SPREADING RELIGION AND CULTURE THROUGH INTERNET MEMES
Digital Culture; Ethical Memes; Internet Memes; Participatory Culture; Religious Memes
This paper delves into the use of Internet memes as a means of spreading religion and ethical values in modern society. With the rise of the Internet and social media, memes have gained popularity as a form of shared content with the ability to convey meaningful messages. The research evaluates the prevalence and impact of memes in religious and ethical contexts. Through this research, the authors present their viewpoint after mulling over the findings of authors like Limor Shifman. The study asserts that memes can facilitate discussions, promote religious literacy, reinforce beliefs, and instil ethical values. Additionally, it anticipates that religious and ethical memes may influence individuals' actions in their daily lives. This research is significant as it examines the potential of memes to serve as a contemporary tool for spreading religion and ethics in a digital world. 2023 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (DVK, Bangalore),.
Journal of Dharma, Vol-48, No. 4, pp. 531-548.
Rathi N., CHRIST (DEEMED TO BE) University, Commerce Department, Bangalore, India; Jain P., CHRIST (Deemed to be University), Bangalore, India
Isothermal autocatalysis of homogeneousheterogeneous chemical reaction in the nanofluid flowing in a diverging channel in the presence of bioconvection
differential transformation method; diverging channel; Gyrotactic microorganisms; heterogeneous reactions; homogeneous reactions; quartic autocatalysis
The nonlinear differential equations play a prominent role in the mathematical description of many phenomena that occur in our world. A similar set of equations appear in this paper that govern the homogeneous and heterogeneous chemical reactions in the nanofluid flowing between two non-parallel walls. Since the concentration of the homogeneous species is substantially high, quartic autocatalysis is considered for the analysis. It is found to be more effective than the cubic autocatalysis. Further, to avoid the deposition of nanoparticles on the surface, self-propelled microorganisms called gyrotactic microorganisms are allowed to swim in the nanofluid. This movement of microorganisms constitutes a major phenomenon called bioconvection. The set of governing equations thus formed are made dimensionless and the resulting system of equations are solved by Differential Transformation Method (DTM) with the help of Padapproximant that reduces the power series into rational function. This transformation helps in achieving a better convergence rate. The fluid flow analysis is interpreted through graphs and tables where it is observed that the heat source enhances the temperature of the nanofluid. Further, the homogeneous and heterogeneous chemical reaction parameters have significant impacts on the concentration of the reactants. Also, the outcomes indicated that the reaction profiles and motile density profiles increase with the increase in Schmidt number. 2021 Informa UK Limited, trading as Taylor & Francis Group.
Waves in Random and Complex Media, Vol-34, No. 6, pp. 5438-5458.
Khan M.I., Laboratory of Systems Ecology and Sustainability Science, College of Engineering, Peking University, Beijing, China, Department of Mathematics and Statistics, Ripah International University, Islamabad, I-14, Pakistan; Puneeth V., Department of Mathematics, CHRIST (Deemed to be University), Bangalore, India
Nonlocal thermoelastic waves inside nanobeam resonator subject to various loadings
Kernel functions; memory influence term; nano-beam; non-local effect; ramp type heating; time-delay; time-dependent load
The present article focuses on the new meticulous model based on the postulate of memory-dependent derivatives to analyze the thermo-mechanical interactions inside the nano-beam-based machined resonators. Also, the size effect on dynamic responses of thermoelastic vibrations of homogeneous and isotropic nano-beam is considered. The fundamental expressions are formulated in the frame of non-local generalized thermoelasticity with paired relaxation times by operating the results of Euler-Bernoulli beam theory, non-local effect, and memory-dependent derivative. The proposed model is applied to study the nano-beam-based machined resonator subjected to the ramp-type heating and exponentially decaying time-dependent load. Closed-form solutions of the physical fields are examined by applying the Laplace transform mathematical mechanism. However, the coherence of the new thermal conductivity framework, a collation has been bestowed among the results obtained in the presence or absence of the memory-dependent derivative; also, the size effect is analyzed on the significant parameters of nano-beam such as deflection, temperature, displacement as well as bending moment. Moreover, the prominent influence of the distinct affecting parameters such as constituents of memory-dependent derivative (kernel function and time delay) and ramping time parameter with an applied load on the physical fields have been investigated with the help of quantitative results. 2022 Taylor & Francis Group, LLC.
Tiwari R.; Saeed A.M.; Abouelregal A.; Singhal A.; Salem M.G.
Mechanics Based Design of Structures and Machines, Vol-52, No. 1, pp. 215-238.
