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Research Initiative on Sustainable Education System: Model of Balancing Green Computing and ICT in Quality Education
Green Computing Practices (GCP) convey the revolutionary changes of the modern education system. The education system is transforming into a hybrid mode of operations in effective teaching and learning procedure. In the modern era, computer devices are playing a foremost role in performing ICT based teaching and learning (ICT-BTL). The GCP and ICT-BTL are the creative and innovative practices that can ensure the eco-friendly enactment and safeguard from various harmful environmental impacts. The motive of projecting the present research outcome is to address the impact of GCP on ICT-BTL activities. The creative and innovative practices of ICT-BTL support the implementation of GCP towards a sustainable education system. A sustainable education system interconnects the teachers, learners, institutions, and industrial experts through eco-friendly electronic and computer devices that ensure maximum efficiency in education with minimum environmental impacts. 2022 IEEE. -
E-Development and Sustainable Management Education for Effective Leadership and Sustainable Society
Electronic development is the process of systematic evolution for mankind and society at large that ensures the overall progress of the electronic mode of learning, education, healthcare, society, and corporate governance. The main objective of the chapter was to address the impacts of e-development and sustainable management education for effective leadership that leads to constructing a sustainable society. The required data were collected both from primary and secondary sources. Primary data were collected from 120 respondents. The secondary data sources included official websites. The study is empirical and various statistical tools like mean, standard deviation, and t-test were executed for data analysis. The results of the research study were indicated the high degree and low degree of contribution from e-development and sustainable management education are not significant between effective leadership and sustainable society. E-development can be effective for creating a sustainable society with the goal setting of improving effective leadership skills. Copyright 2022, IGI Global. -
Level of green computing based management practices for digital revolution and new india
The reality is staring us in the form of global warming, climate changes and air-quality degradation. This reality constitutes an increasing zone on the strategic front. These strategic changes need necessarily to be responded through employees of an organization. Against this backdrop, the Green Information Technology and Green HRM have emerged as a sequel to rapid degradation of our planet due to human activities. Therefore, incorporating the environmentally friendly practices through IT practices, recruitment, training and performance management functions constitute important components of Green IT and HRM. Green information technology is the revolutionary initiatives especially for human resources management practices that lead to digital life towards sustainable society. Keeping this practical and emergent context in view, the present study makes an attempt to develop a framework for assessing the level of green HRM practices actually prevailing in Indian organizations. The requisite data were collected from original sources and clarified with existing sources. The results of the study led to the inference that Information Technology and HRM practices of promoting individual performance needs fine-tuning because any green initiative has necessarily to be a collective exercise by all concerned. BEIESP. -
Impact of blended education system on outcome-based learning and sector skills development
An effective education system transforms the teaching and learning process into innovative idea generation and independent working ability. A blended education system is the representation of effective education that connects the teachers, students, and educational institutions for content development, delivery of effective teaching methods, and choice-based learning. The motive for initiating the research work was to address the demand for outcome-based learning in society that can fulfill the sector-wise human resource requirements and sector skill development. A blended education system helps to design effective courses and degrees with the capacity of choosing subjects, lectures, and teachers either in online or offline mode of education. The system may also assist in preparing the learning pattern like classroom-based learning, internship-based learning, or learning through project works. The researchers identified the dependent and independent variables with the help of expert opinion. The questionnaire was designed with all relevant questions based on the variables and refined through a pilot study. The research outcomes are described by understanding the nature of quantitative data using statistical tools like frequency distribution, t-test, and ANOVA test with the connectivity of qualitative data and the reality of social issues. 2023 IEEE. -
The future of business management with the power of distributed systems and computing
