Browse Items (9795 total)
Sort by:
-
Environmental value development among preadolescents: a content analysis of reflective responses
Addressing the environmentally detrimental values prevalent in society in the context of rapid climate change is the need of the hour. Combining empathy with cognitive skills such as reflective thinking effectively creates new values among people. The present study attempts to reveal the pattern of environmental value development among 33 preadolescents by reflecting upon the empathy-generating story experiences and the related contents. The study is part of a more extensive quasi-experimental study, and it specifically performs a content analysis on the participants responses in their workbooks. Biospheric nature-related values are the most highly developed, and social justice is the least developed value, implying the need to focus more on the value of social justice. Stories are aids, and reflective thinking and empathetic elicitation are effective techniques for passing environmental values. Empathy generation instead of negative emotions from self-concern and emotion regulation through reflective thinking may be helpful to promote well-being in the context of climate change. Reflective thinking helps environmental value development by enhancing comprehension, emotion regulation, and self-awareness of values, implying a shift from telling the moral of a story to exploring the same through reflective thinking. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
The effect of an environmental education program based on empathy and reflective thinking on preadolescents environmental values and knowledge
As a remedial measure to human-induced climate change, it is imperative to develop intrinsic altruistic values and adequate knowledge of environmental phenomena and behavior to act in favor of the environment. The study aims to assess the effect of an Environmental Education Program (EEP) based on empathy, reflective thinking, and information on preadolescents environmental values and knowledge. For that, a non-equivalent control group design was used. Data collection was done through questionnaires, checklists, and participant thought diaries. Forty-six students (eleven-to-twelve-year-old children) selected through purposive sampling from the sixth standard of two semi-urban schools in Kerala, India, constituted the sample. In the intervention, empathy was manipulated through stories and empathy exercises, reflective thinking through thought diaries, and information on environmental phenomena, issues, and the effect of human actions through stories and knowledge exercises. Empathy, reflective thinking, and information manipulations positively influenced the participants environmental values and knowledge, and the effect manifested in cognitive, affective, and conative dimensions. The study has implications for conducting Environmental Education indoors effectively by integrating affective, cognitive, and metacognitive approaches. Empathy stories can be utilized to address various environmental phenomena and issues. Reflective thinking on environmental phenomena and issues can be applied to teach appropriate environmental behaviors. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Bioremediation and Detoxification of Asbestos from Soil
Asbestos is referred to as magic mineral and used as excellent building material. It finds its application in wide range of products such as floor tiles, pipes, paper, rope, cloth, insulated partition board, etc. On average, India uses 3, 50, 000 tons of asbestos annually and asbestos fibers readily undergo weathering releasing them into soil, water and air. Occupational and environmental exposure to this asbestos is leading to asbestosis (asbestos-related disease), lung cancer, and heart failure. Considering the serious health risk, countries like Australia, Brazil, and Canada had banned the use of asbestos. As asbestos is extensively used in construction of buildings, the demolished materials are dumped in the soil and thus it finds its route in soil as pollutant. Soil borne microbes like bacteria, fungi and lichens are found to be best means to reduce the toxicity of asbestos. These microorganisms remove iron from asbestos and reduce its toxicity. Another most effective bioremediation approach is phytoremediation to clean up the soil wherein vegetative cover on contaminated soil can remove iron and breaks down asbestos as source of inorganic nutrient. The main advantage of phytoremediation is that it can be extended to any geographical area where plants can grow. This chapter emphasizes various means of use and disposal of asbestos, followed by various means of bioremediation using microbes and plants and as an alternate for the sustainable soil condition. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022, corrected publication 2022. -
Analysis of food consumption expenditure patterns for developing sustainable business practices