Tiwari R., Department of Mathematics, Nitishwar College, A constituent unit of Babasaheb Bhimrao Ambedkar Bihar University, Bihar, India; Saeed A.M., Department of Mathematics, College of Science, Qassim University, Buraydah, Saudi Arabia; Abouelregal A., Department of Mathematics, College of Science and Arts, Jouf University, Al-Qurayyat, Saudi Arabia, Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, Egypt; Singhal A., Department of Computational Sciences, School of Sciences, Christ (Deemed to be University) Delhi NCR, Uttar Pradesh, Ghaziabad, India; Salem M.G., Department of Mathematics, Faculty of Science, Mansoura University, Mansoura, Egypt
Development of the House of Collaborative Partnership to overcome supply chain disruptions: evidence from the textile industry in India
case study; House of Collaborative Partnership (HCP); supply chain collaboration; Supply chain disruptions; textile industry; total interpretive structural modelling
Collaboration in a supply chain becomes a significant competitive weapon for member firms in an uncertain business environment. The present study develops a model of supply chain collaboration named as House of Collaborative Partnership (HCP) and includes the enablers and impeders of a successful Collaborative Partnership (CP). Model development follows a three-phase process. The first phase consists of the identification of enablers and impeders of CP based on the literature review and experts opinions. The second phase applies Total Interpretive Structural Modelling (TISM) as a tool to construct hierarchical structures of the enablers and impeders of CP. The third phase deals with the development of HCP based on the hierarchical structures of enablers and impeders. The HCP is then validated with two case studies in the Indian textile industry. Eight enablers and seven impeders were identified in the first phase. After analyzing these factors with TISM, the HCP was developed consisting of four parts: Foundation, Columns, Beam, and Roof. The existence of trust, commitment to long-term collaboration, top management support, adequate financial support, ability to deal with technological changes, and providing regular training to employees constitute the HCP Foundation to achieve supply chain collaboration. The study concludes with the managerial implications of HCP to help supply chain partners in becoming resilient during an uncertain business environment. 2022 Informa UK Limited, trading as Taylor & Francis Group.
Patel B.S.; Nagariya R.; Singh R.K.; Sambasivan M.; Yadav D.K.; Vlachos I.P.
Production Planning and Control, Vol-35, No. 8, pp. 770-793.
Patel B.S., Thiagarajar School of Management, Madurai, India; Nagariya R., School of Business and Management, CHRIST (Deemed to be University), Delhi-NCR, Ghaziabad, India; Singh R.K., Management Development Institute (MDI), Gurgaon, India; Sambasivan M., Thiagarajar School of Management, Madurai, India; Yadav D.K., National Institute of Technology Calicut, Calicut, India; Vlachos I.P., Excelia Business School, La Rochelle, France
Reduction of a Tri-Modal Lorenz Model of Ferrofluid Convection to a CubicQuintic GinzburgLandau Equation Using the Center Manifold Theorem
Center manifold; Electroconvection; Ferrofluid convection; GinzburgLandau; GlukhovskyDolzhansky system; Lorenz model; Quintic
The differential geometric method of the center manifold theorem is applied to the magnetic-Lorenz model of ferrofluid convection in an electrically non-conducting ferrofluid. The analytically intractable tri-modal nonlinear autonomous system (magnetic-Lorenz model) is reduced to an analytically tractable uni-modal cubicquintic GinzburgLandau equation. The inadequacy of the cubic GinzburgLandau equation and the need for the cubicquintic one is shown in the paper. The heat transport is quantified using the solution of the cubicquintic equation and the effect of ferrofluid parameters on it is demonstrated. The stable and unstable regions in the conductive regime and the conductive-convective regime is depicted using a bifurcation diagram. The noticeable discrepancy between the results of the two models is highlighted and the quintic non-linearity effects are delineated. 2021, Foundation for Scientific Research and Technological Innovation.
Differential Equations and Dynamical Systems, Vol-32, No. 1, pp. 151-169.
Siddheshwar P.G., Department of Mathematics, CHRIST (Deemed to be University), Hosur Road Campus, Bengaluru, 560029, India; Sushma T.S., Department of Mathematics, B.N.M. Institute of Technology, Bengaluru, 560070, India, Department of Mathematics (VTU RC), CMR Institute of Technology, Bengaluru, India
Energy-Efficient Long-Range Sectored Antenna for Directional Sensor Network Applications
Cooja simulator; Directional sensor networks; Microstrip patch antenna; Radio frequency switchboard; Sectored antenna
The popularity of Directional Sensor Network (DSN) is increasing due to their improved transmission range, spectral reusability, interference mitigation, and energy efficiency. In this paper, the radio module of the DSN is implemented using an eight-sector antenna array. Two types of sectored antennas, namely the Rectangular Patch Sectored Antenna (RPSA) and the Triangular Patch Sectored Antenna (TPSA), are proposed to operate at frequency of 2.4 GHz ISM band. The RPSA has a half-power beamwidth (HPBW) of 45 and a peak gain of 5.2 dBi, while the TPSA has an HPBW of 48 and a peak gain of 4.16 dBi. The design and performance evaluation of RPSA and TPSA in terms of gain, reflection characteristics (|S11|), and HPBW are conducted using Ansys High Frequency Structure Simulator (HFSS) and Vector Network Analyzer (VNA). To demonstrate the concept, the fabricated sectored antennas are connected to MicaZ Wireless Sensor Network (WSN) nodes using an indigenously designed Single Pole 8 Throw (SP8T) Radio Frequency (RF) switchboard. The performance of the DSNs based on RPSA and TPSA is evaluated using the Cooja simulator and a testbed consisting of MicaZ nodes. The results show that RPSA outperforms TPSA and omnidirectional-based WSNs in terms of power consumption, received signal strength, and packet delivery ratio. 2024 IETE.
