Browse Items (14421 total)
Sort by:
-
Between Floods and Climate Change: Revisiting the Mishing Community of Majuli Island, Northeast India
The transformation of monsoon rainfall patterns in India, largely attributed to climate change, is leading to more frequent and severe floods. These escalating challenges underscore the imperative of prioritising adaptive measures, given the intrinsic link between humans and climate change. This research conducted in Majuli Island, a highly vulnerable region in Indias northeast, aims to understand current adaptive strategies and assess potential risks from impending physical exposures. Empirical evidence was collected using purposive sampling in two flood-prone villages. The objective was to revisit the Mishing communitys experiences with annual flooding and climate challenges. Thematic analysis interpreted the qualitative findings. Implications for community-based adaptation and sustainable practices are discussed for future flood and climate challenges. The study emphasises strengthening ecosystem-based adaptation through multi-sectoral networking in Majuli Island, Northeast India. 2024 IOS Press BV. All rights reserved. -
Skin as Script Embodied Archives of Post-headhunting in Longwa, Nagaland
[No abstract available] -
Towards resilience: navigating local knowledge in flood risk management strategies in Majuli Island, Assam
Scientific knowledge of climate change and its latent effects is important. But it often lacks the expertise of local communities. This paper emphasises the importance of understanding local knowledge within the dynamics of vulnerability and resilience. It also offers insights into the applicability of these knowledge systems, providing valuable lessons and challenges on local knowledge in flood risk management. The study was conducted within a qualitative framework, utilising a case study design, in Majuli Island. Data were collected through 20 key informant interviews. Findings reveal that the diverse dimensions of local knowledge among Indigenous communities strengthen mitigation, coping, and adaptation strategies, enabling them to endure recurring floods. The evidence presented can guide government and non-governmental organisations (NGOs) in Majuli in integrating local knowledge into their interventions. By documenting and critically analysing existing practices, this paper adds to the growing literature on local knowledge in disaster research and practice. 2026 Informa UK Limited, trading as Taylor & Francis Group. -
From resistance to readiness: Leveraging neuroscience perspectives for successful change management in the manufacturing sector
Change initiatives often encounter resistance from employees, impeding successful implementation. Leveraging insights from neuroscience can provide valuable guidance in navigating this resistance and promoting readiness for change. Social experiences in a work environment affect the brain positively or negatively. By understanding the brain's response to change, change managers can create a supportive environment and communicate accordingly. This study examines the impact of social experiences on readiness to change among employees in the manufacturing sector from a neuroscience perspective. SCARF is a neuroscience-based model that evaluates five dimensions of social experiences such as status, certainty, autonomy, relatedness, and fairness. This quantitative study is based on data collected from different manufacturing organisations. The results of this study provide insights into how change managers can address these social domains to promote successful change initiatives and improve employee readiness to change. 2023 by IGI Global. All rights reserved. -
A Comparison of 2 Step Classification with 3-Class Classification for Webpage Classification
The content over internet increasing significantly each year and the web page classification is an essential areas of work upon for web-based information management, content retrieval, data scrapping, content filtering, advertisement removal, contextual advertising, expanding web directories etc. Multiclass classification methods is more popular and commonly use to classify web pages, and 2 step classification is our proposed system. In 2 step classifier, we use 2 primary model which works serially and perform binary classification at each level. The primary source of dataset contain thousands of URL(Uniform Resource Locator) of web pages. The content on webpage is extracted and stored on system to avoid the loss of data sue to the change in URL. The comparison between the two methods validated the system improvement and improved in different metric such as precision and recall using 2 step classification technique. 2 step classification technique is faster while training and also shows performance improvement. There proposed system shows improvement in the performance of the results but not something significant. 2022 IEEE. -
Work motivation of teachers: Relationship with organizational culture /
European Journal Of Educational Sciences, Vol.1, Issue 1, pp.547-560, ISSN No: 1857-6036. -
Digital awakening religious communication in a virtual world
This paper explores the religious presence and possibilities in the virtual world. An analysis of communication progress leads to the present scenario of new media environment. Based on the idea of revelation and a system of autopoiesis, religion appears like a closed communicator. Religion's communication needs to be placed within the context of evolving new media environment. Basing on McLuhan's theory of extension, religious narratives need new forms of presence in the digital world. When it comes to diffusion of innovation (Everett Rogers) the state of religion appears precarious. From a communication perspective adoption of innovation by religion can come under the category of 'laggards' and 'luddites'. The transference of religion's presence from the real to the virtual demands new innovative and participatory models to serve the digital natives. 2015 Journal of Dharma: Dharmaram Journal of Religions and Philosophies (DVK, Bangalore), ISSN: 0253-7222. -