Food consumption patterns are said to be near ideal variable since it is one of the most important commodities that ensure human survival. The patterns within the consumption as well as between different types of food items to different levels of consumption varies with respect to the macroeconomic conditions. The consumption expenditure for cereals, pulses and pulse products, edible oil, milk and milk products, meat, fish and egg, vegetables and fruits, shows an increasing trend in India and in Karnataka. The income elasticity for all food categories are lesser in urban sectors of Karnataka than the rural sector which indicates that the income of the rural households is lesser and the increase in income is also lesser compared to that of the Urban-Karnataka. The business enterprises can aim at sustainable consumption practices through the pricing strategies, product design and distribution channels. 2021 Ecological Society of India. All rights reserved. -
A Simple and Efficient [(n-Bu3Sn)2MO4]n Catalyzed Synthesis of Quinazolinones and Dihydroquinazolinones
A novel unprecedented approach for the synthesis of various quinazolinones and dihydroquinazolinones has been using [(n-Bu3Sn)2MO4]n as a catalyst. The reaction has been screened in various solvents and a gram scale experiment has also been demonstrated under given conditions. Further, the substrate scope of the reaction and the recyclability of the catalyst have also been studied. 2021 Taylor & Francis Group, LLC. -
A simple and efficient synthesis of imidazoquinoxalines and spiroquinoxalinones via pictect-spengler reaction using Wang resin
An efficient approach for the synthesis of various imidazoquinoxalines and spiroquinoxalinones has been reported from 2-(1H-imidazol-1-yl) aniline and different aldehydes using Wang-OSO3H as a reusable catalyst to get in good yields. The reaction condition has been optimized by screening in various solvents and a gram scale experiment has also demonstrated. Further, the substrate scope of the reaction has also been well demonstrated. 2021 Taylor & Francis Group, LLC. -
A Survey on P4 Challenges in Software Defined Networks: P4 Programming
Software Defined Networking (SDN) has been a prominent technology in the last decade that increases networking programmability. The SDN philosophy decouples the application, control, and data plane to increase the network programmability. The data plane is an essential but unsolved component that receives less attention than control and application planes. Traditionally, the data plane uses fixed functions that forward packets using a limited number of protocols. The P4 (Programming Protocol-independent Packet Processors) language makes it possible to program SDN data plane, which push the SDN to the next level. In the research community and industry, programming the data plane has garnered significant attention. Surprisingly, there has been no comprehensive reviews of programmable data-plane switches, which have many advantages in today's networks. The authors reviewed the evolution of networks from legacy to programmable data planes, explained the fundamentals of programmable switches, and summarized the network generation from traditional to programmable networks. In this paper, SDN is described from a P4-centric standpoint and discusses over 75 related research papers. Several taxonomies for the field are provided, outline potential research areas, and provide greater details regarding the patterns that have led to the development of this technology. 2013 IEEE. -
Data visualization: Experiment to impose ddos attack and its recovery on software-defined networks
The entire network is doing paradigm shift towards the software-defined networks by separating forwarding plane from control plane. This gives a clear call to researchers for joining the ocean of software-defined networks for doing research considering its security aspects. The biggest advantage of SDN is programmability of the forwarding plane. By making the switches programmable, it can take live instructions from controllers. The versions of OpenFlow protocol and the compatibility of programmable switches with OpenFlow were the stepping stone making software-defined networks thrashed towards reality. The control plane has come up with multiple options of controllers such as NOX [2], Ryu [3], Floodlight [4], Open- DayLight [6], ONOS [7] and the list is big. The major players are Java based which keeps the doors open for enhancement of features by the contributors. However, more is expected from the practicality of P4Lang programmed switches by bringing skilled people to the industry who can actually implement programmable switches with ease. The obvious reason for delayed progress in the area of software-defined networks is the lack of awareness towards data visualization options existing as of now. The purpose of writing this chapter is to throw light upon the existing options available for data visualization in the area of SDN especially addressing the security aspect by analyzing the experiment of distributed denial of service (DDoS) attack on SDN with clarity on its usage, features, applicability and scopes for its adaptabilities in the world of networks which is going towards SDN. This chapter is a call to network researchers to join the train of SDN and push forward the SDN technology by proved results of data visualization of network and security matrices. The sections and subsections show clearly the experimental steps to implement DDoS attack on SDN and further provide solution to overcome the attack. Springer Nature Singapore Pte Ltd. 2020. -