Nagaraju S.; Gudino L.J.; Kadam B.V.; Khairnar V.V.; Rodriguez J.X.; Ramesha C.K.
IETE Journal of Research, Vol-70, No. 5, pp. 4442-4452.
Nagaraju S., Department of Computer Science & Engineering, School of Engineering & Technology, Christ (Deemed to be University), Bengaluru, India; Gudino L.J., Department of Computer Science & Information Systems, BITS Pilani, Pilani, India; Kadam B.V., Department of Electrical & Electronics Engineering, BITS Pilani K. K. Birla Goa campus, Goa, India; Khairnar V.V., School of Electronics Engineering, VIT-AP University, Andhra Pradesh, Amaravati, India; Rodriguez J.X., Department of Electrical & Electronics Engineering, School of Engineering & Technology, Christ (Deemed to be University), Bengaluru, India; Ramesha C.K., Department of Electrical & Electronics Engineering, BITS Pilani K. K. Birla Goa campus, Goa, India
Analyzing the Inter-relationships of Business Recovery Challenges in the Manufacturing Industry: Implications for Post-pandemic Supply Chain Resilience
Business recovery challenges; Flexible policies; Manufacturing industry; Supply chain resilience
The COVID-19 pandemic brought about a rapid change in the global business environment, leading to increased risks of supply and demand disruptions. As society and the industry continue to acclimate to the new normal, the contributions of the manufacturing industry are critical in the recovery process. However, the existing literature lacks a framework to analyze the manufacturing sectors challenges during the recovery to enhance supply chain resilience (SCR). To address this gap, this study develops a framework for business recovery, especially in the manufacturing sector. A broad literature examination and expert survey were conducted to identify the critical potential business recovery challenges. Further, the interplay of business recovery challenges was analyzed using mixed methodologies such as total interpretive structure model and the cross-impact matrix multiplication applied to classification (MICMAC) to foster a framework that can assist the manufacturing industry in improving SCR. The study found that challenges like lack of flexible policies for handling disruptions and lack of management support toward building resilience have the highest driving power impeding business recovery. Other challenges, such as lack of reconfiguring production lines, lack of product competencies to meet disturbances, and less adoption of robust technologies are also identified as major challenges. The implications of the study offer valuable insights into global manufacturing industries. It also has significant propositions for the Pacific region. The Pacific region faces unique challenges, including geographic isolation, resource dependency, diverse economies, climate vulnerabilities, and complex trade relationships. The suggested frameworks adaptability and applicability to these regional characteristics enable businesses and policymakers in the Pacific to better understand and address the specific dynamics of post-pandemic recovery, ultimately contributing to enhanced SCR tailored to the regions needs. The study enriches the existing SCR literature by analyzing inter-relationships between business recovery challenges in the manufacturing industrys post-pandemic context. The Author(s) under exclusive licence to Global Institute of Flexible Systems Management 2024.
Dwivedi A.; Srivastava S.; Agrawal D.; Jha A.; Paul S.K.
Global Journal of Flexible Systems Management, Vol-24, pp. 31-48.
Dwivedi A., Jindal Global Business School, O.P. Jindal Global University, Sonipat, India; Srivastava S., Operations and Decision Sciences, Christ University, Bangalore, India; Agrawal D., Institute of Management Technology, Ghaziabad, India; Jha A., Decision Sciences, Jaipuria Institute of Management, Lucknow, India; Paul S.K., UTS Business School, University of Technology Sydney, Sydney, Australia
Automatic Diagnosis of Autism Spectrum Disorder Detection Using a Hybrid Feature Selection Model with Graph Convolution Network
ABFO; Autism spectrum disorder; Graph convolutional networks; mRMR; SVM-RFE
A neurodevelopmental disorder is called an autism spectrum disorder (ASD) that influences a persons assertion, interaction, and learning abilities. The consequences and severity of symptoms of ASD will vary from person to person; the disorder is mainly diagnosed in children aged 15years and older, and its symptoms may include unusual behaviors, interests, and social challenges. If it is not resolved at this stage, it will become severe in the coming days. So, in this manuscript, we propose a way to automatically tell if someone has ASD that works well by using a combination of feature selection and deep learning. Four phases comprise the proposed model: preprocessing, feature extraction, feature selection, and prediction. At first, the collected images are given to the preprocessing stage to remove the noise. Then, for each image, three types of features are extracted: the shape feature, texture feature, and histogram feature. Then, optimal features are selected to minimize computational complexity and time consumption using a new technique based on a combination of adaptive bacterial foraging optimization (ABFO), support vector machines-recursive feature elimination (SVM-RFE), minimum redundancy and maximum relevance (mRMR). Then, the graph convolutional network (GCN) classifier uses the selected features to identify an image as normal or autistic. According to the research observations, our models accuracy is enhanced to 97.512%. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
