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Kannada translation and validation of Wellman and Liu's theory of mind scale and children's social understanding scale in preschoolers
Background: Assessing theory of mind (ToM) in children is crucial for understanding social cognition. Wellman and Liu's ToM scale and the Children's Social Understanding Scale (CSUS) have been used to study ToM in children but are not available in the local language. Aim: This study aims to translate both scales into Kannada and validate them in preschool children. Methods: Following the rigorous WHO protocol, we meticulously translated and back-translated Wellman and Liu's ToM and CSUS into Kannada with the help of bilingual experts. Validation involved administering both scales to 118 preschool children aged 3 to 6 years from diverse urban and rural backgrounds in a cross-sectional study, ensuring the scales' applicability across different settings. Results: The Cronbach's alpha values for Wellman and Liu's ToM and the CSUS were 0.769 (95% CI 0.698 to 0.828) and 0.983 (95% CI 0.978 to 0.987), respectively, indicating high internal consistency. The test-retest reliability for Wellman and Liu's ToM scale domains ranged from 0.74 to 0.95, and for the CSUS, it was 0.99, demonstrating good reliability. Pearson's correlation between the domains of two scales ranged from 0.32 to 0.69, suggesting a moderate relationship. Conclusion: Our study findings demonstrate that Kannada translations of Wellman and Liu's ToM and CSUS have good internal consistency, test-retest reliability, and construct validity. These tools will be valuable for understanding social cognition in preschool children. 2024 Indian Journal of Psychiatry. -
Kathakali
Kathakali stamp was issued on 26 April, 2002. The Department of Posts, to commemorate the 50th anniversary of diplomatic relations between India and Japan with the issue of a set of 2 commemorative postage stamps and a miniature sheet. The theme is the rich traditions of classical performing arts Kabuki (Japan) and Kathakali (India). -
Kerala Development and the Attapadi Adivasi
The development experience of the state of Kerala in southwest India is based generally on democratic principles of equality and popular participation. This article focuses on the lives of the Adivasi1 people of Attapadi in the Palakkad district of Kerala. It argues that the state of Kerala largely treats the Adivasis as secondary citizens and ignores their right to be socially and economically empowered. The state of Kerala takes pride in its positive ranking on human development and social progress indexes but has not done enough to stop Adivasi infants from dying of malnutrition, and Adivasis demands for land rights have been disregarded. As a result, they are forced to live obscure lives in poverty and generally unable to influence their sociopolitical sphere. 2023 The Author(s). -
Kerala flood : Foreign aid and cooperative federalism /
International Journal of Research And Analytical Reviews (IJRAR), Vol.5, Issue 3, pp.464-467, ISSN No: 2348-1269. -
Keratin as a sustainable biopolymer for waste water treatment
Keratin is one of the most abundant natural polymers with potential application in various fields but is usually seen discarded as waste generated from poultry farms along roadsides and landfills. These are indeed the cheapest source of keratin protein which could be used for various applications. Owing to the structural properties, keratinous materials are now being exploited in wastewater treatment systems as adsorbents. The rich amino acid content having hydroxyl, carboxyl and amino groups has been found to be beneficial in removing contaminants from waste waters like heavy metals and dyes. Research based on this idea has received peak attention to a point where formulations of different adsorbent materials like nanofibre, biofilms and biocomposite from keratinous raw materials are now available for commercial use. This review summarises the application of keratin as an efficient adsorbent for waste water treatment providing an insight into its structure, forms of keratin used for treatments and mechanism of adsorption of different components in waste water. 2022 World Research Association. All rights reserved. -
Kernel granulometric texture analysis and light res-aspp-unet classification for covid-19 detection
This research article proposes an automatic frame work for detecting COVID -19 at the early stage using chest X-ray image. It is an undeniable fact that coronovirus is a serious disease but the early detection of the virus present in human bodies can save lives. In recent times, there are somany research solutions that have been presented for early detection, but there is still a lack in need of right and even rich technology for its early detection. The proposed deep learning model analysis the pixels of every image and adjudges the presence of virus. The classifier is designed in such a way so that, it automatically detects the virus present in lungs using chest image. This approach uses an image texture analysis technique called granulometric mathematical model. Selected features are heuristically processed for optimization using novel multi scaling deep learning called light weight residual-atrous spatial pyramid pooling (LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPPUnet technique has a higher level of contracting solution by extracting major level of image features. Moreover, the corona virus has been detected using high resolution output. In the framework, atrous spatial pyramid pooling (ASPP) method is employed at its bottom level for incorporating the deep multi scale features in to the discriminative mode. The architectural working starts from the selecting the features from the image using granulometric mathematical model and the selected features are optimized using LightRESASPP- Unet. ASPP in the analysis of images has performed better than the existing Unet model. The proposed algorithm has achieved 99.6% of accuracy in detecting the virus at its early stage. 2022 Tech Science Press. All rights reserved. -