Enhancement of the thermal conductivity of a near room temperature magnetocaloric composite using graphene-like hybrid nanosheets derived from organic waste
Polymer matrix composites, fabricated to counter the inherent brittleness of magnetocaloric Heusler alloys, suffer from low thermal conductivity. Here, we demonstrate a low-cost, scalable route towards developing thermally conductive, mechanically robust near-room-temperature magnetocaloric composites by incorporating graphene-like hybrid nanostructures chemically synthesized from discarded sugarcane. Micron-sized particles obtained by manually grinding Ni50.2Mn36.7Sn13 ribbons possessing a strong magnetostructural transformation near room-temperature were chosen as the active magnetocaloric fillers. Both the functional fillers were incorporated into a polysulfone matrix by solution casting. Large values of isothermal entropy change ? 0.43 and -0.46 J/kg.K were observed for a ?H = 2T, driven by two successive first and second-order transformations within the alloy fillers. Additionally, an enhanced value of the in-plane thermal conductivity ? 3.06 0.4 W/m.K was observed in the composites owing to the formation of efficient thermal bridges/pathways by the graphene-like hybrid nanostructures, rendering them promising candidates for magnetic refrigeration applications. 2023 Acta Materialia Inc. -
Autoimmune diseases and an approach to type 1 diabetes analysis using PSO, K-means, and silhouette values
An estimated 50 million Americans suffer from autoimmune diseases, as per the report from AARDA (American Autoimmune Related Diseases Association). More than 30 million people suffer in India from type 1 diabetes. More than $100 billion is spent on healthcare for autoimmune diseases in America, more than for cancer healthcare. Host genes and environmental factors control autoimmune diseases, and typically they do not have any specific cure. This paper proposes an artificial intelligence-based framework for the initial prediction of autoimmune diseases. This work attempts to identify characteristics of autoimmune diseases, and it lists the commonly occurring autoimmune diseases, the organs attacked by them, and the different stages involved. It also seeks to identify ways to prioritize the severity of the patient's disease, for providing treatments based on the severity, with the goal of reducing the pressure on the healthcare sector. Type 1 diabetes is an autoimmune disease and identifying the risk associated with diabetes and other related health problems could help to improve health worldwide. This work proposes a framework while exploring autoimmune disease prediction using machine learning techniques. The autoimmune disease considered is type 1 diabetes. The usage of machine learning techniques can help to enhance patient care and early prediction. This research is an attempt to explore the possibilities and also to propose a framework for early prediction of type 1 diabetes. Clustering is performed using K-means and PSO K-means. Validation of the clusters is carried out using silhouette coefficient. 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
A Document Clustering Approach Using Shared Nearest Neighbour Affinity, TF-IDF and Angular Similarity
Quantum of data is increasing in an exponential order. Clustering is a major task in many text mining applications. Organizing text documents automatically, extracting topics from documents, retrieval of information and information filtering are considered as the applications of clustering. This task reveals identical patterns from a collection of documents. Understanding of the documents, representation of them and categorization of documents require various techniques. Text clustering process requires both natural language processing and machine learning techniques. An unsupervised spatial pattern identification approach is proposed for text data. A new algorithm for finding coherent patterns from a huge collection of text data is proposed, which is based on the shared nearest neighbour. The implementation followed by validation confirms that the proposed algorithm can cluster the text data for the identification of coherent patterns. The results are visualized using a graph. The results show the methodology works well for different text datasets. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Towards Sustainable Living through Sentiment Analysis during Covid19
Artificial intelligence is the process of the machine to perform with the simulation of human intelligence. Computing within the field of emotions paves the recognitions to sentiment analysis. Sentiment analysis is the method of capturing the emotions behind a text whether or not it's positive, negative or neutral. Sentiment Analysis (SA) or Opinion Mining (OA) is the process to provide computational treatment to unstructured data to categorize and identify the sentiments or emotions expressed in a piece of text. It combines Natural Language Processing Techniques and Machine Learning Techniques. This technology is additionally referred to as opinion mining or feeling computing. Sentiment Analysis uses the ideas of machine learning alongside an AI based process called NLP to extract and analyse the data, emotions, information from the text. This work explores the impact of social media during covid 19 and possible link between sustainable living and health care with the usage of sentiments. This paper address the sustainable development goal 3 (good health and wellbeing) of SDG 2030 and a possible way towards sustainable living through sentiment analysis. The Electrochemical Society -
