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Internet of things based financial data managing device in bank /
Patent Number: 359269-001, Applicant: Sapna Bisht. -
A critical study on role of social media in Delhi state elections 2013 & 2015 /
Social media is often hailed as an instrument of digital democracy and social change. This perception is deeply rooted in the well acknowledged potential of ICT’s as ‘agents for development and empowerment .It has liberated people from the tyranny of the free flow of information and ideas. The Delhi elections held in 2013 and 2015 made a revolutionary change in India’s political equations. It shows the emergence of a new party Aam AadmiPart, Aam Aadmi Party and its victory. -
Social media campus party politics and political participation
Political Participation is a desired quality among citizens in a democracy. Earlier studies have indicated that Political Knowledge, Political Efficacy and Civic Engagement enhance the Political Participation of college students. The current study has its background in two key aspects in the Indian context: First, the proliferation of Social Media Usage among newlinecollege students in the last decade; Second, the implementation of the Lyngdoh Commission Report (May 2006) on Campus Party Politics. newlineStudies have not adequately addressed the question of the direct impact of Social Media Usage among college students, on their level of Political Knowledge, Political Efficacy, Civic Engagement, and Political newlineParticipation. The present study attempts to assess the impact of Social Media Usage on Political Knowledge, Political Efficacy and Civic Engagement and Political Participation of college students in the context of Kerala. In the context of the implementation of the Lyngdoh Commission report by some of the college and University campuses in India thus banning Party Politics in their campuses, while some of the other campuses still retaining Campus Party Politics, the newlinepresence/absence of Campus Party Politics is used as a control variable to see the impact of Campus Party Politics on Social Media Usage, Political Knowledge, Political Efficacy, Civic Engagement, and Political newlineParticipation. To check the impact of various factors on Political Participation, a path model is framed and analysed using Structural newlineEquation Modelling. Key demographic variables such as gender, political student union membership, type of management that own and operate the college, the stream of study of students, etc are also examined to see its newlinevarying effects on the key variables under consideration. The results indicate that Social Media Usage and Political Knowledge do not impact Political Participation while Political Efficacy and Civic Engagement do newlineimpact Political Participation. -
Social media, campus party politics and political participation
Political Participation is a desired quality among citizens in a democracy. Earlier studies have indicated that Political Knowledge, Political Efficacyand Civic Engagement enhance the Political Participation of college students. The current study has its background in two key aspects in the Indian context: First, the proliferation of Social Media Usage among college students in the last decade; Second, the implementation of the
Lyngdoh Commission Report (May 2006) on Campus Party Politics. Studies have not adequately addressed the question of the direct impact of Social Media Usage among college students, on their level of Political Knowledge, Political Efficacy, Civic Engagement, and Political Participation. The present study attempts to assess the impact of Social Media Usage on Political Knowledge, Political Efficacy and Civic Engagement and Political Participation of college students in the context of Kerala. In the context of the implementation of the Lyngdoh Commission report by some of the college and University campuses inIndia thus banning Party Politics in their campuses, while some of the other campuses still retaining Campus Party Politics, the presence/absence of Campus Party Politics is used as a control variable to see the impact of Campus Party Politics on Social Media Usage, Political Knowledge, Political Efficacy, Civic Engagement, and Political Participation. -
Growth and Characterization of Sb2Se3 and SnSe2 Crystals for Photovoltaic Applications
Tremendous development in crystal growth technology led to the production of good newlinequality samples for the design and fabrication of optoelectronic devices. As naturally available solids exhibit undesirable characteristics, the present research work deals with the artificial synthesis and characterization of defect free binary layered chalcogenide materials newline(LCMs) for photovoltaic (PV) applications. Antimony selenide (Sb2Se3) and tin diselenide newline(SnSe2) have gained special attention in the PV industry due to their eco-friendly, sustainable, and non-hazardous nature as well as the salient features such as moderate melting temperature, p-type conductivity with direct transition, optimum band gap and high newlineabsorption coefficient. Therefore, cost-effective synthesis was implemented to engineer bulk Sb2Se3 and SnSe2 crystals for the enhancement of optoelectronic parameters. Single crystal growth from melt allows the fabrication of large size samples under controlled environment. It gives rise to complexities in maintaining stable temperature for crystallization and newlineachieving chemical homogeneity, if multiple elements are present in the system. The newlinechallenges associated with Bridgman-Stockbarger and Czochralski methods for preparing bulk crystals include irregular heat flow, mechanical movement of furnace or crucible, thermal stress, etc. Moreover, reactivity of the melted material with the ampoule leads to structural irregularities. Hence, horizontal normal freezing (HNF), the facile and inexpensive melt growth technique was employed to explore the suitability of cleaved samples. Most of the vapor phase synthesis methods, especially, the chemical vapor deposition (CVD) deteriorates material quality, which adversely affects the physical properties due to the presence of contamination or foreign elements. But, the physical vapor deposition (PVD) process is favorable as it offers feasible instrumentation and yields stoichiometric specimens with supreme quality and fine-tuned characteristics. -
