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Local community involvement in wildlife resorts: Issues and Challenges
The Global Code of Ethics for Tourism Article 5 states that tourism should be a beneficial activity for host countries and communities (UNWTO). The code also emphasises on equitable distribution (between host countries and communities) of the economic and sociocultural benefits generated by tourism activities. The tourism resorts and accommodation sector have to involve local communities in socio-economic activities and priority should be given to local manpower. A wildlife resort has vast opportunities to involve local communities in their day to day operation by purchasing local products, promoting local festivals, providing employment opportunities to locals, and involving local communities in decision-making. Wildlife resorts can also promote local culture, create environment awareness among local people, provide educational support to the local children, and support development of infrastructure and medical facilities for the locals. Though local communities can be involved in various activities of wildlife resorts, it is essential to address the issues and challenges that hinder wildlife resorts from doing so. This paper attempts to determine the issues and challenges faced by wildlife resorts in involving local communities in their day to day operations and suggests ways and means to overcome those challenges. The scope of the study covered selected wildlife resorts in Karnataka. The targeted respondents of the research survey were resort managers and data were collected using open-ended questions to understand real-time issues and challenges involving local communities in resort activities. The data were then analysed using thematic text analysis. The findings from the study will help explore means of providing a better framework which will help wildlife resorts overcome issues and challenges involving local communities. The Author(s) 2017. -
Local Hearts, Global Minds: Using SEL to Prevent Bullying in Resource- Constrained Establishments
This chapter explores the role of Socio- Emotional Learning in developing bullying prevention strategies in resource constrained educational establishments. By applying established theoretical frameworks such as the CASEL model, Bronfenbrenners ecological systems theory and Banduras social learning theory, SEL is established as a culturally adaptable approach to combat bullying in under- resourced settings. By drawing on case studies from developing and under- developed countries such as India, Zimbabwe, and Ghana, efficacy of simple interventions are highlighted. Techniques such as circle time, storytelling, peer mentoring, and theatre based interventions are proposed as tools to boost empathy, emotional regulation and prosocial behavior in accordance with SEL. Evidence based effectiveness of SEL in resource constrained environments allows for it to be proposed as a simple but efficient method ready to be integrated into programs as a cost effective approach against bullying. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Local post-hoc interpretable machine learning model for prediction of dementia in young adults
Dementia is still the prevailing brain disease with late diagnosis. There is a large increase in dementia disease among young adults. The major reason is over indulgence of young adults on social media resulting in denial of disease and delayed clinical diagnosis. Dementia is preventable and curable if diagnosed at an early stage, however, no attempts are being made to mitigate dementia in young adults. Today artificial intelligence (AI) based advanced technology with real-life consultations in clinical or remote setups are proved beneficial and is used to detect dementia. Most AI-based test is dependent on computer-aided diagnosis (CAD) tools and uses non-invasive imaging technology such as magnetic resonance imaging (MRI) data for disease diagnosis. In this paper, a local post-hoc interpretable machine learning (LPIML) model for prediction of dementia in young adults is proposed. The performance parameters are computed and compared based on accuracy, specificity, precision, F1 score and recall. The proposed work yields 98.87% training accuracy on original images and 99.31% training accuracy on morphologically enhanced images. The performance results are intrinsic and intuitive in learning the prediction results of individual case. The adoption of the proposed work will accelerate the diagnosis process in the era of digital healthcare. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
Local self-government in India: A critical analysis through the lens of democratic decentralization
Local Self-Government (LSG) existed in a variety of forms throughout India. LSG was seen as an effective form of day-to-day management and administration in rural areas in India. This ensured a democratic form of administration even during the time of monarchs. Although it had a setback during the Muslim and British rulers period, it continued to exist. Gandhi raised his voice for increased decentralization and self-rule of the villages. Although the Constitution did not provide such an option, many committees opined that the local governance should be entrusted to the local people. Accordingly, LSG got constitutional recognition through the 73rd and 74th Amendments in 1992. It provided a three-tier system of governance at the regional level as well. Using analytical and critical lenses, this chapter examines the functioning of the constitutionally established LSG model through the lens of democratic decentralization to expose how far it has achieved its objective. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Localised actor roles in post-disaster housing recovery: A case study from Kerala