Distributed systems and computing have emerged as key technologies for businesses seeking to improve their operations, decision-making, and customer experience. In this chapter, we examine the potential of distributed systems and computing for the future of business management. We analyze the key characteristics and advantages of distributed systems and computing for business management, including improved scalability, flexibility, and availability, as well as increased efficiency and reduced costs. We also discuss the various applications of distributed systems and computing in business management, including inventory management, supply chain optimization, customer relationship management, financial management and accounting, data analytics, decision-making, and collaboration and communication. We also explore the emerging trends and technologies in distributed systems and computing, including edge computing, blockchain, and artificial intelligence, and their potential implications for the future of business management. Furthermore, we discuss the opportunities for research and innovation in the field of distributed systems and computing, including the development of new algorithms and protocols, the exploration of novel applications, and the investigation of the social and ethical implications of distributed systems and computing. In conclusion, distributed systems and computing offer a powerful set of tools for businesses seeking to enhance their performance and competitiveness in the digital age. However, the adoption of distributed systems and computing also entails a range of challenges and risks that must be addressed through careful planning and management. By embracing the potential of distributed systems and computing while also addressing its challenges, businesses can position themselves for success in the increasingly interconnected and digital world of the future. 2024 John Wiley & Sons Ltd. All rights reserved. -
GASP XXIII: A Jellyfish Galaxy as an Astrophysical Laboratory of the Baryonic Cycle
With MUSE, Chandra, VLA, ALMA, and UVIT data from the GASP program, we study the multiphase baryonic components in a jellyfish galaxy (JW100) with a stellar mass 3.2 1011 M o hosting an active galactic nucleus (AGN). We present its spectacular extraplanar tails of ionized and molecular gas, UV stellar light, and X-ray and radio continuum emission. This galaxy represents an excellent laboratory to study the interplay between different gas phases and star formation and the influence of gas stripping, gas heating, and AGNs. We analyze the physical origin of the emission at different wavelengths in the tail, in particular in situ star formation (related to H?, CO, and UV emission), synchrotron emission from relativistic electrons (producing the radio continuum), and heating of the stripped interstellar medium (ISM; responsible for the X-ray emission). We show the similarities and differences of the spatial distributions of ionized gas, molecular gas, and UV light and argue that the mismatch on small scales (1 kpc) is due to different stages of the star formation process. We present the relation H?-X-ray surface brightness, which is steeper for star-forming regions than for diffuse ionized gas regions with a high [O i]/H? ratio. We propose that ISM heating due to interaction with the intracluster medium (either for mixing, thermal conduction, or shocks) is responsible for the X-ray tail, observed [O i] excess, and lack of star formation in the northern part of the tail. We also report the tentative discovery in the tail of the most distant (and among the brightest) currently known ULX, a pointlike ultraluminous X-ray source commonly originating in a binary stellar system powered by either an intermediate-mass black hole or a magnetized neutron star. 2019. The American Astronomical Society. All rights reserved. -
Aza-Michael addition of 1,2-diazoles to structurally diverse enones: Efficient methods toward ?-amino ketones
An efficient and mild protocol was realized using 1,2-diazoles and related heterocycles with cyclic and acyclic enones in presence of T3P (2,4,6-tripropyl-1,3,5,2,4,6-trioxatriphosphorinane-2,4,6-trioxide) toward the regioselective formation of N-cycloalkyl heterocycles at room temperature. The developed reaction conditions showcased good selectivity over a wide range of 1,2-diazoles and enones by delivering N-cycloalkyl heterocycles in excellent yields. 2020 Wiley Periodicals LLC. -
P(III)-Mediated Cascade C-N/C-S Bond Formation: A Protocol towards the Synthesis of N,S-Heterocycles and Spiro Compounds
A P(III)-mediated entry towards construction of C?N/C?S bond has been devised. The developed heterocyclization method was exercised for the synthesis of a diverse range of N,S-heterocycles and related spiro molecules. P(NMe2)3 revealed the maximum efficacies under the aerobic reaction conditions and a spectrum of bis-nucleophiles, and isothiocyanates were tolerated well to serve the access of manifold immense molecules. (Figure presented.). 2020 Wiley-VCH GmbH -
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Synthesis of Thiazines, Thiazinones and N-Cycloalkyl Azoles Via Novel Synthetic Routes
