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Enablers of Successful Fiscal Decentralisation: A Case Study of Three Gram Panchayats in Kerala
Kerala is among the few states that have a successful record in fi scal decentralisation. This study qualitatively analyses primary data from three gram panchayats in Kerala to identify the factors that enable successful decentralised fi scal governance through panchayati raj. Based on the fi ndings of the study, we have constructed a framework to assess the readiness of gram panchayats to carry out successful decentralised fiscal governance. 2022 Economic and Political Weekly. All rights reserved. -
Dynamic Connectedness and Volatility Spillover Effects of Indian Stock Market with International Stock Markets: An Empirical Investigation Using DCC GARCH
This study employs the DCC-GARCH model to investigate the dynamic connectedness between the Indian and significant global stock markets. Specifically, we examine daily log returns data of the National Stock Exchange (NSE) index and several international indices, including the United States, Australia, China, Germany, England, Japan, and Taiwan. Our analysis indicates a significant level of volatility spillover between the Indian stock market and the international stock market. Notably, we observe a significant positive spillover effect from the S&P 500 and FTSE 100 to the Indian stock market, suggesting contagion effects. Additionally, we find bidirectional spillover between the Indian stock market and the Nikkei 225 and Hang Seng, indicating a high level of interdependence between these markets. Our research contributes to the growing literature on the dynamic connectedness of stock markets and has important implications for policymakers and investors in emerging economies such as India. Overall, this study provides valuable insights into the nature and extent of spillover effects between the Indian and international stock markets. 2023 University of Pardubice. All rights reserved. -
Assessing the role of materialism and gratitude in life satisfaction through IPMA: the mediating role of meaningfulness in life
Purpose: This study aims to create a more humane and responsible workplace, individuals gratitude and meaningfulness seem of utmost importance. This study is an effort to understand the role of gratitude intent of potential managers. Design/methodology/approach: This study examines the psychological characteristic of business students in India. The researchers surveyed 333 Indian students as future managers. The collected data has been analysed with the Smart PLS 3 version to assess the formative-reflective scale by comparing model fit, measurement model and structural modelling. Findings: The results establish that gratitude significantly affects the life satisfaction of future managers. Findings also show that materialism is negatively related to life satisfaction and meaningfulness. The importanceperformance map analysis finding suggests that meaningfulness in life is a potential indicator of life satisfaction for the population studied. Originality/value: Due to the limited research available on the psychological underpinnings in the Indian context, there is a massive value in examining how materialism and gratitude concurrently and distinctively predict meaning in life and the life satisfaction of future managers. This paper gives a formative explanation of the model consisted gratitude, materialism and meaningfulness in life on the life satisfaction of future managers. This study establishes the importance of meaningfulness of life in attaining life satisfaction for young managers. 2023, Emerald Publishing Limited. -
Structural equation based model to investigate the moderating effect of fear of COVID using partial least square method
This study assesses the magnitude of work life integration among health care workers with the help of positive psychology constructs in COVID-19 crisis. The effect of optimistic approach and sense of belongingness is studied on the performance-oriented healthcare workers and how it influenced their withdrawal cognition. The moderating effect of fear of corona disease is also analysed on performance orientor and withdrawal cognition. Empirical data derived through face-to-face interactions of 357 health care professionals using partial least squares-structural equation modelling PLS-SEM 3.3.3 provides the detailed analysis of the model (measurement and structural). The results indicate that optimistic approach and sense of belongingness contribute towards performance-oriented health care work with R2 value of 79% (? =.533; t = 7.042; p< 0.00) and (? =.0.391; t = 5.43; p< 0.00) respectively. Performance orientor show negative relation with withdrawal cognition (? = -0.122.; t = 2.11; p< 0.00) and R2-value of 74.8%. The moderation effect of fear of corona disease shows negative affect on performance orientor (? = -0.044.; t = 26.10; p< 0.01); R2-value of 79.3% and positive interaction on the withdrawal cognition (? = 0.844; t = 38.42; p< 0.00) and R2-value of 76.4%. 2022 Taru Publications. -
Predicting of Open Source Software Component Reusability Level Using Object-Oriented Metrics by Taguchi Approach