KESMR: A Knowledge Enrichment Semantic Model For Recommending Microblogs
In today's world, there's an enormous amount of information available on the Internet. Because of this, it's become really important to come up with better and smarter ways to search for things online. The old-fashioned methods, like just matching certain words or using statistics, don't work so well anymore. They often suggest web pages that are irrelevant. As the Semantic Web keeps getting bigger, it needs algorithms for the best fit. In this paper, a way to measure how different the words used for web search. This helps in suggesting the most relevant web pages. A special algorithm that can change its settings. Our proposed method demonstrates 94% accuracy. 2023 IEEE. -
Key challenges in developing and executing higher education learners' learning outcomes
This chapter examines higher education institutions' complex obstacles in developing and implementing effective learning outcomes. It emphasizes the need for outcomes that include subject-specific and general skills, meet students' diverse requirements, align with market demands, and incorporate emerging technologies. To facilitate student success in the 21st century, institutions must address these. It examines multidisciplinary programs, technology integration, faculty training, and student participation in outcome formation. It proposes enhancing outcomes through emerging technologies, social and emotional learning, global citizenship education, and entrepreneurship education, emphasizing student-centred approaches. Effective learning outcomes are essential for fostering student success in a constantly changing environment. Case studies from India, the United Kingdom, and the United States provide insights, emphasizing India and lessons from the US and UK experiences. 2024, IGI Global. All rights reserved. -
Key factors elevating omni-channel retail experience : A study of critical capability dimensions
Increasing digital disruption is driving the necessity for Omni-Channel Retailing, compelling the integration of online and offline channels. The line between online and physical retailing is blurring as retailers intend to deliver a unified experience anytime anywhere, than a mere channel specific experience. As customer expectation for seamless experience intensifies, and retailers organizational, operational and technical barriers persist, it would be vital to formulate a suitable strategy towards e levating Omni-Channel Retail Experience. Though prior studies have observed the need for realigning the strategy around blended advantages of multiple channels, there is a limited understanding with regards to Omni-Channel Capabilities influencing customer experience elements. Besides, it is challenging to adopt all the capabilities within a competitive timeframe. Thus, the importance of prioritizing these capabilities remains fairly underexplored. This research aims to close this gap by ascertaining key Omni-Channel Dimensions and Capabilities influencing experiential aspects, pertaining to Apparel and Fashion retail, which is a leading category in India. The research first employs a qualitative study to corroborate the appropriateness of the Omni-Channel constructs identified from literature review, in the context of Indian retail market, followed by a quantitative study to validate their influence on Omni- Channel Retail Experience. The research determines key capabilities and dimensions from a retailers perspective that underpin key experience elements. The findings established new knowledge in terms of top priority capabilities towards Omni- Channel adoption, and accordingly designed a novel framework termed Capability Priority Framework as a plausible approach to elevate Omni-Channel Retail Experience. The framework is an original contribution of this research serving as an accelerator for retailers to build and reinforce key Omni-Channel capabilities. The research provides a novel perspective of extending The Dialectic Theory of retailing to a modern context such as Omni-Channel. It serves as a basis for organized retailers in India to realign their strategy towards Omni-Channel adoption, as they embark on this path. Finally, it adds to the knowledge base on Omni-Channel, providing a conceptual background towards strategic retailing and further research in this domain. -
Key factors elevating omni-channel retail experience: A study of critical capability dimensions
Increasing digital disruption is driving the necessity for Omni-Channel Retailing, compelling the integration of online and offline channels. The line between online and physical retailing is blurring as retailers intend to deliver a unified experience anytime anywhere, than a mere channel specific experience. As customer expectation for seamless experience intensifies, and retailers’ organizational, operational and technical barriers persist, it would be vital to formulate a suitable strategy towards elevating Omni-Channel Retail Experience. Though prior studies have observed the need for realigning the strategy around blended advantages of multiple channels, there is a limited understanding with regards to Omni-Channel Capabilities influencing customer experience elements. Besides, it is challenging to adopt all the capabilities within a competitive timeframe. Thus, the importance of prioritizing these capabilities remains fairly underexplored. This research aims to close this gap by ascertaining key Omni-Channel Dimensions and Capabilities influencing experiential aspects, pertaining to Apparel and Fashion retail, which is a leading category in India. The research first employs a qualitative study to corroborate the appropriateness of the Omni-Channel constructs identified from literature review, in the context of Indian retail market, followed by a quantitative study to validate their influence on OmniChannel Retail Experience. The research determines key capabilities and dimensions from a retailers’ perspective that underpin key experience elements. -