https://doi.org/10.1007/s42979-023-02439-z https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180210216&doi=10.1007%2fs42979-023-02439-z&partnerID=40&md5=b01c9cae8e1de2938bc2945e56cff620
Lamani M.R., CHRIST (Deemed to be University), Computer Science and Engineering, Karnataka, Bangalore, 560074, India; Benadit P.J., CHRIST (Deemed to be University), Computer Science and Engineering, Karnataka, Bangalore, 560074, India
What drives the wheels of evolution in NGC 1512?: A UVIT study
Galaxies: interactions; Galaxies: spiral; Galaxies: star formation
Context. Environmental and secular processes play a pivotal role in the evolution of galaxies. These can be external processes such as interactions or internal processes linked to the action of bar, bulge, and spiral structures. Ongoing star formation in spiral galaxies can be affected by these processes. By studying the star formation progression in the galaxy, we can gain insights into the role of different processes that regulate the overall evolution of a galaxy. Aims. The ongoing interaction between the barred-spiral galaxy NGC 1512 and its satellite NGC 1510 offers an opportunity to inves- tigate how galactic interactions and the presence of a galactic bar influence the evolution of NGC 1512. We aim to understand the recent star formation activity in the galaxy pair and thus gain insight into the evolution of NGC 1512. Methods. The UltraViolet Imaging Telescope (UVIT) on board AstroSat enables us to characterise the star-forming regions in the galaxy with a superior spatial resolution of ?85 pc in the galaxy rest frame. We identified and characterised 175 star-forming regions in the UVIT far-ultraviolet (FUV) image of NGC 1512 and correlated with the neutral hydrogen (Hi) distribution. Extinction correc- tion was applied to the estimated photometric magnitude. We traced the star-forming spiral arms of the galaxy and studied the star formation properties across the galaxy in detail. Results. We detect localised regions of star-formation enhancement and distortions in the galactic disc. We find this to be consistent with the distribution of Hi in the galaxy. This is evidence of past and ongoing interactions affecting the star formation properties of the galaxy. We studied the properties of the inner ring. We find that the regions of the inner ring show maximum star-formation-rate density (log(SFRDmean[M yr?1 kpc?2]) ? ?1.7) near the major axis of the bar, hinting at a possible crowding effect in these regions. The region of the bar in the galaxy is also depleted of UV emission. This absence suggests that the galactic bar may have played an active role in the redistribution of gas and quenching of star formation inside the identified bar region. We therefore suggest that both secular and environmental factors might be influencing the evolution of NGC 1512. The Authors 2023.
Robin T.; Kartha S.S.; Ujjwal K.; Saha K.; Parekh V.; George K.; Mathew B.
All Open Access; Green Open Access; Hybrid Gold Open Access
Robin T., Department of Physics and Electronics, Christ (Deemed to Be University), Hosur Main Road, Bangalore, India; Kartha S.S., Department of Physics and Electronics, Christ (Deemed to Be University), Hosur Main Road, Bangalore, India; Ujjwal K., Department of Physics and Electronics, Christ (Deemed to Be University), Hosur Main Road, Bangalore, India; Saha K., Inter-University Centre for Astronomy and Astrophysics, Post Bag 4, Ganeshkhind, Pune, 411007, India; Parekh V., National Radio Astronomy Observatory (NRAO), 1003 Lopezville Rd, Socorro, 87801, NM, United States; George K., Faculty of Physics, Ludwig-Maximilians-Universit, Scheinerstr 1, Munich, 81679, Germany; Mathew B., Department of Physics and Electronics, Christ (Deemed to Be University), Hosur Main Road, Bangalore, India
The evaluation of the electrochemical properties of Co3O4 nanopowders synthesized by autocombustion and solgel methods
Autocombustion; Co3O4; CV; Cycle stability; GCD; Solgel
The present investigation involves two synthesis methods, autocombustion (Co3O4-AC) and solgel (Co3O4-SG), for producing nearly spherical-shaped and polygonal shaped nanomaterials of spinel cobalt oxide (Co3O4) respectively as electrode materials. TEM image analysis unveiled distinct particle morphologies for the two samples. The Co3O4-AC particles exhibited a nearly spherical shape, whereas the Co3O4-SG particles displayed a polygonal shape. The phase purity of the Co3O4 samples were confirmed via XRD patterns analysis and the crystallite size was calculated to be 44nm for Co3O4-AC and 36nm for Co3O4-SG. The surface area, estimated via BET experiments, of Co3O4-AC was found to be 15m2/g, while Co3O4-SG exhibited a slightly lower surface area of 11m2/g. Co3O4-AC exhibited a higher specific capacitance (Cs) of 162F/g at 0.25A/g, indicating its superior energy storage capability. On the other hand, Co3O4-SG shows a Cs of 98F/g, indicating slightly lower performance compared to Co3O4-AC. Both nanomaterials exhibited better stability, with more than 85% capacity retention after 5000 chargedischarge cycles. 2023, The Author(s), under exclusive licence to the Institute of Chemistry, Slovak Academy of Sciences.
Kalpana S.; Bhat V.S.; Hegde G.; Prabhu T.N.; Anantharamaiah P.N.
Chemical Papers, Vol-78, No. 1, pp. 343-356.