Component-based software development (CBSD) is an efficient approach used by software developers to develop new software. The commercial off the shelf (COTS) and open-source software (OSS) are two styles to implement CBSD. The COTS provides the interface and depicts the black-box behavior, but does not support several software quality characteristics. On the other hard, OSS is a more efficient approach compared to COTS due to its source code availability. This research aims to identify the reusability level of OSS components from an online repository of OSS. The OSS components are classified based on Chidamber and Kemerer reusability metrics (CK-metrics). This paper proposed a mathematical model to establish the relationship between the reusability of CK-metrics. Reusability level of OSS component has been measured and most effective CK-metrics obtained by applying the Taguchi design and analysis of variance (ANOVA). The input parameters for the experimental design are evaluated based on the OSS repository. Performance analysis has been carried out based upon the interaction effect between the reusability of CK-metrics. Main effect plots are created to identify the most reusable component of the OSS. The genetic algorithm (GA) is used to predict the optimized value of the different control parameters. The results indicate that the OSS component reusability level is 0.698194. The reusability of software has a significant effect on the quality of software. The quality of software can be improved by increasing the reusability of software components. 2021 World Scientific Publishing Company. -
Evaluation of Remote Sensing and Meteorological parameters for Yield Prediction of Sugarcane (Saccharum officinarum L.) Crop
In the Agriculture sector, the farmers need a reliable estimation for pre-harvest crop yield prediction to decide their import-export policies. The present work aims to assess the impact of remote sensing-based derived products with Climate data on the accuracy of a prediction model for the sugarcane yield. The regression method was used to develop an empirical model based on VCI, Historical Sugarcane Yield, and Climatic Parameters of 75 districts of six major sugar-producing states of India. The MOD13Q1 product of MODIS on Board Terra Satellite at 16-day intervals was accessed during the growing season of sugarcane crop with 36 meteorological parameters for experimentation. The accuracy of the model was evaluated using R2, Root Mean square Metric (RMSE), Mean Absolute Error (MAE), and mean square error (MSE). The preliminary results concluded that the proposed methodology achieved the highest accuracy with (R2 =0.95, MAE=5.18, MSE=34.5, RMSE=5.87). The conclusion of the study highlighted that the coefficient of determination can be improved significantly by incorporating maximum and minimum temperature parameters with Remote sensing derived vegetation indices for the sugarcane yield. 2023 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY NC) license (https://creativecommons.org/licenses/by-nc/4.0/). -
Passenger flow prediction from AFC data using station memorizing LSTM for metro rail systems
Metro rail systems are increasingly becoming relevant and inevitable in the context of rising demand for sustainable transportation methods. Metros are therefore going to have a consistently expanding user-base and hence user satisfaction will require meticulous planning. Usage forecast is clearly an integral component of metro planning as it enables forward looking and efficient allocation of resources leading to greater commuter satisfaction. An observation from studying the usage of Kochi Metro Rail Ltd. is that there is a consistently occurring temporal pattern in usage for every station. But the patterns differ from station to station. This hinders the search for a global model representing all stations. We propose a way to overcome this by using station memorizing Long Short-Term Memory (LSTM) which takes in stations in encoded form as input along with usage sequence of stations. This is observed to significantly improve the performance of the model. The proposed architecture with station parameter is compared with algorithms like SVR (support vector regression) and neural network implementation with the best architecture to testify the claim. The proposed model can predict the future flow with an error rate of 0.00127 MSE (mean squared error), which is better than the other models tested. CTU FTS 2021. -
Synthesis, Mechanical Properties and Thermal Stability of Polydimethylsiloxane Nanocomposites
The polydimethylsiloxane/nano-graphite (PDMS-NG) nanocomposites were prepared via a two rolled mixing mill and subjected to characterization using techniques such as Transmission Electron Microscopy (TEM), stress-strain analysis during elongation, as well as thermal properties including Thermo-Gravimetric Analysis (TGA) and Differential Scanning Calorimetry (DSC). The transition temperature was observed to be below-70C for PDMS nanocomposites reinforced with Nano-Graphite (NG). The thermogram from the thermo-gravimetric analysis indicated that at 10%, 20%, 30%, and 50% weight loss, the temperatures for PDMS nanocomposites were higher compared to unfilled PDMS. These findings suggest a substantial improvement in the thermal stability of PDMS-NG nanocomposites. 2023, Books and Journals Private Ltd. All rights reserved. -
Investigation of fluorescence enhancement and antibacterial properties of nitrogen-doped carbonized polymer nanomaterials (N-CPNs)
Carbonized Polymer Nanomaterials (CPNs) have acquired substantial research interest in recent years due to their budding applications in various optical and electrochemical studies like electrocatalysis, solar cells, biosensing, etc. Due to their stability and toxicity, the enhancement of CPNs' properties was the primary cause of concern. Herein, we synthesized Nitrogen-doped (N-doped) N-CPNs using the one-step hydrothermal approach of PVA and PVDF polymers with Nitric acid (HNO3) as the nitrogen source. The luminescence intensity was observed to be enhanced by increasing nitrogen doping concentration. The synthesized fluorescent samples exhibited significant antibacterial properties, making them useful in biomarkers, sensing strategies, drug delivery, etc. Doped PVA samples exhibited negligible antibacterial activity, but nitrogen-doped PVDF samples displayed considerable biocidal activity against gram-positive bacteria, according to antibacterial research. Each sample's growth inhibition was distinct and species-specific. 2022 Taylor & Francis Group, LLC. -