Key Indicators of Corporate Financial Status: Empirical Evidence from Indian Industrial Sector
The present pandemic situation has led to the rise in the number of financially distressed companies in the Indian business ecosystem. Stakeholders, especially shareholders, unsecured and trade creditors who do not enjoy lien on company assets, should be extra cautious about the financial status of a company before making any investment or lending decision. The present study attempts to suggest the key indicators of corporate financial status after analysing 12 ratios from the financial statements of 162 sample companies for five financial years. The suggested key indicators can assist the shareholders and creditors in differentiating a financially distressed company from a financially sound company in the Indian industrial sector. 2021 SCMS Group of Educational Institutions. All rights reserved. -
Key management solutions for database as a service: A selective survey
In todays scenario, efficient data processing is a fundamental and vital issue for almost every scientific, academic, or business organization. To tackle this issue, organizations end up installing and managing database systems to satisfy different processing needs. In case of adopting a traditional solution, the organization needs to purchase the necessary hardware, deploy database products, establish network connectivity, and hire professional people who run the system. But this solution is getting impractical and expensive as the database systems and problems become larger and complicated (El-Khoury et al., 2009). Again, traditional solution entails different costs from the perspective of the investments involved. These concerns are handled efficiently to a great extent by the fast developing technology that goes by the name, cloud computing.. 2014 by Taylor & Francis Group, LLC. -
Khadi Textiles, Women and Rural Development: An Analysis from Past to Present
The paper aims at bringing out the cultural, economic, and political importance that Khadi has for India from pre-colonial times, during the colonial rule and after independence till the present times. The paper brings out Khadis potential in rural development by solving the pressing concerns of unemployment and working conditions in developing countries like India. In spite of this, Khadis potential at rural development is subsided by the contemporary threats the industry faces due to the different agents of neoliberalism, discussed in the paper. The questionnaire report on workers collected at the Cheriyakonni production unit and information collected at the District Project Office Trivandrum; brings out that rural women are the largest workforce victims of these threats, as the industry is slowly showing signs of degradation. Methodology: The research paper is based on both quantitative and qualitative data analysis. Data is collected from Kerala Khadi and Village Industries Board (KVIB) Project Office Thiruvananthapuram through interviews. Questionnaire is conducted on workers of Cheriyakonni Production Unit, Thiruvananthapuram that operates under the board. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Khaki on Screen: Understanding the Representation of Cops in Malayalam Cinema
[No abstract available] -
Kho Kho Model: A Novel Technique for Efficient Handoff in Vehicular Ad-hoc Networks
The highly mobile nature of VANET implies that the nodes involved are constantly disconnecting and reconnecting as they switch between access points or move out of the range of their access points. In such scenarios, seamless connectivity is essential, especially when emergency services are involved. Handoff is a process in wireless communication that takes care of the switching process that happens between access points whenever a mobile device moves from one point to another. In a dynamic scenario involving vehicular nodes, this switching needs to take place between a mobile node or a fixed access point (known as RSUs), as quickly as possible. To this end, this research work proposes a novel handoff method known as the Kho Kho Model - which is loosely based on the traditional Indian sport of the same name. The model groups together nodes that are moving in the same direction, thereby effectively reducing the amount of processing required to perform handoff for a set of nodes. The use of ANN have helped to improve handoff since it can help in making decisions quickly by making use of multiple parameters including signal strength, noise, direction, and others. To improve the efficiency of the proposed handoff model, RBFNN has been used in this research. The proposed model was implemented using NS-3 simulator. The results have shown that the proposed method has a slightly better improvement in the overall NRO, a reduced average delay and reduced jitter compared to the existing handoff method employed by the IEEE 802.11p standard. 2023 IEEE. -
Kidney Abnormalities Detection and Classification Using CNN-based Feature Extraction
The presents of noises degrade the quality of ultrasound images and diminishes the disease diagnosis accuracy. Thus, an effective automatic stone and cyst detection system is beneficial to both the medical practitioners and patients. In this paper, an automatic detection and classification system for kidney stone and cyst image is proposed. The Gaussian filtering and Contrast Limited Adaptive Histogram Equalization (CLHE) techniques are applied to improve the quality of the images. In the next step, segmentation has been done based on the entropy of the image. The gamma correction technique has been applied to improve the overall brightness and an optimal global threshold value is selected to extract the region. The CNN model has attained much attention in medical image recognition and classification. In this paper, the pre-trained model ResNet-50 is utilized as a feature-extractor and Support Vector Machine as classifier to categorize the normal, cyst and stone images. The CNN model is analyzed with various other classification models such as k-nearest neighbor, decision tree and Nae Bayes. The results demonstrate that the ResNet-50 with supervised classification algorithm SVM is an optimal solution for analyzing kidney diseases. 2022 IEEE. -