Heterocyclic building blocks have gained the utmost importance in recent past on the newlineaccount of their significance in biological and pharmaceutical fields. Among these newlinenitrogen and sulphur containing heterocyclic building blocks such as thiazines, newlinethiazinones and N-cycloalkyl heterocyclic motifs hold an important role in medicinal newlinechemistry. Thiazine cores are used for the treatment of various life threating diseases newlinelike cancer, cardiovascular and fabry diseases. Drugs containing thiazinone cores were used to treat Parkinson, Alzheimer s and various neuro degenerative diseases. Ncycloalkyl azole motifs are used to treat various life threating cancers like neck, lung, prostate, breast etc. As per the literature review, synthesis of these motifs are done using multi steps and harsh conditions, which limited the substrate scope. In this thesis we describe our studies on development of one pot, mild condition for newlinesynthesis of thiazinone cores using P(NMe2)3 (HMPT). We had developed HMPT [P(NMe2)3] mediated reactions towards synthesis of Carbon-Nitrogen/Carbon-Sulphur bond. The developed methodology was extended for thiazine cores as well. This new synthetic methodology is useful to synthesize various N, S-heterocycles including the novel spiro molecules. HMPT reagent under the mild and aerobic reaction conditions provided the access for many manifold immense molecules. Molecular docking studies were conducted for the synthesized compounds considering MOA-B inhibitors as target. MOA-B inhibitor motifs were approved for the treatment of Parkinson illness. The synthesized thiazine and thiazinone molecules showed good binding affinity in molecular docking studies conducted. We had developed a new strategy using T3P (1-Propanephosphonic anhydride, ~ 50 % wt, in EA solution) mediated synthetic procedure for the synthesis of N-cycloalkyl newlineazoles. -
Predicting nitrous oxide contaminants in Cauvery basin using region-based convolutional neural network
Nitrous oxide (N2O) in riverbeds affects hydrological processes by contributing to the greenhouse effect, indicating poor water quality, disrupting biogeochemical cycling, and linking to eutrophication. Elevated N2O levels signal environmental issues, impacting aquatic life and necessitating precise forecasting for effective environmental management and reduced greenhouse gas emissions. Precisely forecasting nitrous oxide (N2O) emissions from riverbeds is paramount for effective environmental management, given its significant potency as a greenhouse gas. This study focuses on the difficulties related to spatial feature extraction and modeling accuracy in predicting N2O in riverbeds in Tamil Nadu. To address the obstacles, the research suggests utilizing the Deep Learning Based Prediction of Nitrous Oxide Contaminants (DL-PNOC), which studies the N2O contaminants in water using Region-based Convolutional Neural Network (RCNN) for spatial feature extraction, to predict nitrous oxide contaminants. The study is centered on the Cauvery River Basin located in Tamil Nadu, where the emission of N2O is a matter of environment. The outcomes encompass the specialized N2O contaminant model for riverbeds and the implementation of RCNN achieves precise N2O forecasting. The DL-PNOC approach combines a contaminant model with RCNN deep learning techniques to capture spatial characteristics and predict N2O pollutants accurately. Furthermore, using the River Bed Dynamics Simulator reinforces the dependability of the findings. The DL-PNOC approach has exhibited encouraging results, as evidenced by the following metrics: a high IoU of 88.66%, precision of 88.96%, recall of 90.03%, F1 score of 89.22%, and low RMSE and MAE values of 9.14% and 7.59%, respectively. The findings highlight the efficacy of the DL-PNOC approach in precisely forecasting N2O pollutants in river sediments. 2024 Elsevier B.V. -
Discovering the Micro-Clusters from a group of DHH learners: An approach using machine learning techniques
The e-learning environment is essentially helpful for improving the autonomous learning skills of the DHH learners. Facing numerous resources online, DHH learners need support to choose the right learning materials. This can be done by recommending suitable learning objects to similar types of learners. Hence, this research attempts to explore the possibilities of forming micro clusters from the group of DHH learners to improve the recommendation. As a result of k-means, three different micro clusters are formed. So, from the initial analysis, it is identified that the formation of micro clusters is possible, and features such as communication and learning ways play an important role in forming the well-defined micro clusters. This will definitely help the teachers in traditional classrooms and recommendation engines in e-learning to explore the micro clusters of learners with same learning patterns and communication preferences to appropriately stream the right pedagogical methods. 2024, IGI Global. All rights reserved. -
Unsupervised Learning for Understanding Diversity: Applying Feature Engineering and Cluster Analysis to Deaf and Hard of Hearing Data