Improved Security of the Data Communication in VANET Environment Using ASCII-ECC Algorithm
Now-a-days, with the augmenting accident statistics, the Vehicular Ad-hoc Networks (VANET) are turning out to be more popular, helping in prevention of accidents in addition to damage to the vehicles together with populace. In VANET, message can well be transmitted within a pre-stated region to attain systems safety and also improveits efficacy. Ensuring authenticity of messages is a challenge in such dynamic environment. Though few researchers worked on this, security level is very less. Hence enhanced communicationsecurity on the VANET environment utilizing the American Standard Code for Information Interchange centred Elliptic Curve Cryptography (ASCII-ECC) is proposedin this paper. The network design is definedinitially. Subsequently, the entire vehicles get registered to the Trusted Authority (TA); similarly, all vehicle users areregistered with their On-Board Unit (OBU). This is followed byMedian-centred K-Means (MKM) performs the cluster formation together with Cluster Head Selection (CHS). Next, TA takes care of the verification procedure. Modified Cockroach Swarm Optimization (MCSO) calculates the shortest path and the ASCII-ECC carries out the secure data communication if the vehicle is an authorized one. If not, TA sends the alert message for discarding the request. The system renders better performance when it was weighed against the top-notch methods. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
An Image Quality Selection and Effective Denoising on Retinal Images Using Hybrid Approaches
Retinal image analysis has remained an essential topic of research in the last decades. Several algorithms and techniques have been developed for the analysis of retinal images. Most of these techniques use benchmark retinal image datasets to evaluate performance without first exploring the quality of the retinal image. Hence, the performance metrics evaluated by these approaches are uncertain. In this paper, the quality of the images is selected by utilizing the hybrid naturalness image quality evaluator and the perception-based image quality evaluator (hybrid NIQE-PIQE) approach. Here, the raw input image quality score is evaluated using the Hybrid NIQE-PIQE approach. Based on the quality score value, the deep learning convolutional neural network (DCNN) categorizes the images into low quality, medium quality and high quality images. Then the selected quality images are again pre-processed to remove the noise present in the images. The individual green channel (G-channel) is extracted from the selected quality RGB images for noise filtering. Moreover, hybrid modified histogram equalization and homomorphic filtering (Hybrid G-MHE-HF) are utilized for enhanced noise filtering. The implementation of proposed scheme is implemented on MATLAB 2021a. The performance of the implemented method is compared with the other approaches to the accuracy, sensitivity, specificity, precision and F-score on DRIMDB and DRIVE datasets. The proposed schemes accuracy is 0.9774, sensitivity is 0.9562, precision is 0.99, specificity is 0.99, and F-measure is 0.9776 on the DRIMDB dataset, respectively. 2023 Baqiyatallah University of Medical Sciences. All rights reserved. -
Eye-Vision Net: Cataract Detection and Classification in Retinal and Slit Lamp Images using Deep Network
In the modern world, cataracts are the predominant cause of blindness. Early treatment and detection can reduce the number of cataract patients and prevent surgery. However, cataract grade classification is necessary to control risk and avoid blindness. Previously, various studies focused on developing a system to detect cataract type and grade. However, the existing works on cataract detection does not provide optimal results because of high detection error, lack of learning ability, computational complexity issues, etc. Therefore, the proposed work aims to develop an effective deep learning techniques for detecting and classifying cataracts from the given input samples. Here, the cataract detection and classification are performed using two phases. In order to provide an accurate cataract detection, the proposed study introduced Deep Optimized Convolutional Recurrent Network_Improved Aquila Optimization (Deep OCRN_IAO) model in phase I. Here, both retinal and slit lamp images are utilized for cataract detection. Then, the performance of these two image datasets are analysed, and the best one is chosen for cataract type and grade classification. By analysing the performance, the slit lamp images attain higher results. Therefore, phase II uses slit lamp images and detects the type and grade of cataracts through the proposed Batch Equivalence ResNet-101 (BE_ResNet101) model. The proposed classification model is highly efficient to classify the type and grades of cataracts. The experimental setup is done using MATLAB software, and the datasets used for simulation purposes are DRIMDB (Diabetic Retinopathy Images Database) and real-time slit lamp images. The proposed type and grade detection model has an accuracy of 98.87%, specificity of 99.66%, the sensitivity of 98.28%, Youden index of 95.04%, Kappa of 97.83%, and F1-score is 95.68%. The obtained results and comparative analysis proves that the proposed model is highly suitable for cataract detection and classification. 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved. -