Sustainability & Comparative Impact Analysis of Coral reef bleaching in Indian context
An estimated value of 500 million of the population are directly benefited through coral reefs related jobs, food and defence of coastal areas. Coral reefs help to reduce wave energy by 97%. They help to protect the coastal areas from storms, floods and wave energy by 97%. Natural disasters such as Tsunami and erosion of coastal areas are protected by reefs. In this process, they help to protect the lives of many staying in the coastal areas including animals, properties, and other natural resources. There are reasons for reef deterioration like change of climate, high pollution, destructive fishing, bleaching of coral reefs is a big concern now worldwide. Severe coral bleaching is also reported in India. A significant rise in the surface temperature of Sea has become a critical reason for coral bleaching. This work attempts to Study the link between sustainability, SDG goals of 2030 given by United Nations and coral bleaching. In this work study period is focussed from 1985 to 2021 in the Indian coral reef bleaching areas. The Electrochemical Society -
eHED2SDG: A Framework Towards Sustainable Professionalism & Attaining SDG through Online Holistic Education in Indian Higher Education
To enable sustainable development of society it is essential to train the leaders and professionals of tomorrow. Developing a sustainable society and holistically developed future for budding professional is a significant objective of higher education Institutions. Every professional course learner is expected to utilize his skills, knowledge and time to contribute towards the development of society. Fostering sustainability in various domains of development is a requirement for Sustainable Development Goals (SDG). This research is inspired by multiple mental health related problems among professionals, inability to cope up with stress, quick dissatisfaction and frustrations, suicide, poor happiness quotient measured through multiple psychological tests and many other negative mental status which have paved the path for more serious approaches towards holistic development of young professions. This research addresses the SDG goal 4, Quality Education directly. Indirectly it can work as a catalyst to ignite the interest and create awareness about all the sustainable development goals. The Electrochemical Society -
Sentiment and Emotion Analysis of Significant Diseases in India and Russia
Healthcare organizations need this information to understand and treat the patient's concerns. The motivation for this kind of analysis is how patients provide this information while wrapping it in their thoughts and emotions. It is less practicable to manually study all the free and abundant health-related knowledge accessible online to arrive at decisions that might contribute to an immediate and beneficial decision. Sentiment analysis methods perform this function through automated procedures with minimal human intervention. In this paper, an investigation is conducted to compare the region-wise, language-wise, and sentiment analysis of the tweets collected from Russia and India. The results obtained through research have shown some significant characteristics of the language models used for language detection. The inferenc and analysis obtained from the observations are included in this paper. 2023 IEEE. -
Intelligent Approaches of Clinical and Nonclinical Type-1 Diabetes Data Clustering and Analysis
Every year in India, there are nearly 15,600 fresh cases being reported among these age groups. In 2011, in the United States, 18,000 children under 15 were newly reported for T1DM. Over 13years, the Karnataka state government has a list of records showing that out of 100,000, 37% of boys and 40% of girls are affected by T1DM Disease. This paper investigates two methodologies to identify significant details about Type-1 diabetes. The first methodology is applicable to clinical data. The second methodology is demonstrated for the NDA T1D dataset. The dataset is utilized further to apply machine learning techniques to group similar patient traits. Exploratory data analysis on the dataset has revealed significant information answering a few research questions. This analysis can be useful for India, China, and other countries with high populations. In this paper, a unique methodology based on Artificial Intelligence Technique is proposed for both clinical and non-clinical data. The Autoimmune Disease, Diabetes Type 1-T1D, is focused. Non Clinical data based on 2021 reports are collected to identify patterns. Substantial unique issues are addressed in this work which were never reported before. The knowledge generated can be helpful for creating new clinical datasets, methodology and new insights related to Type-1 diabetes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