The effectiveness of post-disaster housing reconstruction (PDHR) is increasingly being challenged by the frequency and complexity of climate-induced disasters. In the Indian state of Kerala-particularly the highland regions of Kottayam and Idukki-landslides and floods have caused significant housing losses in recent years. While the government initiated housing recovery interventions after the 2021 landslide event, multiple civil society actors, including faith-based organisations, political parties, and professional groups, also participated in reconstruction efforts. This study examines the actor-specific approaches to community consultation in PDHR and their impact on beneficiary satisfaction. Using a qualitative case study design, the analysis identifies variations in participation across planning, design, and construction stages, and maps these to outcomes such as reconstruction speed, satisfaction levels, and community cohesion. While some actors offered comprehensive engagement strategies, others limited their consultation, resulting in mismatches between needs and outcomes. Findings suggest that community consultation remains uneven and often symbolic, with beneficiaries perceiving external aid as benevolence rather than entitlement. The study underscores the importance of meaningful participation in PDHR, especially in the context of localized climate events. These insights offer practical implications for designing inclusive recovery frameworks and enhancing community resilience in hazard-prone regions. The Authors, published by EDP Sciences, 2025. -
Localization Method for Camera Networks in Surveillance System
The significance of prevention and mitigation of critical issues especially in the homeland security has been increasing day by day. Emergence of autonomous video analytics tools greatly helped in the prevention of security threats. The recognition of video analytics for anomaly detection based on a set of unsupervised approaches has many fundamental technical challenges. This entails autonomous object localization and tracking technique especially in the presence of occlusion. This paper focuses on deriving a solution for the object detection and tracking in a heterogeneous camera network. The object tracking method is mainly based on Kalman filter whereas frame difference algorithm is used for object localization. This detection and tracking solution is expected to significantly reduce the effect of occlusion while tracking the anomaly. The organisation of the thesis is done into various chapters. The first chapter contains an introduction to the video surveillance system and the need for an unsupervised approach. This chapter also states the objective of the research. The solution overview gives high level solution architecture of the proposed system. The second chapter focus on the literature overview in which the citation from different papers in the field of video analytics, Kalman filter implementation and camera configuration has been referred. Chapter 3 provides the methodology in which a brief introduction to the basic algorithms used in the solution, the Kalman filter and the frame difference algorithm, are discussed. This is followed by the solution architecture of the proposed system. Chapter 4 shows the Matlab implementation of the mentioned algorithms. In Chapter 5, the results of the implementation are discussed. Chapter 6 talks about the summary of the work done and conclusion. This chapter also includes the future enhancements suggested. -
Localizing and Classifying Kannada Texts Using a YOLO-Based Approach
Extracting handwritten characters from the scanned documents is a critical step due to the inherent complexities of various writing styles, inconsistent alignments, multi-touch scenarios, and overwriting characters. Expanding upon the real-time object detection capabilities of YOLOv8 (You Only Look Once), the current paper presents an experiment utilizing a dataset of 2000 handwritten images. This dataset combines the standard dataset (Chars74K) with the custom dataset featuring multi-touch handwritten text, encompassing both individual characters and character combinations that form words. The annotations were created using the Roboflow application and exported to a yaml (yet another markup language) file. The hybrid dataset was split into training, validation, and testing sets. The evaluation process yielded an accuracy of 96.8% at a threshold of 0.5 for recognizing and classifying the characters. The result suggests a positive correlation between training dataset size and model accuracy. Further, fine-tuning the hyperparameters could increase the accuracy upto 98.4%. Additional experiments were conducted to compare YOLOv8 and Detectron2 with Faster R-CNN. The results demonstrated that YOLOv8 offers substantially faster inference times, while Detectron2 with Faster R-CNN exhibited marginally higher accuracy in few classes. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Locating Indian universities in knowledge societies: A critique
Knowledge societies characterize a defining feature of the present era. Veering away from their initial connotation of scientific temper and reasoning, today, they assume a new meaning in which the basis of economy, polity, and social action is knowledge. In the post-capitalist, post-industrial societies, knowledge has become the foundation of industrial productivity and social wellbeing. The crux of knowledge production has been shifting from the traditional disciplinary contexts promoted by academic interests in the universities to its applications for better productivity and wellbeing. Nevertheless, productivity and usefulness are accorded an epistemological appeal in defining what counts as knowledge. In this context, the present paper discusses the changes in knowledge production and dissemination processes in knowledge societies and their implications for universities in India. 2019 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (DVK, Bangalore). -
