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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. -
Kibble-Zurek scaling and spatial statistics in quenched binary Bose superfluids
The emergence of order from an initially uncorrelated state across a phase transition is a central problem in quantum many-body physics, particularly in multicomponent systems where interactions between components lead to rich nonequilibrium dynamics. While defect formation is known to follow universal scaling laws, prior studies have focused mainly on defect density, leaving their spatial organization largely unexplored. Here we show that gradually tuning the chemical potential in a two-dimensional binary Bose gas drives condensation into either a miscible or immiscible phase. In the immiscible regime, domains form whose number, size, and boundary length obey Kibble-Zurek (KZ) scaling and evolve self-similarly. In the miscible regime, vortices emerge with KZ scaling. In both cases, the spatial distribution of vortices and domains is well described by a Poisson point process with KZ-determined density. These results reveal universal features of far-from-equilibrium dynamics and provide a framework to characterize stochastic geometry in multicomponent quantum systems. The Author(s) 2026. -
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. -
Kinematic study of molecular gas in cometary globule - LBN 437
Bright-rimmed, cometary-shaped star-forming globules, associated with H ii regions, are remnants of compressed molecular shells exposed to ultraviolet radiation from central OB-type stars. The interplay between dense molecular gas and ionizing radiation, analysed through gas kinematics, provides significant insights into the nature and dynamic evolution of these globules. This study presents the results of a kinematic analysis of the cometary globule, Lynds' Bright Nebula (LBN) 437, focusing on the first rotational transition of CO and CO molecular lines observed using the Taeduk Radio Astronomy Observatory. The averaged CO spectrum shows a slightly skewed profile, suggesting the possibility of a contracting cloud. The molecular gas kinematics reveals signatures of infalling gas in the cometary head of LBN 437, indicating the initial stages of star formation. The mean infall velocity and mass infall rate towards the cometary head of LBN 437 are 0.25 km s and 5.08 10 M yr, respectively, which align well with the previous studies on intermediate or high-mass star formation. 2025 The Author(s). -
Kinetic characterisation of proteases from Punica granatum, Musa acuminata, Carica papaya, and Ananas comosus as sustainable enzyme sources
Proteases are vital industrial enzymes, contributing approximately 60% of the global enzyme market, by facilitating protein hydrolysis. Fruit peels, a major agricultural waste, offer a sustainable alternative for commercial enzyme production. This study investigates the proteases extracted from the peels of Punica granatum, Musa acuminata, Carica papaya, and Ananas comosus, with a primary focus on determining their optimal pH, temperature, and substrate specificity. Additionally, K? and V??? kinetics were assessed to characterize their catalytic efficiency. Optimal proteolytic activity was observed at pH 8 and 30C for P. granatum, pH 7 and 30C for M. acuminata, pH 8 and 30C for C. papaya, and pH 7 and 50C for A. comosus. substrate specificity of protease was assessed using casein, fish meal, soybean meal, black soldier fly larvae, bovine serum albumin, and egg albumin, revealing broad applicability, especially in P. granatum peels. The stability of P. granatum proteases across substrates suggests multiple isoforms or a flexible active site. Kinetic analysis using Lineweaver-Burk plots revealed Vmax and KM values of 8.45 mol/min/mL and 3.81 M (P. granatum), 4.56 mol/min/mL and 10.08 M (M. acuminata), 2.98 mol/min/mL and 2.84 M (C. papaya), and 2.97 mol/min/mL and 11.38 M (A. comosus) respectively. Among the tested fruit peels, P. granatum exhibited the highest reaction rate, while C. papaya demonstrated the highest substrate affinity, making them as promising candidates for feed supplementation and industrial enzyme applications. The broad substrate specificity and high catalytic efficiency of P. granatum further reinforce its potential for use in feed formulations, enhancing protein hydrolysis and improving nutrient availability. These findings highlight the significant potential of fruit peel-derived proteases in promoting sustainable enzyme production and advancing bioeconomic applications. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2026. -
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. -
KleinGordon nonlocal dynamics of porous piezo-thermoelastic medium with surface irregularity under fractional-order modified LS model
The miniaturization of devices alongside advances in thermal management technologies necessitates the generalization of heat conduction and thermal elastic coupling to faithfully represent material responses at ultrashort temporal scales. Motivated by viscoelastic mechanical analogies, this work develops an analytical framework for investigating vibrational behavior in an orthotropic, size-dependent piezo-thermoelastic substrate featuring voids, modeled within the Modified LordShulman (MLS) thermoelasticity theory augmented by fractional derivatives. Employing the KleinGordon nonlocal elasticity formulation, the governing equations of motion are rigorously derived. The normal mode method facilitates the examination of coupled thermoelectro-mechanical excitation phenomena. Emphasis is placed on a corrugated interface contiguous to a vacuum, where comprehensive boundary conditions encompassing thermal, electrical, mechanical, and stress equilibria are imposed to determine fundamental field variables. The study systematically evaluates the influence of pivotal parameters, including temporal evolution, nonlocality characteristics, and spatial coordinates, on the thermomechanical and electrical responses, with outcomes substantiated through detailed graphical representations. Although previous investigations have addressed vibrations in porous piezo-thermoelastic media under varying theoretical constructs, the current research uniquely elucidates the dynamic response of a size-dependent porous piezo-thermoelastic medium with a corrugated surface within the fractional-order modified LordShulman framework, marking a significant advancement in the modeling of smart microstructured materials. The Author(s), under exclusive licence to Springer Nature B.V. 2026. -