Di-cationic ionic liquid catalyzed synthesis of 1,5-benzothiazepines
A simple and elegant method for the synthesis of 1,5-benzothiazepines has been developed using di-cationic liquid as a solvent cum catalyst by the reaction of o-aminothiophenol with a variety of chalcones under mild reaction conditions. Furthermore the reusability of the catalyst has also been studied for three cycles. All the reactions are proposed to proceed through a 1,4-conjugate Michael addition followed by a cyclo-condensation reaction. 2018, Chemical Publishing Co. All rights reserved. -
Exploration of the effects of anisotropy and rotation on RayleighBard convection of nanoliquid-saturated porous medium using general boundary conditions
This paper presents an analysis of RayleighBard convection (RBC) of a Newtonian-nanoliquid-saturated anisotropic porous medium in the presence of rotation (RayleighBardTaylor convection). The investigation is performed using non-classical boundary conditions. The effect of various parameters on the onset of convection is presented graphically. The system sees stabilisation due to an increase in the rotation rate and thermal anisotropy parameter whereas the system destabilises due to an increase in the mechanical anisotropy parameter. The results of 82 limiting cases can be extracted from the current work. The results of free-free, rigid-free and rigid-rigid isothermal/adiabatic boundaries are obtained from the present study by considering appropriate limits. The results of the limiting cases of the present study are in excellent agreement with those observed in earlier investigations. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
A Malicious Botnet Traffic Detection Using Machine Learning
Detection of incorrect and malign data transfers in the Internet of Things (IoT) network is important for IoT safety to observe an eye on and prevent unwelcomed traffic flow to the network of IoT. For it, Machine Learning (ML) strategic methods are produced by several researchers to prevent malign data flows through the network of IoT. Nonetheless, because of the wrong choice of feature, a few malign Machine Learning models differentiate especially the movement of malign traffic. Still, what matters is the problem that needs to be deliberated in-depth to select the best features for better malign traffic acquisition in the network of IoT. Dealing with the challenge, a new process was proposed. 1st, the metric method of selecting a novel feature called the proposed CorrAUC, and hinged on CorrAUC, a new highlight for choosing the Corrauc algorithm name is also being developed, designed hinged on the system folding filter features precisely and select the active features of the choose ML method using AUC metric. After that, we apply a combined application Order of Preference by Similarity to Ideal Solution Using Shannon Entropy (TOPSIS) built on a bijective set which is soft to verify selected features for identification of malign 1traffic in IoT network. We test our method using data set of Bot-IoT and 4 dissimilar ML classifiers. Practical outcomeanalysis showed that our proposed approach works as well and can achieve greater than 96% results on average. 2022 Wolters Kluwer Medknow Publications. All rights reserved. -
Enhancing Transparency and Trust in Agrifood Supply Chains through Novel Blockchain-based Architecture
At present, the world is witnessing a rapid change in all the fields of human civilization business interests and goals of all the sectors are changing very fast. Global changes are taking place quickly in all fields manufacturing, service, agriculture, and external sectors. There are plenty of hurdles in the emerging technologies in agriculture in the modern days. While adopting such technologies as transparency and trust issues among stakeholders, there arises a pressurized necessity on food suppliers because it has to create sustainable systems not only addressing demandsupply disparities but also ensuring food authenticity. Recent studies have attempted to explore the potential of technologies like blockchain and practices for smart and sustainable agriculture. Besides, this well-researched work investigates how a scientific cum technological blockchain architecture addresses supply chain challenges in Precision Agriculture to take up challenges related to transparency traceability, and security. A robust registration phase, efficient authentication mechanisms, and optimized data management strategies are the key components of the proposed architecture. Through secured key exchange mechanisms and encryption techniques, client's identities are verified with inevitable complexity. The confluence of IoT and blockchain technologies that set up modern farms amplify control within supply chain networks. The practical manifestation of the researchers' novel blockchain architecture that has been executed on the Hyperledger network, exposes a clear validation using corroboration of concept. Through exhaustive experimental analyses that encompass, transaction confirmation time and scalability metrics, the proposed architecture not only demonstrates efficiency but also underscores its usability to meet the demands of contemporary Precision Agriculture systems. However, the scholarly paper based upon a comprehensive overview resolves a solution as a fruitful and impactful contribution to blockchain applications in agriculture supply chains. Copyright 2024 KSII. -