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Locations of environmental marginalisation and resistance: mapping environmental movements in India
This paper intends to revisit some of the environmental movements in India to examine how environmental exploitation intersects with the social locations of those affected, drawing on insights from prominent environmentalists and historians like Madhav Gadgil and Ramachandra Guha. Their work underscores that environmental movements in India often come from the grassroots, driven by the poor. In countries like India, socio-economic marginalisation and poverty intensify the impact of environmental degradation on the poor. Moreover, the concept of development in India remains largely detached from social welfare. Similarly, the compensations for the displacement and other damages caused by unsustainable development are also highly influenced by social hierarchies like caste. The significant involvement of participant protesters from socially and economically marginalised communities in both historical and ongoing environmental movements highlights their vulnerability and underscores the deep-seated inequalities they confront. Their active participation reflects not only their struggle against environmental degradation but also their fight to reclaim and protect their lost land. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
Log-Base2 of Gaussian Kernel for Nuclei Segmentation from Colorectal Cancer H and E-Stained Histopathology Images
Nuclei Segmentation is a very essential and intermediate step for automatic cancer detection from H and E stained histopathology images. In the recent advent, the rise of Convolutional Neural Network (CNN), has enabled researchers to detect nuclei automatically from histopathology images with higher accuracy. However, the performance of automatic nuclei segmentation by CNN is fraught with overfitting, due to very less number of annotated segmented images available. Indeed, we find that the problem of nuclei segmentation is an unsupervised problem, because still now there is no automatic tool available which can make annotated images (nuclei segmented images) accurately, to the best of our knowledge. In this research article, we present a Logarithmic-Base2 of Gaussian (Log-Base2-G) Kernel which has the ability to track only the nuclei portions automatically from Colorectal Cancer H and E stained histopathology images. First, Log-Base2-G Kernel is applied to the input images. Thereafter, we apply an adaptive Canny Edge detector, in order to segment only the nuclei edges from H and E stained histopathology images. Experimental results revealed that our proposed method achieved higher accuracy and F1 score, without the help of any annotated data which is a significant improvement. We have used two different datasets (Con-SeP dataset, and Glass-contest dataset, both contains Colorectal Cancer histopathology images) to check the effectiveness and validity of our proposed method. These results have shown that our proposed method outperformed other image processing or unsupervised methods both qualitatively and quantitatively. 2023 SPIE. -
Logistic growth and SIR modelling of coronavirus disease (COVID-19) outbreak in India: Models based on real-time data
The logistic growth model and the Susceptible-Infectious-Recovered (SIR) framework are utilized for the mathematical modelling of the Coronavirus disease (COVID-19) outbreak in India. Karnataka, Kerala and Maharashtra, three states of India, are selected based on the pattern of the disease spread and the prominence in being affected in India. The parameters of the models are estimated by utilizing real-time data. The models predict the ending of the pandemic in these states and estimate the number of people that would be affected under the prevailing conditions. The models classify the pandemic into five stages based on the nature of the infection growth rate. According to the estimates of the models it can be concluded that Kerala is in a stable situation whereas the pandemic is still growing in Karnataka and Maharashtra. The infection rate of Karnataka and Kerala are lesser than 5% and reveal a downward trend. On the other hand, the infection rate and the high predicted number of infectives in Maharashtra calls for more preventive measures to be imposed in Maharashtra to control the disease spread. The results of this analysis provide valuable information regarding the disease spread in India. 2020, International Information and Engineering Technology Association. -
Loneliness and Psychological Distress among Female School and University Students in India: Mediating Role of Coping Style, Social Support and Resilience