As e-Learning emerges as a promising tool for instruction delivery, personalizing the e-Learning platform for DHH learners will benefit them to improve their learning engagement and educational attainment. This study aims to collect and analyze the different features unique to DHH learners and analyze the significant features among them. This study highlights the importance of addressing the diversity among DHH learners, while creating a personalized learning environment for them. With this focus, we employ the K-Means clustering algorithm to group the learners based on similar needs and preferences and identified that distinguishing clusters can be formed within the DHH group. We also tried to understand the significant features contributing to forming well separated groups. These results provide valuable insights into the diverse preferences and requirements when they interact with the learning materials. These findings emphasize the significance of personalized approach for DHH learners in educational settings and serve as the stepping stone to develop a personalized learning environment for them. 2024 IEEE. -
A Comprehensive Study on E-learning Environments for Deaf or Hard of Hearing Learners
Quality education is the fundamental right of every individual regardless of the disabilities they have. For the Deaf or Hard of Hearing (d/DHH) people, e-learning is the most promising way to access the educational materials referred to as digital learning objects (LO) at any time and space which increase their autonomous learning skills. This form of instruction delivery was widely accepted during the outbreak of Covid-19. Hence a background study has been conducted to investigate the challenges in teaching the d/DHH learners during the pandemic. This research work aims at providing a personalized e-learning environment to the d/DHH student community belonging to St. Clare Oral Higher Secondary School for The Deaf, situated in Kerala. To build personalized systems, the primary step is to review the existing e-learning solutions available in the literature and the adaptation techniques implemented by them to offer personalization in line with the components of traditional adaptive e-learning systems. The study carried out in this paper illuminates the need of personalized e-learning platforms that adapt the basic needs, abilities and disabilities of deaf learners which will find the 'best learning solutions' in the form of learning objects. 2023 IEEE. -
Tomato Plant Disease Classification Using Transfer Learning
Detecting and categorizing diseases in tomato plants poses a significant hurdle for farmers, resulting in considerable agricultural losses and economic harm. The prompt underscores the significance of promptly identifying and classifying diseases to enact successful management strategies. Convolutional Neural Networks (CNNs) have demonstrated their effectiveness in tasks involving image classification, notably in categorizing diseases that impact tomato plants. However, CNN models can be computationally expensive to train and require large datasets of labeled images. Utilizing advanced CNN models can enhance the efficacy of classification models for tomato plant diseases, simultaneously decreasing computational expenses and the demand for extensive training data. Enhanced CNN models can be developed using a variety of techniques, such as transfer learning, data augmentation, and residual networks. This project aims to implement a tomato plant disease classification model using an enhanced convolution neural network. This work uses the lifelong learning method which is the model that allows one to learn new tasks without forgetting previous knowledge. Leveraging sophisticated CNN models can improve the effectiveness of classification models for tomato plant diseases, while also reducing computational costs and the need for extensive training data. It is beneficial for tasks where there is limited data available to train a model from scratch. 2024 IEEE. -
5G-UFMC System For PAPR Reduction Using SRC-Precoding With Different Numerologies
Universal Filtered Multicarrier (UFMC) has been incorporated in 5G and is likely to be considered in future generations (B5G). The prominent limitation of UFMC manifests as a high Peak-to-Average Power Ratio (PAPR). Our suggested approach to address the Peak-to-Average Power Ratio (PAPR) issue in UFMC signals involves the application of diverse precoding matrices, including Square Root Raised Cosine Function (SRC), Discrete Cosine Transform (DCT), and Discrete Hartley Transform (DHT).This technique reduces the PAPR performance of UFMC signals over current state of the art methods. In square root raised cosine (SRC) precoding techniques, a novel precoding matrix is adapted for minimizing PAPR and improvement of BER respectively. Results show that the different subcarrier was applied and surpasses all existing techniques in reduction of PAPR and BER improvement. A novel SRC-Precoding technique reduces PAPR by 5dB for considering 512 sample points with QAM modulation as compared to 10dB for the conventional technique. Additionally, the Bit Error Rate Performance is maintaining 14dB when compared to conventional technique. Furthermore, the evaluation of Bit Error Rate (BER) performance and Peak-to-Average Power Ratio (PAPR) in the UFMC system reveals superior results compared to conventional technique. 2024 IEEE. -
