Browse Items (16488 total)
Sort by:
-
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. -
An in-Depth Analysis on the Cumulative Effect of Co and Sintering Temperatures on the Formaldehyde Sensing Attributes of NiO
In-depth studies are availing to explore and utilize the sensing attributes of p-type NiO nanostructures. However, the surface functionalization of NiO using Co for gas sensing along with varying temperature profile is a novel attempt till date. The research succeeded in synthesizing pure and substituted NiO via co-precipitation route and assessed the sensing capability of the samples by testing with 10 different target gases. The Co doped NiO sintered at 500C exhibited promising sensing performance within a concentration range of 1100ppm, notably achieving a high response of 7817 for 100ppm HCHO at room temperature. The proposed sensor demonstrated rapid response and recovery times (9s and 8s), and it successfully passed stability tests conducted over a 30-day period and repeatability tests consisting of eight cycles. The work paved a way to the implication of the prepared sensor as a breath analyzer to detect lung cancer due to its appreciable formaldehyde sensing characteristics. Graphical Abstract: (Figure presented.) The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Modelling of Cointegration with Students T-errors
Two or more non-stationary time series are said to be co-integrated if a certain linear combination of them be-comes stationary. Identification of co-integrating relationships among the relevant time series helps the researchers to develop efficient forecasting methods. The classical approach of analyzing such series is to express the co-integrating time series in the form of error correction models with Gaussian errors. However, the modeling and analysis of cointegration in the presence of non-normal errors needs to be developed as most of the real time series in the field of finance and economics deviates from the assumption of normality. This paper focuses on modeling of a bivariate cointegration with a students-t distributed error. The co-integrating vector obtained from the error correction equation is estimated using the method of maximum likelihood. A unit root test of first order non stationary process with students t-errors is also defined. The resulting estimators are used to construct test procedures for testing the unit root and cointegration associated with two time series. The likelihood equations are all solved using numerical approaches because the estimating equations do not have an explicit solution. A simulation study is carried out to illustrate the finite sample properties of the model. The simulation experiments show that the estimates perform reasonably well. The applicability of the model is illustrated by analyzing the data on time series of Bombay stock exchange indices and crude oil prices and found that the proposed model is a good fit for the data sets. 2022 by authors, all rights reserved. -
Impact of Variable Distributed Generation on Distribution System Voltage Stability
With advances in renewable energy (RE)technologies and the promotion of restructuring, distributed energy (DG)sources play a vital role in today's power sector. From the technical and economic point of view, DG sources provide a no of benefits such as lesser system losses, better system voltage profile and lower line congestion. The aim of this work is to determine the voltage stability of a distribution system at different levels of DG compensation determined as a percentage of the total load on the system. The objective function is formulated to minimize the real power loss. At first, the locations are chosen based on strategy using Loss Sensitivity Factors (LSF)and the optimal sizing of multiple units of DG sources is optimized using Particle Swarm Optimization (PSO)algorithm. The simulations are performed on standard IEEE 33-bus and 69-bus test systems and the results validate the importance of placing appropriately sized DG sources at suitable locations to achieve improved voltage stability and reduced distribution losses. 2019 IEEE. -
Teaching Learning-Based Optimization with Learning EnthusiasmMechanism for Optimal Control of PV Inverters in Utility Grids for Techno-Economic Goals
This study presents the optimal placement and operation of distributed generation (DG) sources in a distribution system embedded with utility-owned DG sources. Cost minimization and technical improvement of the network are the key objectives of the distribution company (DisCo). With the increasing popularity for renewable energy sources, DisCos are installing their own DGs to fulfill their electricity demand partially. When DisCos are the DG owners, the technical and economic considerations overlap. A novel method is proposed in this paper based on the recent variant of the teaching learning-based optimization (TLBO) algorithm and learning enthusiasm-based TLBO (LebTLBO) to optimize locations, sizes, and operational power factors of DGs in a distribution system with DisCo-owned DGs. A multi-objective function to improve voltage stability, reduce distribution losses, and reduce energy costs has been considered for solving the problem. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Optimal DG Planning and Operation for Enhancing Cost Effectiveness of Reactive Power Purchase
The demand for reactive power support from distributed generation (DG) sources has become increasingly necessary due to the growing penetration of DG in the distribution network. Photovoltaic (PV) systems, fuel cells, micro-turbines, and other inverter-based devices can generate reactive power. While maximizing profits by selling as much electricity as possible to the distribution companies (DisCos) is the main motive for the DG owners, technical parameters like voltage stability, voltage profile and distribution losses are of primary concern to the (DisCos). Local voltage regulation can reduce system losses, improve voltage stability and thereby improve efficiency and reliability of the system. Participating in reactive power compensation reduces the revenue generating active power from DG, thereby reducing DG owners profits. Payment for reactive power is therefore being looked at as a possibility in recent times. Optimal power factor (pf) of operation of DG becomes significant in this scenario. The study in this paper is presented in two parts. The first part proposes a novel method for determining optimal sizes and locations of distributed generation in a radial distribution network. The method proposed is based on the recent optimization algorithm, TeachingLearning-Based Optimization with Learning Enthusiasm Mechanism (LebTLBO). The effectiveness of the method has been compared with existing methods in the literature. The second part deals with the determination of optimal pf of operation of DG sources to minimize reactive power cost, reduce distribution losses and improve voltage stability. The approachs effectiveness has been tested with IEEE 33 and 69 bus radial distribution systems. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Optimal Load Control for Economic Energy Equilibrium in Smart Grid Using Adaptive Inertia Weight Teaching-Learning-Based Optimization
Due to numerous operational restrictions and economic purposes, optimal load management for energy balance in the smart grid (SG) is one of the compensating responsibilities. This research provides a novel multiobjective optimization technique for attaining energy balance in SG, with the goal of avoiding fines due to excessive upstream network power extraction beyond contractual demand. Due to a lack of capacity to create the whole optimization towards the global optimum after each run, optimal load control (OLC) is a prevalent challenge. Adaptive-TLBO, the most recent variation of Teaching Learning Based Optimization (TLBO), comprises both alterations during the exploitation and exploration phases (ATLBO). Because the ATLBO is used on a modified IEEE 33-bus system, the results obtained in this mode are extraordinary. The energy balance has improved in addition to the enhancement of the voltage profile and the reduction of distribution losses. As evidenced by comparisons with PSO, basic TLBO, backtracking search algorithm (BSA), and cuckoo search algorithms, the suggested ATLBO algorithm has precedence over any other proposed algorithm (CSA) 2022, International Journal of Intelligent Engineering and Systems.All Rights Reserved. -
An adaptive inertia weight teachinglearning-based optimization for optimal energy balance in microgrid considering islanded conditions
The energy balance in islanded microgrids is a complex task due to various operational constraints. This paper proposes a new approach to multi-objective optimization for achieving energy balance in aMicrogrid(MG) in both islanded and normal modes. Optimal load control (OLC)is achallenge, due to a lack of capacity to generate the global optimum after each run. The latest variant of Teaching Learning Based Optimization (TLBO), known as Adaptive-TLBO, includes both modifications during exploitation and exploration stages (ATLBO). The results achievedwith the proposed method are exceptional on a modified IEEE 33-bus system. In addition to the improvement of the voltage profile and the decrease of the distribution losses, the energy balance improves with the method. The proposed ATLBO algorithm overrides any proposed other algorithm, as shown by comparison with PSO, base TLBO, Backtrackingsearch algorithm (BSA) and cuckoo search algorithms, etc. (CSA). The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. -
Informal Waste Recycling in Dharavi A Marxist Feminist Reading of The Women of Wasteland
[No abstract available]