Loneliness and psychological distress are major psychological health issues among the student community because of social and continuously increasing academic pressure. Female students are vulnerable to these challenges, with a risk of severe mental health issues. In the present study, we aimed to examine loneliness and psychological distress among female students in India, assessing how social support, coping style, and resilience mediate these effects. Through a web-based survey, 687 responses were collected from female students across various educational institutions, including high school (n=292) and college/university (n=395) students in India. Standardized tools were used to gather data on resilience, psychological distress, coping styles, social support, and loneliness. The findings revealed moderate levels of resilience and psychological distress among the participants. The coping style scores indicated a general trend toward effective coping mechanisms. A higher level of social support was observed than a moderate level of loneliness. While coping style had a minimal mediating influence, resilience and social support were significant mediators of the association between loneliness and psychological distress. This study highlights the psychological experiences of female students in India and makes a significant contribution by providing empirical evidence on the unique roles of protective factors such as resilience and social support against psychological distress and loneliness. These findings are crucial in promoting a targeted mental health framework and support systems for students in similar contexts, reinforcing the need for a holistic approach to student mental health. 2025 The Authors. Turkish Journal of Counseling Psychology and Guidance is published by Turkish Psychological Counselling and Guidance Association -
Long memory investigation during demonetization in India
Long-range dependence (LRD) in financial markets remains a key factor in determining whether there is market memory, herding traces, or a bubble in the economy. Usually referred to as 'Long Memory', LRD has remained a key parameter even today since the mid-1970s. In November 2016, a sudden and drastic demonetization measure took place in the Indian market, aimed at curbing money laundering and terrorist funding. This study is an attempt to identify market behavior using long-range dependence during those few days in demonetization. Besides, it tries to identify nascent traces of bubble and embedded herding during that time. Auto Regressive Fractionally Integrated Moving Average (ARFIMA) is used for three consecutive days around the event. Tick-by-tick data from CNX Nifty High Frequency Trading (CNX Nifty HFT) is used for three consecutive days around demonetization (approximately, 5000 data points from morning trading sessions on each of the three days). The results show a clear and profound presence of herd behavior in all three data sets. The herd intensity remained similar, indicating a unique mixture of both 'Noah Effect' and 'Joseph Effect', proving a clear regime switch. However, the results on the event day show stable and prominent herding. Mandelbrot's specified effects were tested on an uncertain and sudden financial event in India and proved to function perfectly. Bikramaditya Ghosh, Saleema J. S., Aniruddha Oak, Manu K. S., Sangeetha R., 2020. -
Long run relationship between macroeconomic indicators and Indian sectoral indices
Investors and fund managers continuously strive to find new ways to diversify their portfolio and minimise risk exposure. The study aims to find out whether the macroeconomic indicators exert the same influence on stock prices across the entire stock market or varies across different sectors. The impact of macroeconomic indicators would not be the same on all the sectors. This paper provides empirical evidence of macroeconomic indicators such as crude oil prices, interest rates, foreign currency rates, money supply and inflation rates having a varied impact on Nifty50 index and each of the select sectoral stock indices namely, Nifty Bank, Nifty IT and Nifty financial services. The sample period runs from Jan 2009 to Jan 2019. The study employs the Error Correction Mechanism to study whether the macroeconomic indicators have the same impact across sectoral stock indices in the long run. The findings show that variations in macroeconomic variables do not trigger the same response from all the sectoral stock indices. While most of the variables chosen have a significant influence on Nifty50 index and NiftyIT; Nifty financial services and Nifty Bank remain unaffected by changes in few major macroeconomic variables or show opposite reaction than the other sectors. The findings of the study have significant implications for long term investors and investment managers for building a diversified portfolio and thereby protecting themselves from financial losses during adverse market conditions. 2019, Institute of Advanced Scientific Research, Inc.. All rights reserved. -
Long Term X-Ray Spectral Variations of the Seyfert-1 Galaxy Mrk 279
We present the results from a long term X-ray analysis of Mrk 279 during the period 2018-2020. We use data from multiple missions - AstroSat, NuSTAR and XMM-Newton, for the purpose. The X-ray spectrum can be modeled as a double Comptonization along with the presence of neutral Fe K? line emission, at all epochs. We determined the sources X-ray flux and luminosity at these different epochs. We find significant variations in the sources flux state. We also investigate the variations in the sources spectral components during the observation period. We find that the photon index and hence the spectral shape follow the variations only over longer time periods. We probe the correlations between fluxes of different bands and their photon indices, and found no significant correlations between the parameters. 2024. National Astronomical Observatories, CAS and IOP Publishing Ltd. -
Long-term adaptation study of bacterial isolates of plant growth-promoting bacteria in heat-stressed conditions