Springer Science and Business Media Deutschland GmbH
https://doi.org/10.1007/s11696-023-03093-8 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173115955&doi=10.1007%2fs11696-023-03093-8&partnerID=40&md5=b6e94d6eca83c32baa0d97bc7086a2ff
Kalpana S., Department of Chemistry, Faculty of Mathematical and Physical Sciences, M. S. Ramaiah University of Applied Sciences, Bangalore, 560058, India; Bhat V.S., Centre for Nano-Materials and Displays (CND), B. M. S. College of Engineering, Bull Temple Road, Bangalore, 560019, India; Hegde G., Department of Chemistry, CHRIST (Deemed to Be University), Hosur RD, Bhavani Nagar, S. G. Palya, Bangalore, 560029, India; Prabhu T.N., Department of Chemistry, Faculty of Mathematical and Physical Sciences, M. S. Ramaiah University of Applied Sciences, Bangalore, 560058, India; Anantharamaiah P.N., Department of Chemistry, Faculty of Mathematical and Physical Sciences, M. S. Ramaiah University of Applied Sciences, Bangalore, 560058, India
Media's influence on suicide: Building a safer online world for all
Media; Psychiatry; Social Media; Suicide
In 1999, World Health Organization (WHO) initiated a global campaign focused on suicide prevention. In collaboration with International Association for Suicide Prevention, WHO compiled recommendations and resources intended to educate various societal and groups with the potential to impact suicide prevention, and this included the media. In order to combat the alarmingly high incidence of suicides (Tandon and Nathani, 2018), it is imperative to institute guidelines outlining how the social media forums ought to disseminate altruistic, essential educational content while. This work is a step toward achieving the same by laying down guidelines that could potentially reduce the suicide rate. 2023 Elsevier B.V.
https://doi.org/10.1016/j.ajp.2023.103868 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85180563467&doi=10.1016%2fj.ajp.2023.103868&partnerID=40&md5=a1fa1109bdf227e00fdae206f344a67a
Mukku L., CHRIST (Deemed to be University), Kengeri Campus, Bangalore, 560065, India; Thomas J., CHRIST (Deemed to be University), Kengeri Campus, Bangalore, 560065, India
An Enhanced Approximation Algorithm Using Red Black Tree and HashMap for Virtual Machine Placement Problem
Approximation algorithm; Cloud computing; HashMap; Red Black Tree; Virtual machine; Virtual machine placement
The virtual machine placement problem (VMPP) is an np-hard optimization problem in cloud computing that involves efficiently allocating virtual machines (VMs) to physical hosts in such a way that the resource wastage is minimized, and resource usage is optimal while ensuring adequate performance. This paper proposes a modified best-fit approximation algorithm using Red Black Tree (RBT) and HashMap for addressing the VMPP with enhanced computational efficiency in such a way that the active hosts in a given data center remains minimum possible. The proposed algorithm builds up on the existing best-fit approximation algorithm by using RBT and HashMap. The proposed approach considers various attributes such as CPU utilization, memory requirements, and network bandwidth while allocating virtual machines. To evaluate the performance the simulation is done in cloudsim environment with PlanetLab workload. Test cases are considered in both homogeneous and heterogeneous environments and results are taken. Comparative analyses were performed against existing benchmark algorithms in terms of time complexity and resource usage in terms of active hosts. The results demonstrate that the proposed algorithm outperforms the existing algorithms and guarantees time complexity of O(log n) and give better results compared to other algorithms. 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
John R.R.; Kanaga E.G.M.; Lovesum J.; Sandeep S.G.
https://doi.org/10.1007/s42979-023-02465-x https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181458050&doi=10.1007%2fs42979-023-02465-x&partnerID=40&md5=8c6d509ecfebac92eac0e92f42798d16
John R.R., Karunya Institute of Technology and Sciences, Tamil Nadu, Coimbatore, India; Kanaga E.G.M., Karunya Institute of Technology and Sciences, Tamil Nadu, Coimbatore, India; Lovesum J., Christ (Deemed to be University), Karnataka, Bangalore, India; Sandeep S.G., Karunya Institute of Technology and Sciences, Tamil Nadu, Coimbatore, India
DES J024008.08-551047.5: A new member of the polar ring galaxy family
Galaxies: individual: DES J024008.08-551047.5; Galaxies: peculiar; Galaxies: photometry; Galaxies: structure
Aims. This study presents the discovery of a new polar ring galaxy (PRG) candidate and highlights its unique features and characteristics. We provide evidence from photometric analysis that supports the inclusion of galaxy DES J024008.08-551047.5 (DJ0240) in the PRG catalogue. Methods. During the visual observations of optical imaging data obtained from the Dark Energy Camera Legacy Survey, a serendipitous discovery was made of a ringed galaxy, DJ0240. We conducted a one-dimensional isophotal analysis to determine the position angle of the ring component and its relative orientation to the host galaxy. A two-dimensional GALFIT analysis was performed to confirm the orthogonal nature of the ring galaxy and identify distinct components within the host galaxy. We compared the photometric properties of the host and ring components of DJ0240 with PRGs and other ring-type galaxies (RTGs), finding that DJ0240 shares similar properties with both of these galaxy types. Results. We have discovered the galaxy DJ0240, a PRG candidate with a ring component positioned almost perpendicular to the host galaxy. The position angles of the ring and host components are ?80 and ?10, respectively, indicating that they are nearly orthogonal to each other. The extension of the ring component is three times greater than that of the host galaxy and shows a distinct colour separation, being bluer than the host. The estimated g-r colour values of the host and ring components are 0.86 0.02 and 0.59 0.10 mag, respectively. The colour value of the ring component is similar to those of typical spiral galaxies. The host galaxy's colour and the presence of a bulge and disc components indicate that the host galaxy may be lenticular. Our findings reveal a subtle yet noticeable colour difference between the host and ring components of PRGs and RTGs. We observe that both the host and ring components of DJ0240 align more closely with PRGs than with RTGs. Furthermore, we compared the Sersic index values of the ring component (nring) of galaxy DJ0240 with a selected sample of PRGs and Hoag-type galaxies. The results show that DJ0240 has a remarkably low nring value of 0.13, supporting the galaxy's classification as a PRG. Hence, we suggest that the ring galaxy DJ0240 is a highly promising candidate for inclusion in the family of PRGs. 2024 The Authors.