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. -
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. -
Knee-Osteoarthritis Detection Using Deep Learning
Arthritis is a condition that causes pain, stiffness, inflammation, and other symptoms in one or more joints. It is more common in older adults and tends to worsen with age. There are different types of arthritis, but osteoarthritis is the most prevalent. A study discusses the use of Convolutional Neural Networks (CNN) for detecting knee osteoarthritis. CNN is a deep learning algorithm that can analyze data and classify images accurately, like the human brain. The purpose of this study is to classify different knee X-ray images to predict the severity of the disorder, allowing for early detection and lifestyle changes to prevent the disease from worsening. An online tool has been developed to diagnose knee osteoarthritis and provide remedies based on various K-grade predictions. This tool can help patients understand their knee's condition and take necessary measures to manage the disease. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Knots of the umbilical cord: Incidence, diagnosis, and management
Knot(s) of the umbilical cord have received emphasis because the clinical assessments and sonographic literature show a crucial role in fetal outcomes. The true umbilical cord knot could be a knot in a singleton pregnancy or an entanglement of two umbilical cords in monoamniotic twins. Clinical manifestations are almost silent, which can raise clinical challenges. They worsen outcomes, and the pathology can be easily missed during prenatal visits because ultrasonographers do not pay attention to the cord during an obstetric ultrasound scan. However, most medical centers now have ultrasound machines that improve fetal assessment. The umbilical cord should be routinely evaluated during a fetal assessment, and suspicion of an umbilical cord knot can be more frequently diagnosed and is detected only incidentally. Clinical outcome is usually good but depends on the knot's characteristics and if it is tight or loose. In this review, we discuss pathophysiology, the theories on formation, the main risk factors, ultrasound signs and findings, different opinions in the management, and features of pregnancy outcomes feature. 2024 International Federation of Gynecology and Obstetrics. -
Knowing Discovery from Legal Documents Dataset using Text Mining Techniques
International Journal of Computer Applications Vol.66, No.23, pp. 32-34 ISSN No. 0975-8887 -
Knowing Human Gaze
Studies have examined the understanding of human gaze in animals. Gaze following in human infants has been successfully demonstrated, showing that infants can follow the eye direction of another though the ability to represent the intentions of gaze behaviour emerges only after the onset of object permanence or a period over eighteen months of age. Comparative studies indicate that gaze following or gazing at humans vary in different species of animals. Wolves for example were observed to look least at human experimenters during a performance task, whereas dogs turned to look more often and monkeys looked to a lesser extent at their experimenters. It is of interest to discuss that gaze following is different from understanding gaze. Dogs have been observed to be successful in gaze following and almost at par with young children. However, the intention of another's gaze is not as clearly understood by dogs as in humans. We (Ittyerah and Gaunet 2009) have shown that the response of dogs to the gaze of their caretakers did not differ between guide dogs of the blind and pet dogs of sighted people. Both groups of dogs seemed to have responded to the head direction of their caretakers during a search task, indicating that the visual status of the caretaker did not affect the dog's understanding of human gaze. Further some breeds of dogs may respond more often to gaze, while larger inter ocular distances in large dogs could facilitate their response to visual cues. Therefore awareness of interactions between breed and physical size could contribute to studying gaze and cognition among dogs. 2012 Nova Science Publishers, Inc. -
Knowing human gaze
Studies have examined the understanding of human gaze in animals. Gaze following in human infants has been successfully demonstrated, showing that infants can follow the eye direction of another though the ability to represent the intentions of gaze behaviour emerges only after the onset of object permanence or a period over eighteen months of age. Comparative studies indicate that gaze following or gazing at humans vary in different species of animals. Wolves for example were observed to look least at human experimenters during a performance task, whereas dogs turned to look more often and monkeys looked to a lesser extent at their experimenters. It is of interest to discuss that gaze following is different from understanding gaze. Dogs have been observed to be successful in gaze following and almost at par with young children. However, the intention of another's gaze is not as clearly understood by dogs as in humans. We (Ittyerah and Gaunet 2009) have shown that the response of dogs to the gaze of their caretakers did not differ between guide dogs of the blind and pet dogs of sighted people. Both groups of dogs seemed to have responded to the head direction of their caretakers during a search task, indicating that the visual status of the caretaker did not affect the dog's understanding of human gaze. Further some breeds of dogs may respond more often to gaze, while larger inter ocular distances in large dogs could facilitate their response to visual cues. Therefore awareness of interactions between breed and physical size could contribute to studying gaze and cognition among dogs. -