Carbon dots as an effective material in enzyme immobilization for sensing applications
In carbon dots (CDs), both graphene quantum dots and carbon quantum dots were the latest entrants to the carbon family, all of which are spherical carbon nanoparticles of size <10nm. CDs have found their way in the various applications in the field of chemical sensing, biosensing, bioimaging, photocatalysis, nanomedicine, and electrocatalysis ever since their discovery. CDs provide interesting attributes to electrochemical and optical biosensing using enzyme biosensor due to they have desired advantages of biocompatibility, excellent physicochemical properties, high resistance to photo bleaching, intrinsic non/low-toxicity, high solubility, large specific surface area for the binding of enzymes, and low quantum yields, as well as their ability for modification with the attractive surface area. Surface active functional groups such as epoxide, hydroxyl(OH), and carboxylic acid (COOH) groups can be used for the immobilizing biomolecules on CDs. The enzyme immobilization is a process which is generally carried out by ionic/covalent interaction, encapsulation, and adsorption. The process of adsorption is considered to be a simple, effective, and economical method for enzyme immobilization. Thus enzymes immobilized on CDs have shown significant improvement in both activity and stability. This chapter aims to throw light on the progress and development of enzyme immobilization (e.g., laccase, bovine serum albumin, and horseradish peroxidase) in the CDs, which acts as a probe for sensing application, with laying emphasis on their synthesis along with the challenges faced in this exciting and promising field. 2023 Elsevier Inc. All rights reserved. -
Employing bioactive compounds derived from Ipomoea obscura (L.) to evaluate potential inhibitor for SARS-CoV-2 main protease and ACE2 protein
Angiotensin converting enzyme 2 (ACE2) and main protease (MPro) are significant target proteins, mainly involved in the attachment of viral genome to host cells and aid in replication of severe acute respiratory syndrome-coronaviruses or SARS-CoV genome. In the present study, we identified 11 potent bioactive compounds from ethanolic leaf extract of Ipomoea obscura (L.) by using GC-MS analysis. These potential bioactive compounds were considered for molecular docking studies against ACE2 and MPro target proteins to determine the antiviral effects against SARS-COV. Results exhibits that among 11 compounds from I. obscura (L.), urso-deoxycholic acid, demeclocycline, tetracycline, chlorotetracycline, and ethyl iso-allocholate had potential viral inhibitory activity. Hence, the present findings suggested that chemical constitution present in I. obscura (L.) will address inhibition of corona viral replication in host cells. 2020 The Authors. Food Frontiers published by NCU, NWU, JSU, ZJU & FAFU and John Wiley & Sons Australia, Ltd. -
Generation of Dynamic Table Using Magic Square to Enhance the Security for the ASCII CODE Using RSA
The efficiency of any cryptosystem not only depends on the speed of the encryption and decryption processes but also on its ability to produce different ciphertexts for the same plaintext. RSA, the public key cryptosystem, is the most famous and widely accepted cryptosystem, but it has some security vulnerabilities because it produces the same ciphertext for identical plaintexts occurring in several places. To enhance the security of RSA, magic square-based encoding models have been proposed in the literature. Although magic square-based encoding models have been proposed, they are static. Thus, this paper introduces a dynamic-based magic square with RSA, where encryption and decryption are performed using numbers generated from the magic square instead of ASCII values. Unlike the static magic square, the proposed dynamic magic square allows users to specify the starting and ending numbers in any position rather than fixed positions. In the proposed dynamic magic square generation, different 4 4 magic square templates are created, and 16 16 magic squares are generated from them. Experimental results clearly demonstrate the improved security of RSA. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024. -
Prediction of software defects using object-oriented metrics
In recent years, many of the object-oriented software metrics were proposed for increasing the quality of software design such as prediction of defects and the maintainability of classes and methods. As the word metrics is frequently used for specific measurements taken on a particular process or item and in object-oriented metrics the metrics are the unit of measurements that is used to characterize the data.The fundamental point of this research is to identify the significance difference between software metrics which observes defect prediction and also study about their relation involving in the object oriented metrics that is named as "Chidamber and Kemerer metric suite" which is also known as "CK metrics suite", the number of defects and then finally decide the differences of the metrics in ordering to Eclipse classes as defective and selected with regard to defect prediction. IAEME Publication.