Akhil K.R.; Kartha S.S.; Mathew B.; Ujjwal K.; Ezhikode S.H.; Robin T.
Akhil K.R., Department of Physics and Electronics, CHRIST (Deemed to be University), Bangalore, 560029, India; Kartha S.S., Department of Physics and Electronics, CHRIST (Deemed to be University), Bangalore, 560029, India; Mathew B., Department of Physics and Electronics, CHRIST (Deemed to be University), Bangalore, 560029, India; Ujjwal K., Department of Physics and Electronics, CHRIST (Deemed to be University), Bangalore, 560029, India; Ezhikode S.H., Department of Physics and Electronics, CHRIST (Deemed to be University), Bangalore, 560029, India; Robin T., Department of Physics and Electronics, CHRIST (Deemed to be University), Bangalore, 560029, India
Exploratory Architectures Analysis of Various Pre-trained Image Classification Models for Deep Learning
deep learning; image classification; neural network
The image classification is one of the significant applications in the area of Deep Learning (DL) with respective to various sectors. Different types of neural network architectures are available to perform the image classification and each of which produces the different accuracy. The dataset and the features used are influence the outcome of the model. The research community is working towards the generalized model at least to the domain specific. On this gesture the contemporary survey of various Deep Learning models is identified using knowledge information management methods to move further to provide optimal architecture and also to generalized Deep Learning model to classify images narrow down to the sector specific. The study systematically presents the different types of architecture, its variants, layers and parameters used for each version of Deep Learning model. Domain specific applications and limitations of the type of architecture are detailed. It helps the researchers to select appropriate Deep Learning architecture for specific sector. 2024 by the authors.
Journal of Advances in Information Technology, Vol-15, No. 1, pp. 66-78.
https://doi.org/10.12720/jait.15.1.66-78 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183348264&doi=10.12720%2fjait.15.1.66-78&partnerID=40&md5=835724298bb9f8c31d2fdd872094d8e3
Deepa S., Department of Computer Science, Christ University, Bangalore, India; Zeema J.L., Department of Computer Science, Christ University, Bangalore, India; Gokila S., Department of Computer Applications, Hindustan Institute of Technology and Science, Chennai, India
In silico analysis of NHP2 membrane protein, a novel vaccine candidate present in the RD7 region of Mycobacterium tuberculosis
Gene ontology; Molecular docking; Molecular dynamics simulation; Mycobacterium tuberculosis; NHP2; PFAM; TLR3; Vaccine
Mycobacterium tuberculosis, the etiological agent of tuberculosis, is one of the trickiest pathogens. We have only a few protective shields, like the BCG vaccine against the pathogen, which itself has poor efficacy in preventing adult tuberculosis. Even though different vaccine trials for an alternative vaccine have been conducted, those studies have not shown much promising results. In the current study, advanced computational technology was used to study the potential of a novel hypothetical mycobacterial protein, identified by subtractive hybridization, to be a vaccine candidate. NHP2 (Novel Hypothetical Protein 2), housed in the RD7 region of the clinical strains of M. tuberculosis, was studied for its physical, chemical, immunological and structural properties using different computational tools. PFAM studies and Gene ontology studies depicted NHP2 protein to be functionally active with a possible antibiotic binding domain too. Different computational tools used to assess the toxicity, allergenicity and antigenicity of the protein indicated its antigenic nature. Immune Epitope Database (IEDB) tools were used to study the T and B cell determinants of the protein. The 3D structure of the protein was designed, refined and authenticated using bioinformatics tools. The validated tertiary structure of theprotein was docked against the TLR3 immune receptor to study the binding affinity and docking scores. Molecular dynamic simulation of the protein-protein complex formed were studied. NHP2 was found to activate host immune response against tubercle bacillus and could be explored as a potential vaccine in the fight against tuberculosis. 2023, The Author(s), under exclusive licence to Plant Science and Biodiversity Centre, Slovak Academy of Sciences (SAS), Institute of Zoology, Slovak Academy of Sciences (SAS), Institute of Molecular Biology, Slovak Academy of Sciences (SAS).