Optical Properties of Magnetic Quantum Dots
The delta-like dispersion of the density of states (DoS) enable quantum dots (QD) to display optical and electronic properties comparable with those of real atoms. The discrete electronic structure of QDs akin to that of atoms is formed due to the effect of quantum confinement (QCE). In the case of magnetic quantum dots (MQD), the QDs are incorporated with magnetic impurities such as Mn atom, rare earth elements etc., by which the QDs undergo significant changes in optical and electronic properties by lifting their degeneracies (Zeeman effect). The combination of fluorescent and magnetic entities opens up opportunities for synthesizing two-in-one nanocomposites beneficial for multi-functional, multi-targeting, and multi-theranostic tools. Optical properties of QDs consisting of magnetic impurities, such as the absorption coefficient, oscillator strength, and refractive index are discussed in this chapter. 2023 selection and editorial matter, Amin Reza Rajabzadeh, Seshasai Srinivasan, Poushali Das, and Sayan Ganguly. -
Mulberry Leaves (Morus Rubra)-Derived Blue-Emissive Carbon Dots Fed to Silkworms to Produce Augmented Silk Applicable for the Ratiometric Detection of Dopamine
Silk fibers (SF) reeled from silkworms are constituted by natural proteins, and their characteristic structural features render them applicable as materials for textiles and packaging. Modification of SF with functional materials can facilitate their applications in additional areas. In this work, the preparation of functional SF embedded with carbon dots (CD) is reported through the direct feeding of a CD-modified diet to silkworms. Fluorescent and mechanically robust SFare obtained from silkworms (Bombyx mori) that are fed on CDs synthesized from the Morus rubra variant of mulberry leaves (MB-CDs). MB-CDs are introduced to silkworms from the third instar by spraying them on the silkworm feed, the mulberry leaves. MB-CDs are synthesized hydrothermally without adding surface passivating agents and are observed to have a quantum yield of 22%. With sizes of ?4nm, MB-CDs exhibited blue fluorescence, and they can be used as efficient fluorophores to detect Dopamine (DA) up to the limit of 4.39nM. The nanostructures and physical characteristics of SF weren't altered when the SF are infused with MB-CDs. Also, a novel DA sensing application based on fluorescence with the MB-CD incorporated SF is demonstrated. 2023 Wiley-VCH GmbH. -
Doping and Surface Modification of Carbon Quantum Dots for Enhanced Functionalities and Related Applications
Carbon quantum dots (CQDs) are a unique class of 0D nanomaterials, featured by a graphitic core and shell layers saturated with hydrogen atoms and functional groups. CQDs are prepared through top-down and bottom-up strategies from natural and synthetic precursors. CQDs can be modified through chemical (e.g., surface functionalization/passivation, doping, etc.) and physical (e.g., coreshell architecture, composite material blending, etc.) strategies to control their properties. This review highlights the effect of such modifications on the photophysical properties of CQDs, such as photoluminescence (PL), absorbance, and relaxivity. The dependence of PL upon the size, orientation at the edges, surface and edge functionalization, doping, excitation wavelength, concentration, pH, aggregate formation, etc., are summarized along with the supporting theoretical evidence available in the literature. Also, this review outlines the recent advancements, and future prospective of optical (e.g., sensing, bioimaging, and fluorescent ink) and catalytic applications (e.g., photocatalysis and electrocatalysis) of CQDs enhanced through physical and chemical modifications of their structure and composition. 2021 Wiley-VCH GmbH -
Surface functionalized fluorescent carbon nanoparticles and their applications
Fluorescent carbon nanoparticles or carbon dots (CDs) are zero-dimensional nanomaterials embodying physicochemical characteristics appropriate for novel and improved applications in various disciplines. Tunable photoluminescence, photostability, small size, low cost, biocompatibility, etc., are some of the promising features of CDs. The CDs are usually composed of a graphitic core surrounded by shell layers containing various functional groups. Surface functionalization of CDs is known to customize, and regulate the properties of CDs, thereby proliferating their applications. A variety of physical and chemical methods have been used for the preparation of CDs with tailored surfaces. The choice of the synthetic strategy generally depends on the type of surface modification required and the fluorescence behavior expected. This chapter summarizes and discusses the existing strategies for preparing surface functionalized CDs and the resultant fluorescence phenomena. The surface functionalization of CDs can decisively influence their suitability in several applications. In some applications, surface functionalization improves the existing utility, while novel utilities are emerging in others. The influence of surface functionalities of CDs on biomedical and catalytic applications has been discussed in detail in this chapter. CDs have emerged as a promising material for enhancing the performance, sustainability, and safety of various energy storage devices like batteries, supercapacitors etc. Continued research and development in this area could lead to the realization of more efficient and environmentally friendly energy storage solutions. The chapter concludes by discussing the challenges in synthesizing surface functionalized CDs and their acceptability in biomedical and industrial applications. 2025 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
