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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. -
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 -
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. -
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. -
The Role of Imposter Phenomenon on Self-Handicapping and Psychological Distress among Young Adults
The Imposter Phenomenon (IP), characterized by persistent self-doubt and a fear of being exposed as a fraud despite objective success, is a growing concern, particularly among young adults. This study explores the intricate relationships between the Imposter Phenomenon, Self-handicapping, and Psychological Distress in a sample of 242 young adults aged 1825. The data is analysed using descriptive statistics, correlation, and regression. Findings from a comprehensive survey, utilizing the Clance Impostor Phenomenon Scale, the Self-Handicapping Scale, and the Mental Health Inventory reveal a significant positive correlation and prediction between the Imposter Phenomenon and self-handicapping and a positive relationship between the Imposter phenomenon and psychological distress. These findings contribute to a deeper understanding of how the Imposter Phenomenon influences self-handicapping behaviours in young adults, shedding light on the psychological distress associated with these experiences. The study underscores the need for targeted interventions to address imposter feelings and their potential consequences on mental well-being in this vulnerable population, ultimately aiming to foster a healthier and more resilient generation. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
Ensemble approach of transfer learning and vision transformer leveraging explainable AI for disease diagnosis: An advancement towards smart healthcare 5.0
Smart healthcare has advanced the medical industry with the integration of data-driven approaches. Artificial intelligence and machine learning provided remarkable progress, but there is a lack of transparency and interpretability in such applications. To overcome such limitations, explainable AI (EXAI) provided a promising result. This paper applied the EXAI for disease diagnosis in the advancement of smart healthcare. The paper combined the approach of transfer learning, vision transformer, and explainable AI and designed an ensemble approach for prediction of disease and its severity. The result is evaluated on a dataset of Alzheimer's disease. The result analysis compared the performance of transfer learning models with the ensemble model of transfer learning and vision transformer. For training, InceptionV3, VGG19, Resnet50, and Densenet121 transfer learning models were selected for ensembling with vision transformer. The result compares the performance of two models: a transfer learning (TL) model and an ensemble transfer learning (Ensemble TL) model combined with vision transformer (ViT) on ADNI dataset. For the TL model, the accuracy is 58 %, precision is 52 %, recall is 42 %, and the F1-score is 44 %. Whereas, the Ensemble TL model with ViT shows significantly improved performance i.e., 96 % of accuracy, 94 % of precision, 90 % of recall and 92 % of F1-score on ADNI dataset. This shows the efficacy of the ensemble model over transfer learning models. 2024 -
Design of decision support system to identify crop water need
Crop Water Need (ET crop) is referred to as the amount of water needed by a crop to grow. ET crop has high significance to identify the adequate amount of irrigation need. In this paper, a decision support system is proposed to identify Crop Water Need. The proposed decision support system is implemented through sensors and android based smartphone. Internet of Things (IoT) based temperature sensor (DHT11) is used to acquire the real time environmental factors that affect the ET crop. The sensor will communicate with android based smartphone application using Bluetooth Technology (BT-HC05). This proposed system has been compared with available evapotranspiration and existing manual method of evapotranspiration and it was found that proposed system is more correlated than existing manual method of evapotranspiration. The correlation coefficient obtained between proposed system and available evapotranspiration is 0.9783. The proposed decision support system is beneficial for farmers, agriculture researchers and professionals. 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Intelligent Diagnostic Prediction and Classification Models for Detection of Kidney Disease
Kidney disease is a major public health concern that has only recently emerged. Toxins are removed from the body by the kidneys through urine. In the early stages of the condition, the patient has no problems, but recovery is difficult in the later stages. Doctors must be able to recognize this condition early in order to save the lives of their patients. To detect this illness early on, researchers have used a variety of methods. Prediction analysis based on machine learning has been shown to be more accurate than other methodologies. This research can help us to better understand global disparities in kidney disease, as well as what we can do to address them and coordinate our efforts to achieve global kidney health equity. This study provides an excellent feature-based prediction model for detecting kidney disease. Various machine learning algorithms, including k-nearest neighbors algorithm (KNN), artificial neural networks (ANN), support vector machines (SVM), naive bayes (NB), and others, as well as Re-cursive Feature Elimination (RFE) and Chi-Square test feature-selection techniques, were used to build and analyze various prediction models on a publicly available dataset of healthy and kidney disease patients. The studies found that a logistic regression-based prediction model with optimal features chosen using the Chi-Square technique had the highest accuracy of 98.75 percent. White Blood Cell Count (Wbcc), Blood Glucose Random (bgr), Blood Urea (Bu), Serum Creatinine (Sc), Packed Cell Volume (Pcv), Albumin (Al), Hemoglobin (Hemo), Age, Sugar (Su), Hypertension (Htn), Diabetes Mellitus (Dm), and Blood Pressure (Bp) are examples of these traits. 2022 by the authors. -