An empirical analysis of similarity measures for unstructured data
With fast growth in size of digital text documents over internet and digital repositories, the pools of digital document is piling up day by day. Due to this digital revolution and growth, an efficient and effective technique is required to handle such an enormous amount of data. It is extremely important to understand the documents properly to mine them. To find coherence among documents text similarity measurement pays a humongous role. The goal of similarity computation is to identify cohesion among text documents and to make the text ready for the required applications such as document organization, plagiarism detection, query matching etc. This task is one of the most fundamental task in the area of information retrieval, information extraction, document organization, plagiarism detection and text mining problems. But effectiveness of document clustering is highly dependent on this task. In this paper four similarity measures are implemented and their descriptive statistics is compared. The results are found to be satisfactory. Graphs are drawn for visualization of results. 2019 COMPUSOFT, An international journal of advanced computer technology. -
Discovering patterns using feature selection techniques and correlation
Term Frequency and inverse document frequency is reported to have a significant contribution for various text categorization, document clustering and many other text mining related tasks. A collection of the applications and the enhancements of the Term Frequency and Inverse Document Frequency based document representation technique is examined in this work. The document representation algorithm is essential in the field of text - script mining. In this algorithm, unstructured data is converted into a vector space model where each related document is considered as a point in the vector space. Related documents come in proximity to the other related documents while the documents that are very far away from being coherent remain different from each other. In this paper, four feature selection techniques are implemented to discover the patterns from a repository of unstructured data by using correlation similarity measure. Analysis and comparison with other existing technique is also included. The validation of the patterns formed is performed by using silhouette values. Experiments are conducted to compare performance. Results indicate that TDMp1 performance is poor compared to others. Springer Nature Switzerland AG 2020. -
Evaluation of ML-Based Sentiment Analysis Techniques with Stochastic Gradient Descent and Logistic Regression
In recent times, along with the expansion of technology, the Internet also has flourished exponentially. World is more connected today not only through the technology, but also through sharing sentiments to express views, either be constructive or destructive in front of the world through social media. Twitter, Facebook, Instagram, etc., are being used as social media to reach the world. The study of understanding peoples emotions, intentions, attitudes from unstructured data is opinion mining/sentiment analysis. This is an application of NLP or text mining. In this paper, an attempt is made to realize sentiment analysis's multiple dimensions using approaches such as ML and NLP-based technqies like word frequency and TF-IDF. Using ML approach, experiments were conducted, and the performance of the predictions was visualized. Three different datasets are used. A comparison of logistic regression (LR) and stochastic gradient descent (SGD) algorithms are compared using two different document representation. An extensive comparison is carried out using three different types of dataset. Amazon instant video datasets, bank dataset and movie reviews datasets are being used for the same. Analysis of performance is accomplished by using different graphs. The results indicate that logistic regression performs better than stochastic gradient descent for movie review dataset by using word frequency and TF-IDF-based approach. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Comparative Analysis of Sentiment Analysis Using RNN-LSTM and Logistic Regression
Social media analytics makes a big difference in the success or failure of an organization. The data gathered from social media can be used to get a hit type product by analyzing the data and getting important information about the need of the people. This can be done by implementing sentiment analysis on the available data and then accessing the feelings of the customers about the product or service and knowing if it is actually being liked by them or not. Tracking data of the customers helps the organization in many ways. This study was done to get familiarized with the concept of data analytics and how social media plays an important role in it. Furthermore, Web scraping of Twitter and YouTube data was done following which a standard dataset was selected to do the other analytics. The field of sentiment analysis was used to get the emotions of the people. Logistic regression and RNN-LSTM models were used to perform the same, and then, the results were compared. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.