Development and Psychometric Validation of Teachers Receptivity to Change Scale
In this article, we report the development and psychometric validation of the Teachers Receptivity to Change Scale (TRCS). The sample included secondary school teachers of Kerala, India. In India, the teachers receptivity to change becomes important in the context of the newly drafted National Education Policy, (2020) which places teachers at the center of the reforms. The present study proceeded through five phases namely item analysis, exploratory factor analysis, confirmatory factor analysis, validation of the scale, and testretest reliability. The development of the tool started with the generation of a pool of items followed by item analysis. The exploratory factor analysis extracted four factors and the confirmatory factor analysis confirmed the four-factors namely individual, organizational, educational, and bridging factors. The structural equation modelling established the four-correlated factor construct of teachers receptivity to change and an additive model indexing teachers receptivity to change as the sum of the four factors. Both the model fit indices indicated an excellent fit. The validity of the TRCS established by correlating the teachers receptivity to change and its factors with multidimensional work motivation scale and engaged teachers scale indicated a moderate correlation. The final 28 item TRCS showed adequate internal consistency (Cronbachs alpha = 0.897) and discriminant validity. The test re-test reliability analysis (Cronbachs alpha = 0.884) confirmed the temporal stability of the scale. The findings recommend a psychometric reliable and valid scale for assessing teachers receptivity to change with implications for teachers, researchers, and policy makers. De La Salle University 2023. -
An Analysis on the Reasons for Students Opting Tourism as a Course with Reference to Bangalore
Contemporary Research in India, Vol-3 (3), pp. 133-142. ISSN-2231-2137 -
Visitor Satisfaction of Muziris Heritage Site in Kerala
Global Interdisciplinary Business-Economics Advancement Conference, pp. 883-888. ISSN-2333-4207 -
Young adults socialization in housing and real estate purchase decisions in India
Purpose: The purpose of this paper is to understand the influence of young adults socialization and product involvement on family housing and real estate purchase decision-making process. While previous studies have used these constructs in the fast-moving commercial goods category, this paper is considering the real estate family purchase decision as the core point of research and analysis. Design/methodology/approach: Data were collected from 429 young working adults across various sectors in India. The proposed conceptual framework is tested using structural equation modeling. Findings: The findings suggest that the teenagers with high social life have a better say in the decision-making process. It was also found that the young adults product involvement (measured in terms of gratification and symbol) construct shows how involved they are with the final decision-making in a family. The results suggested that the more young adult socializes, the more voice he has in the family housing and real estate decision-making process. Originality/value: This paper is the first to analyze the role of teenage socialization and product involvement on family housing and real estate purchase decision-making process. This paper will be practicable to all the stakeholders of the housing industry as a whole. 2020, Emerald Publishing Limited. -
Removal of Occlusion in Face Images Using PIX2PIX Technique for Face Recognition
Occlusion of face images is a serious problem encountered by the researchers working in different areas. Occluded face creates a hindrance in extracting the features thereby exploits the face recognition systems. Level of complexity increases with changing gestures, different poses, and expression. Occlusion of the face is one of the seldom touched areas. In this paper, an attempt is made to recover face images from occlusion using deep learning techniques. Pix2pix a condition generative adversarial network is used for image recovery. This method is used for the translation of one image to another by converting an occluded image to a non-occluded image. Webface-OCC dataset is used for experimentation, and the efficacy of the proposed method is demonstrated. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
3D face reconstruction techniques: Passive methods
In the recent literature, 3D face reconstruction received wide interest and has become one of the significant areas of research. 3D face reconstruction provides in depth details on geometrics, texture and color of the face, which are utilized in different applications. It supports a multitude of applications, ranging from face recognition and surveillance to medical imaging, gaming, animation, and virtual reality. This paper attempts to consolidate the research works that have happened in the history of 3D face reconstruction. Also, we try to classify the existing methods based on the input for the process. The databases used in the recent works are discussed and the performance evaluation of methods on different databases is analyzed. The challenges addressed in recent studies are mainly focused on the faster reconstruction of 3D Images, improved accuracy of reconstructed images, human pose identification, image reproduction with higher resolution. Researchers have also tried to address occlusion related problems. Passive methods, used by different researchers are analyzed and their effects on different parameters are discussed in this work. Finally, possible future areas for improvement in terms of reconstructions are presented for the benefit of researchers. BEIESP. -
An Objective Evaluation of Harris Corner and FAST Feature Extraction Techniques for 3D Reconstruction of Face in Forensic Investigation