In many bacterial species, there is still a lack of comprehensive research and characterization of the basic mechanisms behind bacterial adaptation. Furthermore, it's still unclear if prokaryotes can learn by association and adaptation. Since Plant Growth Promoting Bacteria (PGPB) are essential to the preservation of plant physiology and growth across a range of stress scenarios, PGPB can be utilized to analyze this adaptation of bacteria under stress. This study examines the initial findings on adaptive flexibility in PGPB under conditions of heat stress. The performance of the isolated PGPB receiving both periodic and non-periodic heat stress was compared to that of the control group. Characteristics such as ammonia and siderophore production, phosphate utilization, and amount of indole-3-acetic acid produced, as well as antioxidant activities like DPPH activity, hydroxyl radical scavenging activity, and hydrogen peroxide scavenging activity were analysed. Following heat stress treatment, it was clear from the isolated PGPB that those under periodic stress were able to outperform the PGPB exposed to non-periodic stress in comparison to the control. When compared to the other isolates in our investigation, the two novel strains of Paenibacillus alvei SJ6 and Paenibacillus alvei SJ8, among the four isolated PGPB have demonstrated the greatest capacity to respond to sporadic heat stress. Therefore, preliminary evidence for the existence of history-dependent adaptation has been examined in this work. (2026), (University of Food Technologies Plovdiv). All rights reserved. -
Long-term Optical and ?-Ray Variability of the Blazar PKS 1222+216
The ?-ray emission from flat-spectrum radio quasars (FSRQs) is thought to be dominated by the inverse Compton scattering of the external sources of photon fields, e.g., accretion disk, broad-line region (BLR), and torus. FSRQs show strong optical emission lines and hence can be a useful probe of the variability in BLR output, which is the reprocessed disk emission. We study the connection between the optical continuum, H? line, and ?-ray emissions from the FSRQ PKS 1222+216, using long-term (?2011-2018) optical spectroscopic data from Steward Observatory and ?-ray observations from Fermi Large Area Telescope (LAT). We measured the continuum (F C,opt) and H? (F H? ) fluxes by performing a systematic analysis of the 6029-6452 optical spectra. We observed stronger variability in F C,opt than F H? , an inverse correlation between the H? equivalent width and F C,opt, and a redder-when-brighter trend. Using discrete cross-correlation analysis, we found a positive correlation (DCF ? 0.5) between the F ??ray>100 MeV and F C,opt (6024-6092 light curves with a time lag consistent with zero at the 2? level. We found no correlation between the F ??ray>100 MeV and F H? light curves, probably dismissing the disk contribution to the optical and ?-ray variability. The observed strong variability in the Fermi-LAT flux and F ??ray>100 MeV ? F C,opt correlation could be due to the changes in the particle acceleration at various epochs. We derived the optical-to-?-ray spectral energy distributions during the ?-ray flaring and quiescent epochs that show a dominant disk component with no variability. Our study suggests that the ?-ray emission zone is likely located at the edge of the BLR or in the radiation field of the torus. 2022. The Author(s). Published by the American Astronomical Society. -
Long-term optical and infrared variability characteristics of Fermi blazars
We present long-term optical and near-infrared flux variability analysis of 37 blazars detected in the ?-ray band by the Fermi Gamma-Ray Space Telescope. Among them, 30 are flat spectrum radio quasars (FSRQs) and 7 are BL Lac objects (BL Lacs). The photometric data in the optical (BVR) and infrared (JK) bands were from the Small and Moderate Aperture Research Telescope System acquired between 2008-2018. From cross-correlation analysis of the light curves at different wavelengths, we did not find significant time delays between variations at different wavelengths, except for three sources, namely PKS 1144-379, PKS B1424-418, and 3C 273. For the blazars with both B- and J-band data, we found that in a majority of FSRQs and BL Lacs, the amplitude of variability (?m) in the J band is larger than that in B band, consistent with the dominance of the non-thermal jet over the thermal accretion disc component. Considering FSRQs and BL Lacs as a sample, there are indications of ?m to increase gradually towards longer wavelengths in both, however, found to be statistically significant only between B and J bands in FSRQs. In the B-J v/s J-colour magnitude diagram, we noticed complicated spectral variability patterns. Most of the objects showed a redder when brighter (RWB) behaviour. Few objects showed a bluer when brighter (BWB) trend, while in some objects both BWB and RWB behaviours were noticed. These results on flux and colour characteristics indicate that the jet emission of FSRQs and BL Lacs is indistinguishable. 2020 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. -
Longitudinal study on noncommunicable diseases using machine learning
This longitudinal case study thoroughly explores the intricate connection between body mass index (BMI) and four key factors: physical health, psychological well-being, lifestyle choices, and the impact of diet on health. Through the analysis of longitudinal data, notable trends emerge, revealing an increase in risk factors for noncommunicable diseases (NCDs) and unhealthy behaviors over time. This highlights the combined impact of these interconnected factors on health outcomes and the risk of developing NCDs like heart disease, diabetes, and cancer. Leveraging machine learning, the study effectively identifies individuals at elevated risk for NCDs and dispels common health misconceptions, underscoring the significance of holistic wellness approaches. Serving as a beacon for the next generation, this study provides insights that contribute to shaping a healthier future. 2025 selection and editorial matter, Arun Kumar Rana, Vishnu Sharma, Sanjeev Kumar Rana, and Vijay Shanker Chaudhary; individual chapters, the contributors. All rights reserved.