Kootery K.P., Department of Life Sciences, CHRIST (Deemed to Be University), 29, Bangalore, India; Sarojini S., Department of Life Sciences, CHRIST (Deemed to Be University), 29, Bangalore, India
Detecting Fake Information Dissemination using Leveraging Machine Learning and DRIMUX with B-LSTM
Accuracy; B-LSTM; DRIMUX; Fake Information Dissemination Detection; Machine Learning
Information integrity and public confidence are seriously threatened by the rapid expansion of fake news and misinformation that has resulted from the online broadcast of information. This work focuses on the detection of fraudulent information propagation utilizing machine learning techniques and the Digital Reputation and Influence Measurement Unit (DRIMUX) in order to address this problem. The use of Bidirectional Long Short-Term Memory (B-LSTM) networks into the detection process is something we really advocate. B-LSTM enables the capture of contextual dependencies from both past and future time steps, enhancing the understanding of sequential data. Additionally, DRIMUX provides reputation and influence measurements to assess the credibility of information sources. Experimental analyses on various datasets reveal the promising performance of the suggested methodology, highlighting its potential in preventing the spread of false information and protecting the veracity of digital information. 2024, Ismail Saritas. All rights reserved.
Kaneti V.R.; Venkatesan M.; Sharma H.; Bodhankar A.; Bajaj R.; Ganesh Kumar R.; Pokhariya H.S.; Deepak A.
International Journal of Intelligent Systems and Applications in Engineering, Vol-12, No. 5s, pp. 91-100.
Kaneti V.R., Department of Computer Science and Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Telangana, Hyderabad, India; Venkatesan M., Dr. D.Y. Patil Institute of Technology, Pune-18, Pimpri, India; Sharma H., Computer Engineering and Applications, GLA University, Mathura, India; Bodhankar A., Department of Decision Sciences, Dr. Ambedkar Institute of Management Studies and Research, Maharashtra, India; Bajaj R., NB Global University, Rajasthan, Bikaner, India; Ganesh Kumar R., Department of Computer Science and Engineering, CHRIST (Deemed to be University), School of Engineering and Technology, Kanminike, Bangalore, India; Pokhariya H.S., Department of Computer Science & Engineering, Graphic Era Deemed to be University, Uttarakhand, Dehradun, India; Deepak A., Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Tamilnadu, Chennai, India
An analysis of factors associated with employee satisfaction in information technology companies
(AMOS); Analysis of Moment Structure; job commitment; Job satisfaction; Performance and employee; Science (SPSS); Statistical Package for Social; teamwork; turnover; Work-life balance
BACKGROUND AND OBJECTIVES: An employees satisfaction and performance are linked to the companys work discipline, personal factors, and organizational culture. This paper studies these three factors in the context of Information Technology companies and their connection to employee satisfaction. Job satisfaction is a significant issue in Information Technology Companies, leading to increased labour turnover in Information Technology Companies. The study highlights the relevance of Information Technology companies to understanding the reasons behind their employees satisfaction. Until now, little is known concerning the variants of job satisfaction among Information Technology employees, enriching the understanding in this particular professional area. The study was conducted to assess the job satisfaction needs of the employees in major Information Technology companies. The study helps to know the preferences and problems of the employees. METHODS: In this study, data was collected from employees from various Information Technology companies to uncover the factors that impact the satisfaction of employees. Considering the studys goal and the literature review, the technique was analytical and interpretive. Due to large populations random sampling method is convenient for the study. The studys objectives were achieved explicitly via the questionnaires design. To test the proposed hypotheses, all data were processed using the Structural Equation Modelling, Statistical Package for Social Science (SPSS) and Analysis of Moment Structures. FINDINGS: Information Technology companies need their employees to feel satisfied to achieve the overall objectives and remain loyal to the company to achieve company success. From the responses, we learned that 31% of the respondents were satisfied with their employer about the various allowances and benefits they receive. Also, we knew that around 50% of the respondents were happy with their choice of the company because of its future commitments. 102 of the respondents highly disagreed that they were satisfied with the attitude and nature of their employees. Also, 22.26% of the male respondents have said they are only sometimes motivated to go to work. The limitation of this study was that the collected data was only of the general employees of the Indian Information Technology companies and not to specific departments of those companies. Also, no categories of companies were defined as per turnover. CONCLUSION: By recognizing the importance of job satisfaction, managers can create an environment that motivates and engages employees, leading to better performance, increased productivity and reduced employee turnover 2024 Tehran Urban Research and Planning Center. All Rights Reserved.
International Journal of Human Capital in Urban Management, Vol-9, No. 1, pp. 135-156.
Tehran Urban Research and Planning Center
https://doi.org/10.22034/IJHCUM.2024.01.10 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181878393&doi=10.22034%2fIJHCUM.2024.01.10&partnerID=40&md5=232f3500ebf0c4224546b919a3db38a6
Singhal D., School of Business and Management, Christ (Deemed to be University), Bangalore, India; Salunkhe H.A., School of Business and Management, Christ (Deemed to be University), Bangalore, India
Impact of Globalization and Multinational Corporations on Farmer Suicides in India: An Overview, Effects, Strategies and Policies
Globalization; MNCs; privatization of the seed sector; unsustainable agriculture
The tragic rise of farmer suicides in India brought to light some of the high social and ecological costs associated with globalization and unsustainable agriculture. The study analyzes the impact of globalization and MNCs on farmer suicides and suggests strategies and policies. The crucial findings show regressive agricultural policies, output declines, insufficient credit support, private parties intervention, land fragmentation, and the high cost of cultivation due to the privatization of the seed sector that led to worst debt traps among other factors as major contributors to this turmoil. This research underlines the ongoing efforts in understanding and tackling these issues. 2023 Taylor & Francis Group, LLC.