3d reconstructed face images are the volumetric data from two dimensions, it can provide geometric information, which is very helpful for different application like facial recognition, forensic analysis, animation. Reconstructed face images can provide better visualization, than a two dimensional image can provide. For a proper 3d reconstruction one of primary step is feature extraction. The objective of this study is to conduct a comprehensive evaluation of two prominent traditional feature extraction techniques, namely Harris Corner and FAST (Features from Accelerated Segment Test), for the purpose of 3D reconstruction of face images in forensic analysis. In this research paper feature extraction was carried out using the Harris corner detection and FAST Feature technique. 3D reconstruction is completed using the retrieved features. In this study a comparative analysis was conducted assessing the aspect ratio, depth resolution. The results of the assessment provide valuable insights into the strengths and limitations of both techniques, aiding researchers and practitioners in selecting the most suitable method for 3D face image reconstruction applications. 2023, Ismail Saritas. All rights reserved. -
A HYBRID APPROACH FOR LANDMARK DETECTION OF 3D FACES FOR FORENSIC INVESTIGATION
Facial landmark detection is a key technology in many forensic applications, such as facial identification and facial reconstruction. However, the accuracy of facial landmark detection is often limited in 3D face images due to the challenges of occlusion, illumination, and pose variations. This paper proposes a hybrid approach for landmark detection of 3D faces for forensic investigation. A hybrid method of edge contour detection and Harris corner detection is proposed for feature extraction in face images for forensic investigation. Edge contour detection is used to detect the boundaries of the face, while Harris corner detection is used to detect the corners. The advantage of using a hybrid method of edge contour detection and Harris corner detection for feature extraction in face images is that it can capture both global and local features of the face. Edge contour detection can capture global features, such as the overall shape and outline of the face, while Harris corner detection can capture local features, such as the corners of the mouth, nose and eyes which are vital for facial reconstruction. Experimental results show that the proposed method outperforms existing landmark detection algorithms in terms of time complexity and minimum loss. 2023 Little Lion Scientific. -
Lightweight Model for Occlusion Removal from Face Images
In the realm of deep learning, the prevalence of models with large number of parameters poses a significant challenge for low computation device. Critical influence of model size, primarily governed by weight parameters in shaping the computational demands of the occlusion removal process. Recognizing the computational burdens associated with existing occlusion removal algorithms, characterized by their propensity for substantial computational resources and large model sizes, we advocate for a paradigm shift towards solutions conducive to low-computation environments. Existing occlusion riddance techniques typically demand substantial computational resources and storage capacity. To support real-time applications, it's imperative to deploy trained models on resource-constrained devices like handheld devices and internet of things (IoT) devices possess limited memory and computational capabilities. There arises a critical need to compress and accelerate these models for deployment on resource-constrained devices, without compromising significantly on model accuracy. Our study introduces a significant contribution in the form of a compressed model designed specifically for addressing occlusion in face images for low computation devices. We perform dynamic quantization technique by reducing the weights of the Pix2pix generator model. The trained model is then compressed, which significantly reduces its size and execution time. The proposed model, is lightweight, due to storage space requirement reduced drastically with significant improvement in the execution time. The performance of the proposed method has been compared with other state of the art methods in terms of PSNR and SSIM. Hence the proposed lightweight model is more suitable for the real time applications with less computational cost. 2024 by the author(s). -
3D Face Reconstruction with Feature Enhancement using Bi-FPN for Forensic Analysis
The representation of facial features in three-dimensional space plays a pivotal role in various applications such as facial recognition, virtual reality, and digital entertainment. However, achieving high-fidelity reconstructions from two-dimensional facial images remains a challenging task, particularly in preserving fine texture details. This research addresses this problem by proposing a novel approach that leverages a combination of advanced techniques, including Resnet, Flame model, Bi-FPN, and a differential render architecture. The primary objective of this study is to enhance texture details in reconstructed 3D facial images. The integration of Bi-FPN (Bi-directional Feature Pyramid Network) enhances feature extraction and fusion across multiple scales, facilitating the preservation of texture details across different regions of the face. The objective is to accurately represent facial features from 2D images in three-dimensional space. By combining these methods, the proposed framework achieves significant improvements in preserving fine texture details and overall facial structure. Experimental results demonstrate the effectiveness of the approach, suggesting its potential for various applications such as virtual try-on and facial animation. 2024 The Authors. -
An Enhanced Approximation Algorithm Using Red Black Tree and HashMap for Virtual Machine Placement Problem
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.