Mass layoffs at BYJUS founders dilemma
Learning outcomes: This case study provides students/managers an opportunity to learn about the following: to infer the challenges involved in the downsizing of employees; to asses and evaluate BYJUS organizational culture; and to determine the impact of workplace toxicity. Case overview/synopsis: The focus of this case is the controversy faced by BYJUS due to its mass layoffs and toxic work culture. This case discusses the CEOs dilemma in resolving the controversy. Two rounds of mass layoffs at BYJUS are discussed in detail. The industrial dispute filed by Employees Union against BYJUS accusing it of denying due compensation to laid-off employees is also discussed. This case consists of a section explaining the toxic work culture at BYJUS, which is supported by employee complaints. The CEOs justification and apology have been illustrated in this case. The case ends with a closing dilemma and challenges faced by the CEO. Complexity academic level: The case is best suited for undergraduate students studying Human Resources Management subjects in Commerce and Business Management streams. The authors suggest that the instructor inform students to read the case before attending the 90-min session. It can be executed in the classroom after discussing the theoretical concepts. Supplementary material: Teaching notes are available for educators only. Subject code: CSS 6: Human Resource Management. 2024, Emerald Publishing Limited. -
Coca-Cola product placement strategy backfires a case of celebrity activism
Research methodology: A secondary research method was used to collect data for this case. The authors have made use of newspaper articles and published articles written by journalists and experts which are available in the public domain. Case overview/synopsis: Instances of celebrity activism such as athlete activism are rising. Social media has amplified the voice of celebrities and given them a personal channel to directly communicate with their fans without any media censorship. The same is true especially concerning endorsement by sports superstars, who now seem to have a mind of their own, independent of the official line of clubs, tournament organizers or sponsoring companies. This case discusses the embarrassment and financial loss faced by soft drinks giant Coca-Cola due to the public snub by football superstar Cristiano Ronaldo during an official press conference of the EURO 2020 championship. Complexity academic level: Undergraduate and postgraduate students studying marketing management and brand management courses in business management and commerce streams can use this case. This case can also be used for marketing specialization students at the undergraduate and postgraduate levels. 2023, Emerald Publishing Limited. -
Pricing and content Netflixs dilemma in India
Learning outcomes: The learning outcomes of this study are as follows:1. Analyze the pricing strategy followed by Netflix in India;2. Examine the challenges faced by media companies, including over-the-top (OTT) service providers, in developing content for target consumers in emerging markets; and3. Evaluate the dynamics of the Indian OTT industry and understand the effect of external and internal factors on the growth of Netflix in India. Case overview/synopsis: This case discusses the dilemma faced by Netflix in India regarding pricing and content. Netflix was accused of hurting the religious and political sentiments of Indians by broadcasting bold shows such as Sacred Games and A Suitable Boy. Netflix is caught in a dilemma between its pursuit to achieve its target of achieving 100 million subscribers from India versus continuing its profitable high pricing strategy. Another key dilemma is regarding the streaming of attractive bold content which may occasionally hurt the religious/political sentiments of some Indians or stream only safe content which may be deemed as boring by its young target audience. Complexity academic level: Undergraduate and postgraduate students studying Marketing courses in Commerce and Business Management streams can use this case. Supplementary materials: Teaching notes are available for educators only. Subject code: CSS 8: Marketing. 2022, Emerald Publishing Limited. -
Good-looking step in bad direction RBI Internal Working Group proposal
Learning outcomes: After discussing this case, the authors expect that the students will have the following learnings: critically analyse the latest Reserve Bank of India (RBI) banking proposal, which was proposed by the Internal Working Group (IWG) in November 2020. Understand concepts such as connected lending, crony capitalism and financial crisis. Have a basic idea about the Banking Regulations Act, 1949 and regulatory framework in the Indian banking sector. Case overview/synopsis: This case is an analysis of the recent RBI proposal on banking regulations in India. The authors have referred secondary data in terms of published papers by stalwarts and experts in the banking and economics field. This case analyses the pros and cons of the IWG proposal to RBI governing body. The case also touches upon interesting banking and macroeconomics concepts. What makes this case interesting is that RBI is open to receive comments from all the stakeholders till January 2021. Complexity academic level: Applicable to undergraduate and postgraduate students studying banking and finance specialisation in commerce and business management streams. Supplementary materials: Teaching notes are available for educators only. Subject code: CSS 1: Accounting and Finance. 2021, Emerald Publishing Limited.