An Enhanced SEIR Model for Prediction of COVID-19 with Vaccination Effect
Currently, the spread of COVID-19 is running at a constant pace. The current situation is not so alarming, but every pandemic has a history of three waves. Two waves have been seen, and now expecting the third wave. Compartmental models are one of the methods that predict the severity of a pandemic. An enhanced SEIR model is expected to predict the new cases of COVID-19. The proposed model has an additional compartment of vaccination. This proposed model is the SEIRV model that predicts the severity of COVID-19 when the population is vaccinated. The proposed model is simulated with three conditions. The first condition is when social distancing is not incorporated, while the second condition is when social distancing is included. The third one condition is when social distancing is combined when the population is vaccinated. The result shows an epidemic growth rate of about 0.06 per day, and the number of infected people doubles every 10.7 days. Still, with imparting social distancing, the proposed model obtained the value of R0 is 1.3. Vaccination of infants and kids will be considered as future work. 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). -
Performance Evaluation of Predicting IoT Malicious Nodes Using Machine Learning Classification Algorithms
The prediction of malicious nodes in Internet of Things (IoT) networks is crucial for enhancing network security. Malicious nodes can significantly impact network performance across various scenarios. Machine learning (ML) classification algorithms provide binary outcomes ("yes" or "no") to accurately identify these nodes. This study implements various classifier algorithms to address the problem of malicious node classification, using the SensorNetGuard dataset. The dataset, comprising 10,000 records with 21 features, was preprocessed and used to train multiple ML models, including Logistic Regression, Decision Tree, Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Performance evaluation of these models followed the ML workflow, utilizing Python libraries such as scikit-learn, Seaborn, Matplotlib, and Pandas. The results indicated that the Naive Bayes classifier outperformed others with an accuracy of 98.1%. This paper demonstrates the effectiveness of ML classifiers in detecting malicious nodes in IoT networks, providing a robust predictive model for real-time application. The SensorNetGuard dataset is available on the IEEE data port and Kaggle platform. 2024, Prof.Dr. ?skender AKKURT. All rights reserved. -
Additively Composite Model Objective Function for Routing Protocol for Low-Power and Lossy Network Protocol
The Internet of Things (IoT) networks always operate within the context of diverse and constrained characteristics of the devices. Low-Power and Lossy Networks (LLNs) constitute a network architecture commonly utilized in IoT application deployments, facilitating networking and the establishment of paths for data transmission. The Routing Protocol for Low-Power and Lossy Networks (RPL) demonstrates promising capabilities for LLN network operations, supporting IPv4 and IPv6-enabled services. The RPL protocol constructs a Destination Oriented Directed Acyclic Graph (DODAG) logical routing topology based on defined Objective Function (OF) metrics. Routing operations within the DODAG utilize these metrics and constraints to select parent nodes and calculate optimal routes between two nodes. Standardized OFs have traditionally focused on either parent node selection or routing objectives within the DODAG, often treating load balancing and bottleneck optimization separately. However, their combined impact on RPL's effectiveness has been overlooked. This paper introduces an Adaptively Composite Objective Function (AC-OF) approach that considers the combined objectives of DODAG load balancing and optimized routing operations. Through simulation evidence, the paper presents improved network parameters. The AC-OF implementation brings out significant results in the form of a balanced DODAG topology and it has good impacts on data transmission, control overhead messages, parent switching, delay, energy consumption, and node lifetime. 2024 Totem Publisher, Inc. All rights reserved. -
Solid-state fermentation of pigment producing endophytic fungus Fusarium solani from Madiwala lake and its toxicity studies