A Video Surveillance-based Enhanced Collision Prevention and Safety System
Road traffic crashes that result in fatalities have become a global phenomenon. Therefore, it is imperative to use caution and vigilance while being on the road. Human mistake, going over the speed limit, being preoccupied while driving or walking, disobeying safety precautions, and other factors can also contribute to such unforeseen accidents or injuries, which can result in both bodily and material loss. So, safety is what we seek to achieve. Furthermore, as the number of automobiles has increased, so too have collisions between vehicles and pedestrians. Using computer vision and deep learning approaches, this research seeks to anticipate such encounters. The data often comes from traffic surveillance cameras in video formats. We have therefore concentrated on video sequences of vehicle-pedestrian collisions. We begin with a detection phase that includes the identification of vehicles and pedestrians; for this phase, we employed YOLO v3 (You Only Look Once). YOLO v3 has 80 classes, but we only took six of them: person, car, bike, motorcycle, bus, and truck. Following detection, the Euclidean distance approach is used to determine the interspace between the vehicle and the pedestrian. The closer the distance between a vehicle and a pedestrian, the more likely it is that they will collide. As a result, pedestrians in risk are located, and once we are aware of the pedestrians in danger, we search for nearby safer regions to alert them to head to the nearest location that is secure. Grenze Scientific Society, 2023. -
Vision Based Vehicle-Pedestrian Detection and Warning System
Road Sense must be respected and obeyed by both the pedestrian and the driver. Moreover, urbanization has led to a steadfast rise in the fleet of vehicles, their speed, as well as non-compliance with road safety measures, and other such factors have provoked an inescapable increase of accidents in road traffic involving pedestrians. Pedestrian collisions can be predicted and prevented. At the very basic, there has to be vehicle and pedestrian detection along with speed estimation, which can be further applied to Vehicle-Pedestrian Collisions and various emerging fields like Industrial Automation, Transportation, Automotive, Security/Surveillance, or in Dangerous environments. This paper reviews the literature on vehicle and pedestrian detection based on two significant categories: pre-processing phase and detection phase, with a detailed comparative analysis. The papers reviewed cover video-based surveillance systems. 2022 IEEE. -
NDC Pebbling Number for Some Class of Graphs
Let G be a connected graph. A pebbling move is defined as taking two pebbles from one vertex and the placing one pebble to an adjacent vertex and throwing away the another pebble. A dominating set D of a graph G = (V, E) is a non-split dominating set if the induced graph < V ? D > is connected. The Non-split Domination Cover(NDC) pebbling number, ?ns(G), of a graph G is the minimum of pebbles that must be placed on V(G) such that after a sequence of pebbling moves, the set of vertices with a pebble forms a non-split dominating set of G, regardless of the initial configuration of pebbles. We discuss some basic results and determine ?ns for some families of standard graphs. 2024 the Author(s), licensee Combinatorial Press. -
Exploring Quantum Computing in Weather Forecasting: Leveraging Optimization Algorithms for Long-Term Accuracy
Weather forecasting holds immense importance for businesses and society, necessitating accurate long-term predictions. Conventional computing faces challenges in achieving this precision due to the complexity of weather data processing. This study explores the potential of quantum computing and optimization techniques to enhance long-term weather forecasts. It delves into the foundational principles of quantum computing and the capabilities of optimization algorithms, highlighting their aptitude for addressing weather forecastings intricate optimization problems. Quantum computing, with its unique features of parallelism and advanced optimization, offers exciting opportunities for improving the precision of longrange weather forecasts. Examining the complexities of weather data analysis, the study emphasizes challenges arising from enormous datasets. It explores the potential of quantum optimization algorithms to extract insights and enhance meteorological data analysis. Additionally, it investigates quantum optimization methods for specific challenges in weather forecasting. This section compares quantum algorithms with traditional methodologies, evaluating their impact on forecast accuracy. The study concludes by showcasing the transformative role of quantum computing in advancing weather prediction capabilities, laying a robust foundation for future research and advancements in quantum computing technologies, and driving the evolution of more accurate long-term weather forecasts. 2025 Scrivener Publishing LLC. -
A Study on Experimental Analysis of Best Fit Machine Learning Approach for Smart Agriculture
By 2050, the population is projected to exceed nine billion, necessitating a 70% increase in agricultural output to meet the need. Land, water, and other resources are running out due to the growing world population, making it impossible to maintain the demandsupply cycle. The yield of cultivation is also declining as a result of people's ignorance of the growing crop illnesses. Given that food is the most basic human requirement, future research should focus on revitalizing the agricultural sector. Farming may be made more productive for farmers by applying the right artificial intelligence technologies and datasets. Agronomics can benefit greatly from artificial intelligence. So that we can farm more effectively and be as productive as possible, we need to adopt a better strategy. The objective of this paper is to experimentally analyze the machine learning algorithms and methods already in use and forecast the most effective approach to use in each agricultural sector. In this article, we will present the challenges farmers face when using traditional farming methods and how artificial intelligence is revolutionizing agriculture by replacing the traditional methods. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