Knowledge of sexual abuse and resistance ability among children with intellectual disability
Background: Sexual abuse is a global concern among children with intellectual disabilities. Sexual abuse is frequent and long-lasting when the victim is a child with an intellectual disability. Moreover, the rate of sexual abuse is two to eight times the rate in the general population. Objective: This study aimed to investigate the knowledge of sexual abuse and resistance ability among children with intellectual disabilities. Participants and setting: The study was conducted among 120 children with mild or moderate intellectual disabilities attending twelve schools for specific purposes. Methods: We adopted a cross-sectional design to assess knowledge and resistance ability. Personal Safety Questionnaire and Modified What If Situation Test were administered verbally during individual interviews. Institutional Ethics Committee approved our study. Results: Current study suggests that children with intellectual disabilities have average knowledge (M = 6.6, SD = 1.6) regarding sexual abuse. More than 90 % of children demonstrated poor reporting skills. Although children exhibited good knowledge in differentiating appropriate from inappropriate touch requests, most children reported they would not disclose this incident to anyone. Conclusions: This study strongly suggests the need for a structured training program for children with intellectual disabilities to prevent sexual abuse. 2022 Elsevier Ltd -
Knowledge or Personality: An Empirical Analysis of Behavioural Finance and Investor Cognitive Biases
This research attempts to analyze to what extent knowledge and tactics or enduring personality traits predict investor behaviour and cognitive biases in portfolio investment. This study is based on exploring a wide-ranging dataset: responses to a questionnaire survey together with transactional data of the same individual customers of an Indian stock company. From the questionnaire survey, the authors estimate measures of domain-general personality traits, such as the big five, as compared to the knowledge, financial literacy, competency, and attitude specific to investor equity trading. The results show the dominance of knowledge and tactics measures over personality-related measures when predicting nine different dependent variables of investment performance, investor cognitive biases, and portfolio investment activity. This research concludes with the discussion of the findings and with insights into theory and managerial implications. Copyright 2022, IGI Global. -
Knowledge society and the era of post-truth: Challenges to democracy
The future of any country in the contemporary era lies in its ability to harness the knowledge potential. The fruits of knowledge society have transformed the terrain of social and political scenario of countries around the world. Democracy as a form of government, to be successful, requires a critically-engaged and politically literate population. Democracy, therefore, requires not only political literacy but also media and digital literacies given the influence of media in our lives. If democracy is viewed as a relationship between knowledge and power, there needs to be a strong distinction between the ideas, the truth of power and the power of truth. The term, 'Post-truth', signifies that objective facts have become less influential in shaping public opinion than appeals to emotion and personal beliefs. The political processes in various democracies seem to have become more managerial and technologically fixated. There has been significant erosion in the ideas of transparency of information and political leadership has become nothing but a propaganda exercise. The paper analyses how the information technology revolution and the surge of new media has impacted the political processes in democracies, and presents the phenomenon of post-truth as a threat to the modern democratic systems. 2019 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (DVK, Bangalore). -
Knowledge transfer: An information theory perspective
Personalization and codification are two dominant knowledge transfer (KT) mechanisms found in organizations and organizational networks. This paper proposes a theoretical model of KT that explains organizations' choice of KT mechanisms in terms of the tacitness of knowledge being shared and the corresponding information content. Shannon's entropy, an information theoretical concept, has been used to define the constructs of tacitness and information content and explain their influence on the choice of the corresponding KT mechanisms. Contributions of the paper include (a) use of information content as a predictor of the choice of KT mechanisms, (b) development of an expression for tacitness, and an intuitive explanation of the tacit-explicit continuum, (c) characterization of product variety in terms of information content, and (d) development of a KT theoretical model that can be operationalized for predicting the choice of KT mechanisms in real-life situations. 2017 The OR Society.