International Trade Journal, Vol-38, No. 1, pp. 99-112.
Jahnavi A.G., Department of Economics and Analytics, Christ (Deemed to be University), Bangalore, India; Dastagiri M.B., Department of Research Systems Division, ICAR-National Academy of Agriculture Research Management, Hyderabad, India
Facilitating Faculty Development for Training in Multicultural Competence in Health Service Psychology Graduate Programs Through an International Collaboration
cultural competence; Faculty professional development; international collaboration
A critical aspect of strengthening graduate-level clinical and counseling psychology training in cultural competence is to build capacity among faculty teaching in these programs to provide effective training. We addressed this need through an international collaboration between a university in India and another in the United States that included faculty travel to another country, peer mentoring groups, and review of curricula. This article describes faculty perceptions of this program and its perceived impact on their professional development and outlines curricular and research outcomes that resulted from the program. Across 4 years, a total of eight faculty visits took place with Indian faculty (n = 13) visiting the United States and US faculty (n = 11) visiting India. After each visit, faculty at both institutions responded to open-ended questions about the usefulness of these visits and completed a rating scale at the end of the program through an online survey. Faculty from both countries indicated that the visits contributed to enhanced cultural awareness and sensitivity by broadening their perspectives and learning about cultural similarities and differences. Indian faculty described learning about new pedagogical methods and enhanced motivation to engage in research and publish, along with new collaborative opportunities. US faculty described incorporating cultural competence more centrally in their teaching and clinical supervision through increased commitment, as well as inclusion of more global and diverse content and assignments aimed to increase students cultural competence. These responses provide preliminary support for the usefulness of cultural immersion experiences for faculty professional development. 2024 American Psychological Association Inc.. All rights reserved.
Gopal B.; Burlew A.K.; D'Souza G.; George T.S.; Varghese K.J.; Raval V.V.
International Perspectives in Psychology: Research, Practice, Consultation, Vol-13, No. 1, pp. 24-39.
Gopal B., Department of Psychology, CHRIST (Deemed to Be) University, Hosur Road Bengaluru, Karnataka, 560029, India; Burlew A.K., Department of Psychology, University of Cincinnati, OH, United States; D'Souza G., Department of Psychology, CHRIST (Deemed to Be) University, Hosur Road Bengaluru, Karnataka, 560029, India; George T.S., Department of Psychology, CHRIST (Deemed to Be) University, Hosur Road Bengaluru, Karnataka, 560029, India; Varghese K.J., Department of Psychology, CHRIST (Deemed to Be) University, Hosur Road Bengaluru, Karnataka, 560029, India; Raval V.V., Department of Psychology, Miami University, Oxford, OH, United States
A Novel Artificial Intelligence System for the Prediction of Interstitial Lung Diseases
Deep learning; HRCT; ILD; Multi-label classification; Transfer learning
Interstitial lung disease (ILD) encompasses a spectrum of more than 200 fatal lung disorders affecting the interstitium, contributing to substantial mortality rates. The intricate process of diagnosing ILDs is compounded by their diverse symptomatology and resemblance to other pulmonary conditions. High-resolution computed tomography (HRCT) assumes the role of the primary diagnostic tool for ILD, playing a pivotal role in the medical landscape. In response, this study introduces a computational framework powered by artificial intelligence (AI) to support medical professionals in the identification and classification of ILD from HRCT images. Our dataset comprises 3045 HRCT images sourced from distinct patient cases. The proposed framework presents a novel approach to predicting ILD categories using a two-tier ensemble strategy that integrates outcomes from convolutional neural networks (CNNs), transfer learning, and machine learning (ML) models. This approach outperforms existing methods when evaluated on previously unseen data. Initially, ML models, including Logistic Regression, BayesNet, Stochastic Gradient Descent (SGD), RandomForest, and J48, are deployed to detect ILD based on statistical measures derived from HRCT images. Notably, the J48 model achieves a notable accuracy of 93.08%, with the diagnostic significance of diagonal-wise standard deviation emphasized through feature analysis. Further refinement is achieved through the application of Marker-controlled Watershed Transformation Segmentation and Morphological Masking techniques to HRCT images, elevating accuracy to 95.73% with the J48 model. The computational framework also embraces deep learning techniques, introducing three innovative CNN models that achieve test accuracies of 94.08%, 92.04%, and 93.72%. Additionally, we evaluate five full-training and transfer learning models (InceptionV3, VGG16, MobileNetV2, VGG19, and ResNet50), with the InceptionV3 model achieving peak accuracy at 78.41% for full training and 92.48% for transfer learning. In the concluding phase, a soft-voting ensemble mechanism amplifies training outcomes, yielding ensemble test accuracies of 76.56% for full-training models and 92.81% for transfer learning models. Notably, the ensemble comprising the three newly introduced CNN models attains the pinnacle of test accuracy at 97.42%. This research is poised to drive advancements in ILD diagnosis, presenting a resilient computational framework that enhances accuracy and ultimately betters patient outcomes within the medical domain. 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
Raju N., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Augustine D.P., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India; Chandra J., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, India