A Mixed-Methods Study of Training in Evidence-Based Practice in Psychology Among Students, Faculty, and Practitioners in India and the United States
The current mixed-method study in India and the United States assessed understanding of what evidencebased practice in psychology (EBPP) is, how EBPP training and implementation occurs, and perceived barriers and needs related to EBPP training. Graduate students (India, n = 282; United States, n = 214), faculty (India, n = 24; United States, n = 67), and practitioners (India, n = 24; United States, n = 49) were surveyed, and focus groups with students (India, n = 31; United States, n = 12), faculty (India, n = 10, United States, n = 9), and practitioners (India, n = 28; United States, n = 17) were held. Individuals across countries and across the professional continuum were only somewhat aware of EBPP, largely equating it to just using empirically supported treatments. In both the United States and India, EBPP training was largely infused across the curriculum, though a sizable percentage of participants did report only limited exposure to EBPP training. Participants perceived themselves as engaging in EBPP. The biggest barriers to EBPP training (largely shared across countries) were hesitancy about EBPP, investing the time in training, and being wedded to a single school of thought. Indian participants also noted a limitation in primarily relying on data from Western countries. EBPP training needs identified included desire for greater flexibility within EBPP, receiving more theoretical foundation in EBPP, and more applied EBPP training. Results demonstrated advances in EBPP training in the past 15 years since the release of American Psychological Associations task force report but also provide areas for growth in training, specifically surrounding balancing research evidence with clients cultural context as well as ways to promote lifelong EBPP learning. 2024 American Psychological Association -
IOT based application for monitoring electricity power consumption in home appliances
Internet of Things is one of the emerging techniques that help in bridging the gap between the physical and cyber world. In the Internet of Things, the different smart objects connected, communicate with each other, data is gathered from the smart objects and based on the need of the users, and the data gathered are queried and sent back to the user. IoT helps in monitoring electrical and physical parameters. Electricity consumption from electronic devices is one among such parameters that need to be monitored. The development of energy efficient schemes for the IoT is a challenging issue as the IoT becomes more complex due to its large scale the current techniques of wireless sensor networks cannot be applied directly to the IoT. To achieve the green networked IoT, this paper proposes a Wi-Fi enabled simple low cost electricity monitoring device that can monitor the electricity consumption on home appliances which helps to analyses the consumption of electricity on a daily and weekly basis. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
A Survey on Arrhythmia Disease Detection Using Deep Learning Methods
The Cardiovascular conditions are now one of the foremost common impacts on human health. Report from WHO, says that in India 45% of deaths are caused due to heart diseases. So, heart disease detection has more importance. Manual auscultation was used to diagnose cardiovascular problems just a few years ago. Nowadays computer-assisted technologies are used to identify diseases. Accurate detection of the disease can make recovery simpler, more effective, and less expensive. In this proposed work, 11years of research works on arrhythmia detection using deep learning are integrated. Moreover, here presents a comprehensive evaluation of recent deep learning-based approaches for detecting heart disease. There are a number of review papers accessible that focus on traditional methods for detecting cardiac disease. This article addresses some essential approaches for categorizing ECG signal images into desired classes, such as pre-processing, feature extraction, feature selection, and classification. However, the reviewed literatures consolidated details have been summarized. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Performance evaluation of diesel engine using genetic algorithm
?Abstract: Engine analysis and optimization is not a new approach to the field of automobiles. It has always been a keen focus in the research of experts domestically as well as internationally, the control of Air-Fuel Ratio (AFR) in transient operating conditions of engine. For the last few decades, the industry and economic expansion of developed countries has showed a clean increase in the vehicle production as well as transport volume. Global warming, acid rain, greenhouse effect and air pollution problems related to emission of CO2, NOx, PM, CO and unburned HC, together with the consumption of fossil fuels, unite to create serious problems at a global level. Therefore it is a research study considering all these current issues and taking it to a new level of optimization for the output of a better efficiency, better economy and less pollution. Performance of Diesel Engine is evaluated by parameters like Power, Torque and Specific Fuel Consumption. 2018, Blue Eyes Intelligence Engineering and Sciences Publication. All rights reserved. -
Nurses' perception about Human Resource Management system and prosocial organisational behaviour: Mediating role of job efficacy
