognition. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
EMONET: A Cross Database Progressive Deep Network for Facial Expression Recognition
Recognizing facial features to detect emotions has always been an interesting topic for research in the field of Computer vision and cognitive emotional analysis. In this research a model to detect and classify emotions is explored, using Deep Convolutional Neural Networks (DCNN). This model intends to classify the primary emotions (Anger, Disgust, Fear, Happy, Sad, Surprise and Neutral) using progressive learning model for a Facial Expression Recognition (FER) System. The proposed model (EmoNet) is developed based on a linear growing-shrinking filter method that shows prominent extraction of robust features for learning and interprets emotional classification for an improved accuracy. EmoNet incorporates Progressive- Resizing (PR) of images to accommodate improved learning traits from emotional datasets by adding more image data for training and Validation which helped in improving the model's accuracy by 5%. Cross validations were carried out on the model, this enabled the model to be ready for testing on new data. EmoNet results signifies improved performance with respect to accuracy, precision and recall due to the incorporation of progressive learning Framework, Tuning Hyper parameters of the network, Image Augmentation and moderating generalization and Bias on the images. These parameters are compared with the existing models of Emotional analysis with the various datasets that are prominently available for research. The Methods, Image Data and the Fine-tuned model combinedly contributed in achieving 83.6%, 78.4%, 98.1% and 99.5% on FER2013, IMFDB, CK+ and JAFFE respectively. EmoNet has worked on four different datasets and achieved an overall accuracy of 90%. 2020. All Rights Reserved. -
Engagement Detection through Facial Emotional Recognition Using a Shallow Residual Convolutional Neural Networks
Online teaching and learning has recently turned out to be the order of the day, where majority of the learners undergo courses and trainings over the new environment. Learning through these platforms have created a requirement to understand if the learner is interested or not. Detecting engagement of the learners have sought increased attention to create learner centric models that can enhance the teaching and learning experience. The learner will over a period of time in the platform, tend to expose various emotions like engaged, bored, frustrated, confused, angry and other cues that can be classified as engaged or disengaged. This paper proposes in creating a Convolutional Neural Network (CNN) and enabling it with residual connections that can enhance the learning rate of the network and improve the classification on three Indian datasets that predominantly work on classroom engagement models. The proposed network performs well due to introduction of Residual learning that carries additional learning from the previous batch of layers into the next batch, Optimized Hyper Parametric (OHP) setting, increased dimensions of images for higher data abstraction and reduction of vanishing gradient problems resulting in managing overfitting issues. The Residual network introduced, consists of a shallow depth of 50 layers which has significantly produced an accuracy of 91.3% on ISED & iSAFE data while it achieves a 93.4% accuracy on the Daisee dataset. The average accuracy achieved by the classification network is 0.825 according to Cohens Kappa measure. 2020, Intelligent Engineering & System. All rights reserved. -
Effect of Gravity Modulation on the Onset of Ferroconvection in a Densely Packed Porous Layer /
IOSR Journal of Applied Physics, Vol.3, Issue 3, pp.30-40, ISSN No: 2278-4861.
The stability of a horizontal porous layer of a ferromagnetic fluid heated from below is studied when
the fluid layer is subject to a time-periodic body force.Modified Darcy law is used to describe the fluid motion.
The effect of gravity modulation is treated by a perturbation expansion in powers of the amplitude of
modulation. The stability of the system,characterized by a correction Rayleigh number,is determined as a
function of the frequency of modulation, magnetic parameters, and Vadasz number. -
Travails of New Mothers Returning to Work in Corporate India: A Phenomenological Study
A womans life is a myriad of experiences and none, perhaps, leaves a more lasting impression on her than motherhood. The child-birth event along with all its highs and lows not only has a deep psychological impact on her as a person but also impacts her career in many ways. Using interpretive phenomenological analysis, we have studied the lived experience of women who returned to work in corporate settings after maternity leave. Our study found that not only do they go through an emotional upheaval during this phase, but they also see a marked shift in the way they approach their careers. A womans natural instinct to mother her child comes in conflict with another natural (and equally important) desire to succeed in the workplace. Most women in our study experienced a stalling/break in their careers after childbirth and wished they had a mentor to assist them in transitioning back to office life. Besides trying to evaluate if childbirth was perceived as a threat or potential impediment to a high-flying career, we also explored how women were treated in their work environments, and whether their coworkers helped the women to cope during this phase. While the women in our study wanted to achieve success and satisfaction both within their families and careers, they found it most challenging to do so. 2022 Journal of International Womens Studies. -
DISTANCE SPECTRUM OF TWO FAMILIES OF GRAPHS
Let H1 and H2 be two copies of the complete graph Kn, n ? 3 with vertex sets V(H1) = {v1,v2...,vn} and V(H2) = {u1,u2,...,un}. Graph ?(n,p), 1 ? p ? n-1, is obtained from the union of graphs H1 and H2 by adding edges {uivi)|i ? {1, 2...,p}}. Graph ?(n) is obtained from the union of graphs H1 and H2 by joining each vertex vi of H1 to every vertex in {u1, u2, ..., un} \ {ui}, i = 1, 2, ..., n. The adjacency spectrum of ?(n, p) and ?(n) were determined in [9]. An open problem posed in [7] was to find families of graphs of diameter greater than two, for which the adjacency and distance spectrum are both integral. To answer the open problem, the distance spectrum of the above family of graphs is calculated, and new distance equienergetic graphs are constructed in this paper. 2024 Jangjeon Research Institute for Mathematical Sciences and Physics. All rights reserved. -
Spectrum of corona products based on splitting graphs
Let G be a simple undirected graph. Three new corona products of graphs based on splitting graph of G are defined. The adjacency spectra of the three new graphs based on splitting graph of G are determined. The number of spanning trees and the Kirchoff index of the new graphs are determined using their nonzero Laplacian eigenvalues. 2023 World Scientific Publishing Company. -
The vertex distance complement spectrum of subdivision vertex join and subdivision edge join of two regular graphs
The vertex distance complement (VDC) matrix C, of a connected graph G with vertex set consisting of n vertices, is a real symmetric matrix [cij ] that takes the value n ? dij where dij is the distance between the vertices vi and vj of G for i ? j and 0 otherwise. The vertex distance complement spectrum of the subdivision vertex join, G1 ??G 2 and the subdivision edge join G1 ?G2 of regular graphs G1 and G2 in terms of the adjacency spectrum are determined in this paper. 2021, Krasovskii Institute of Mathematics and Mechanics. All rights reserved. -
Preparation for parenthood programme: Experiences from southern India
Parenting skills are critically important to ensure that children are brought up in a safe environment. Recent evidence shows that studies of parenting skills are still at a preliminary stage in low-and middle-income countries. These need to involve family practitioners and religious groups who often play a major role in preparing young people in India. There are organized programmes available in the country for Christian adults to prepare themselves for marriage and family life through various church initiatives and activities. In order to develop a programme which can be used to prepare young parents for responsibilities of parenthood, a needs assessment was carried out among 70 young adults who attended a marriage preparation course in Bangalore, India. All the participants belonged to the Christian faith. Participants consisted of 53% men and 47% women whose average age for deciding to get married was 26.8 years. All of them expressed a need for such a preparatory programme for parenthood. They considered they needed to know about normal child development, behavioural management of children, to develop adequate skills in handling children at different ages, and deal with their own past issues with their own parents when they were being parented. The results suggest that the development of a preparatory programme for young adults to support them in the role of parenthood must take their views and needs into account. 2014 Institute of Psychiatry.