Several consumer products look enticing due to colors and there has been a demand for colors for various applications ever since human civilization started. Although in the primitive days, humans had used natural colors, the wake of the industrial revolution saw the excessive use of diverse types of synthetic colors. Although it looked very fancy initially, slowly scientists discovered the dangers of large-scale use of these colorants. The current demand is for natural colors, and hence, there is a scope for sources of natural colors from biosources. The present study involved the isolation of an endophytic fungus, Fusarium solani producing a red pigment from the polluted waters of Madiwala lake in Bangalore. The fungal extract showed good antimicrobial and moderate antioxidant properties. Cytotoxicity assays using brine shrimps proved negligible toxicity which is a positive trait for natural colorants for safer applications in industries. Media optimization and solid state fermentation were carried out to improve the yield of the fungal pigment and also to formulate a cheaper media for fungal multiplication and pigment production. Green synthesis of silver nanoparticles was also carried out with the fungal extract and the nanoparticles were characterized. Thus, the present study provides an option for the extraction of environment friendly natural colorant from the fungus F. solani for potential industrial applications. 2024 Bhoomika Prakash Poornamath, et al. -
Morphological and Elemental Investigations on CoFeBO Thin Films Deposited by Pulsed Laser Deposition for Alkaline Water Oxidation: Charge Exchange Efficiency as the Prevailing Factor in Comparison with the Adsorption Process
Abstract: Mixed transition-metals oxide electrocatalysts have shown huge potential for electrochemical water oxidation due to their earth abundance, low cost and excellent electrocatalytic activity. Here we present CoFeBO coatings as oxygen evolution catalyst synthesized by Pulsed Laser Deposition (PLD) which provided flexibility to investigate the effect of morphology and structural transformation on the catalytic activity. As an unusual behaviour, nanomorphology of 3D-urchin-like particles assembled with crystallized CoFe2O4 nanowires, acquiring high surface area, displayed inferior performance as compared to coreshell particles with partially crystalline shell containing boron. The best electrochemical activity towards water oxidation in alkaline medium with an overpotential of 315 mV at 10 mA/cm2 along with a Tafel slope of 31.5 mV/dec was recorded with coreshell particle morphology. Systematic comparison with control samples highlighted the role of all the elements, with Co being the active element, boron prevents the complete oxidation of Co to form Co3+ active species (CoOOH), while Fe assists in reducing Co3+ to Co2+ so that these species are regenerated in the successive cycles. Thorough observation of results also indicates that the activity of the active sites play a dominating role in determining the performance of the electrocatalyst over the number of adsorption sites. The synthesized CoFeBO coatings displayed good stability and recyclability thereby showcasing potential for industrial applications. Graphic Abstract: [Figure not available: see fulltext.] 2021, The Author(s). -
A new algorithm with its randomness and effectiveness against statistical tests in data encryption
In the world where security is one of the main concern, we are still not able to make our data secure. Privacy is one of the major concerns in todays world, where all the organization are dealing with data leak problem, data theft, data intrusion. We came up with a mathematical model to encrypt and decrypt data securely. In this paper we have came up with a technique to encrypt and decrypt data using non-deterministic random numbers and generating two cipher text for each data unit (character) and verified the randomness of our cipher text using chi-square test, Gaps test. IJSTR 2020. -
Exploring the mediating role of job and life satisfaction between workfamily conflict, familywork conflict and turnover intention
Purpose: This study investigates the influence of work-to-family and family-to-work conflict on turnover intention (career break), mediated through job and life satisfaction among Indian women in the service sector, using role conflict theory as the base. Design/methodology/approach: A total of 421 usable responses from women who had taken a career break were collected using a 36-item scale from six major metro cities in India through social and digital media platforms. A purposive-cum-snowballing sampling method was adopted. The hypotheses were tested using structural equation modeling (SEM) through AMOS. Findings: Findings suggest that job satisfaction (JS) is a significant predictor of turnover intention, both when work spills into the family domain, and family responsibilities spill into the work domain, thereby confirming the mediating influence of JS. Interestingly, life satisfaction (LS) only seems to mediate between inter-domain conflict and turnover intention partially. Research limitations/implications: This is a descriptive study, and is thereby limited in terms of its generalizability, specifically as it included respondents only from six major metro cities in India. Practical implications: The extended work-family conflict model could help managers structure organizational interventions that support women to deal with the challenges of managing the demands of both work and family domains, thereby reducing the negative influence on JS. Such initiatives could help reduce career breaks among women. Originality/value: We explored the cause of career breaks among Indian urban women employed in the service sector, using the extended model of inter-role conflict and their attitudes towards both life and job. 2024, Emerald Publishing Limited.