Aims: To examine the relationship between nurses' perception about human resource management system and prosocial organisational behaviour through job efficacy. Background: Literature suggests that non-profit organisations are often confronted with financial constraints on one side and the expectation of delivering high-quality services on the other. Employees voluntarily engaging in service-oriented behaviours help to bridge this gap to some extent, and human resource management system plays a significant role in eliciting the requisite behaviours. In this article, the case of nurses from non-profit hospitals has been undertaken to examine the aspects of human resource management system that needs focus while promoting prosocial organisational behaviours among the nurses for ensuring better service delivery. Method: Cross-sectional design was employed. Data were collected from 387 nurses working in non-profit hospitals in India through questionnaires and were analysed with the help of structural equation modelling. Findings: In the absence of sophisticated human resource system in non-profit hospitals, the study found that nurses' perception about human resource management system is positively related to prosocial organisational behaviours, and job efficacy partially mediates the relationship. Conclusion: Positive perceptions such as involvement with the job and communication as well as supervisors' support are essential human resource practices for fostering self-efficacy and, thus, improving prosocial organisational behaviour of nurses working in non-profit hospitals. Implication for Nursing Management: Non-profit hospitals should focus on nurses' participation and supervisory support, which would provide a better human touch approach to patient care and also improve service quality. The findings shed light on the nursing management of non-profit hospitals in terms of human resource management that has to be given much attention for institutionalizing prosocial organisational behaviour. 2021 John Wiley & Sons Ltd -
High-Speed Parity Number Detection Algorithm inRNS Based onAkushsky Core Function
The Residue Number System is widely used in cryptography, digital signal processing, image processing systems and other areas where high-performance computation is required. One of the computationally expensive operations in the Residue Number System is the parity detection of a number. This paper presents a high-speed algorithm for parity detection of numbers in Residue Number System based on Akushsky core function. The proposed approach for parity detection reduces the average time by 20.39% compared to the algorithm based on the Chinese Remainder Theorem. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A Bibliometric Analysis of Asset Allocation for Retirement
Allocation of investment assets is key in attaining a sustainable retirement portfolio. In this research article, the authors analyzed the most recent research publications in the area related to asset allocation for retirement and identified those which have the highest impact. The authors research was conducted using the bibliometric analysis technique of research articles collected from the Scopus database. Most of the research articles were published in reputed journals in the United States, United Kingdom, Australia, and Germany. It was also observed that most of the highly cited research articles in the research area of asset allocation for retirement are focused on financial literacy, increase in retirement age, aging, and pension reforms. The authors findings identified six research themes in asset allocation for retirement such as 1) asset allocation for retirement planning, 2) methods to increase efficiency, 3) investment preferences for retirement savings 4) financial literacy and retirement planning, 5) reforms on retirement savings, and 6) annuities for retirement income. Furthermore, nineteen future research directions are also provided. In conclusion, the authors aim to assist future researchers in identifying highly cited articles, key authors, contributing countries and research themes in asset allocation for retirement. Overall, the analysis provides comprehensive information in addressing research questions in the field of asset allocation for retirement. Copyright 2024 With Intelligence LLC. -
Investment Intentions and Influential Factors among University Students
This study investigates the investment intentions of university students in Delhi NCR and the factors influencing their decision-making, guided by the Theory of Planned Behavior (TPB). Specifically, the research examines how financial attitude, risk tolerance, and academic background contribute to students' intent to invest, alongside demographic factors such as gender, family income, and family structure. A structured questionnaire was administered to 454 university students, and data were analyzed using one-way ANOVA, chi-square tests, and multiple linear regression. Findings indicate that financial attitude and risk appetite significantly influence investment intention, with financial attitude showing the strongest negative effect. While the course of study did not significantly predict general investment intention, it showed a meaningful association with preference for equity investments. Gender differences were statistically significant, with male students more likely to invest both generally and in equities. In contrast, no significant differences were found for family income or family structure. The regression model explained 40.7% of the variance in investment intention, reinforcing TPBs attitudinal and control constructs. The study highlights the importance of integrating behavioral finance elements into education and encourages a shift beyond theoretical literacy toward experiential learning. Although variables such as social influence, financial self-efficacy, and digital platform awareness were not included in this study, their relevance is acknowledged for future research. These insights have practical implications for financial education policies under the NEP 2020 and for designing student-targeted financial awareness programs. 2025, Iquz Galaxy Publisher. All rights reserved.