Kingship and Vedic Literature: Inflections, Deflections and Reflections
This chapter seeks to examine the figurations and configurations of 'kingship' reflected in different Vedic literary narratives in general and particularly aims at foregrounding how 'kingship' that happens to be one of the oldest forms of political governance, originated in Vedic times and how it became multifaceted with the passage of time. This chapter particularly seeks to employ three epistemological lenses - govern(mentality), sacral (infra)structuralism and planetarity - to lay down how the Vedic notion of 'kingship' underwent 'intensive' changes and how it stood in conformity with varied dimensions of contemporary political ecology. Besides that, this chapter aims at bringing out how Vedic notion of 'kingship' embodies the limits of 'human' by means of performing a liaison between the Almighty and ordinary human beings. Finally, at the end, royal haecceities of Vedic 'kingship' are critically taken up to facilitate readers to grasp the ontological and onticological fluidity of Vedic understanding of 'kingship'. 2024 selection and editorial matter, Nizar Zouidi; individual chapters, the contributors. -
Kitchen Waste Derived Porous Nanocarbon Spheres for Metal Free Degradation of Azo Dyes: An Environmental Friendly, Cost Effective Method
A porous nanocarbon spheres (PNCSs) were prepared from kitchen waste and successfully used for the metal and oxidant free degradation of azo compounds. The PNCSs obtained by the pyrolysis of onion peel, at 1000C, were found to be effective catalysts for the reductive degradation of azo dyes in presence of hydrazine hydrate. The reductive cleavage of azo bonds (N=N) was achieved under microwave irradiation. The degradation process was completed in a span of 1040min; the process was monitored by ultravioletvisible spectroscopy. Fourier transform infrared spectroscopy was also used for the illustration of azo degradation. Interestingly, the reductive degradation of azo dyes produced corresponding amines and they were successfully reused for the preparation of fresh azo compounds. The work, therefore, highlights the valorization of largely produced kitchen-wastes to the sustainable PNCSs and it also provides a platform to demonstrate their applicability as highly cost-effective catalysts for bulk scale chemical transformations. Graphical Abstract: [Figure not available: see fulltext.]. 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
KMetaTagger: A Knowledge Centric Metadata Driven Hybrid Tag Recommendation Model Encompassing Machine Intelligence
The emergence of Web 3.0 has left very few tag recommendation structures compliant with its complex structure. There is a critical need for newer novel methods with improved accuracy and reduced complexity for tag recommendation, which complies with the Web 3.0 standard. In this paper, we propose KMetaTagger, a knowledge-centric metadata-driven hybrid tag recommendation framework. We consider the CISI dataset as the input, from which we identify the most informative terms by applying the Term Frequency - Inverse Document Frequency (TF-IDF) model. Topic modeling is done by Latent Semantic Indexing (LSI). A heterogeneous information network is formalized. Apart from this, the Metadata generation quantifies the exponential aggregation of real-world knowledge and is classified using Gated recurrent units(GRU). The Color Harmony algorithm filters out the initial feasible solutions into optimal solutions. This advanced solution set is finalized into the tag space. These tags are recommended along with the document keywords. When the suggested KMetaTagger's performance is compared to that of baseline techniques and models, it is found to be far superior. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
KMSBOT: enhancing educational institutions with an AI-powered semantic search engine and graph database
In the rapidly evolving field of education, a semantic search engine is essential to efficiently retrieve knowledge experts data. Universities and colleges continuously generate a vast amount of educational and research data. A semantic search engine can assist students and staff in efficiently searching for required information in such a big data pool. The existing systems have limitations in providing personalized recommendations that align with the individual learning objectives of students and scholars, thus hindering their educational experience. To address this, this paper proposed a KMSBOT. This novel recommendation system effectively summarizes academic data and provides tailored information for students, research scholars, and faculty, enhancing educational experiences. This paper meticulously details the development of KMSBOT, which comprises a neo4j-based knowledge graph technique, the NLP method for data structuring, and the KNN machine learning model for classification. The system employs a three-module approach, utilizing data structuring, NLP processing, and semantic search engine integration. By leveraging Neo4j, NLTK, and BERT in Python, this proposed work ensures optimal performance metrics such as time, accuracy, and loss value. The proposed solution addresses traditional recommendation systems limitations and contributes to a brighter future, improving user satisfaction and engagement in academic environments. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.