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Traditional finance vs. web 3: A comparative analysis of key features and characteristics for better readability purposes
Web3 is a ground-breaking invention that has the ability to address the shortcomings of web1 and web2. The industry witnessing its major impact is the finance sector. A wave of innovation in traditional finance has been inspired by the introduction of Web3. It is also referred to as the decentralised web and is a developing movement that is upending conventional finance by providing a more open, safe, and decentralised substitute. Traditional banking should work to adopt the features that Web3 offers, including stability, scalability, interoperability, security, performance, extensibility, management, and openness. In order for TradFi to maintain its relevance and expertise in the face of the widespread adoption of digital financial modes, it is now necessary to embrace several Web3 capabilities. Keeping into consideration the relevance and importance of Web3 in finance, this chapter will basically focus on analysing the key features and characteristics of Web3 in comparison to traditional finance. 2023 by IGI Global. All rights reserved. -
Traditional Food Systems in the Indian Himalayas Perspectives from Climate Science
The Indian Himalayas have various crops and livestock that have sustained local communities for generations. Nonetheless, multiple factors, such as climate change, threaten these conventional agricultural systems. This chapter examines the climate science perspectives on traditional farming systems in the Indian Himalayas. It overviews the regions agricultural systems, including cultivated cereals and domesticated animals. The work also examines the effects of climate change on traditional farming systems, including temperature fluctuations. Extreme weather events are becoming more frequent, and precipitation patterns are changing. This passage highlights the significance of traditional knowledge in coping with climate change. It emphasizes the importance of utilizing integrated approaches that merge traditional knowledge with climate science to ensure the sustainability and resilience of conventional agricultural systems in the Indian Himalayas. We require a pragmatic strategy to address the issue of climate change and safeguard the regions agriculture. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Traditional Wisdom in Water Harvesting: A Comparative Review of Ahar Pynes and Tank Systems in India
The Indian subcontinent has historically relied on rainwater harvesting for agricultural and domestic water purposes. Traditional water management practices were crucial in the development of settlements and growth of villages and towns in Ancient India. Numerous indigenous water management practices have evolved across the diverse geography of this region to capture rain and manage surface water runoff. These systems have sustained the agrarian economy over centuries by contributing to irrigation and water security. This research paper focuses on two diverse water management systems in two geographically distinct areas in India. The Ahar Pyne system is practiced in the flood prone alluvial plains of Bihar, while the Tank system of irrigation is prevalent in the arid Deccan region. Both these systems were managed by the local communities living in their vicinity. These systems promote flood mitigation and drought resilience. These systems have become increasingly neglected in the recent years with development and advent of piped water supply. As the world grapples with problems escalated by climate change and the ensuing issue of water scarcity, there is an increasing interest in traditional systems and how they can be adapted to current needs. The comparative study of these two systems accentuates the adaptability of indigenous water management practices in different climatic and topographic conditions. The study also underlines the significance of integrating these traditional systems into the current water management processes. The paper also highlights the relevance of these systems in the current scenario and the need for revival and sustainable management of these systems towards building a resilient future. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Traffic management and congestion control in vehicular adhoc networks
Urban traffic congestion is a growing concern worldwide. Vehicular Adhoc Networks (VANETs) offer a glimpse into a future with smoother traffic flow and reduced congestion. These networks enable real- time communication between vehicles and infrastructure, creating a dynamic traffic management system. Imagine traffic signals that adjust based on real- time data, congestion being predicted and alleviated before it builds, and emergency services receiving faster response times. This is the potential of VANETs. Ensuring reliable communication and data integrity among constantly moving vehicles is crucial. Researchers are developing protocols and algorithms to address this, focusing on efficient routing, data dissemination, and network stability. The integration of emerging technologies like 5G, edge computing, and artificial intelligence holds promise for further enhancing network performance and robustness. While significant progress has been made, widespread adoption of VANETs faces hurdles. Scalability, security, privacy, and infrastructure development costs are significant concerns. 2025, IGI Global Scientific Publishing. All rights reserved. -
Traffic Management in Forest and Ecosystem Conservation. A Study on NH 766 Through Bandipore National Park and Proposing a Traffic Management Plan with Alternate Route Consideration
Transportation network is inevitable in the developing world. In India where we have a rich forest cover, many of the roads are passing through eco-sensitive areas such as national parks and wildlife sanctuaries. There are issues being reported due to these roads passing through the eco-sensitive areas such as animal deaths due to road accidents, loss of habitats, fragmentation of ecosystems, and loss of forest cover. The CalicutKollegal national highway, NH766 is passing through Bandipore national park on the stretch which connects Sultan Bathery and Gundelpette. Recently, a conflict had risen between environmental activists and the public for imposing a complete traffic ban along the NH766 passing through the Bandipore NP. A baseline study had conducted on the NH766, and the impact of the same on the ecosystem existing is analyzed through the data collected. A network analysis is performed on the alternate route available for bypassing the traffic. Traffic management plan and policies are derived out of the analysis on the baseline data collected and the inferences drawn from the network analysis performed on the alternate routes. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Traffic Optimization and Route Detection Based on Air Quality and Pollution Level
This research outlines the development of a groundbreaking Traffic Optimization and Route Detection system based on pollution and air quality. Urbanization has led to increased vehicular traffic, exacerbating concerns about air pollution and its adverse effects on public health. The proposed system aims to address this critical issue by integrating real-time environmental data into route recommendations, prioritizing routes that minimize exposure to high-pollution areas. Beyond improving air quality, the system promotes the health and well-being of commuters, encourages the adoption of eco-friendly transportation modes, and contributes to overall environmental sustainability. An air quality detection system is developed to gather data for the development of the system. This innovative approach aligns with the goals of efficient urban mobility, sustainable transportation, and data-driven decision-making. Through this research, we anticipate providing valuable insights into the potential impact of integrating pollution and air quality considerations into urban transportation systems, ultimately contributing to healthier and more sustainable urban environments. 2024 IEEE. -
Training in Cultural Competence for Mental Health Care: A Mixed-Methods Study of Students, Faculty, and Practitioners from India and USA
Although the need to train clinicians to provide effective mental health care to individuals from diverse backgrounds has been recognized worldwide, a bulk of what we know about training in cultural competence (CC) is based on research conducted in the United States. Research on CC in mental health training from different world populations is needed due to the context-dependent nature of CC. Focusing on India and USA, two diverse countries that provide complementary contexts to examine CC, we explored graduate students, practicing clinicians, and faculty members perspectives regarding CCtraining they received/provided and future training needs using mixed-methods. The data were collected using focus groups (n = 25 groups total: 15 in India, 11 in USA), and a survey (n = 800: 450 in India, 350 in USA). Our data highlight the salient social identities in these countries, and the corresponding constituents of CC training. Participants in India described a practical emphasis to their CC training (e.g., learning about CC through life experiences and clinical practice experiences) more so than through coursework, whereas participants in USA described varying levels of courseworkrelated toCC along with practice. Participants in both countries considered enormity of CC as a challenge, while those in the US also identified CC training limited to a white, straight, male perspective, hesitancy in engaging with diversity topics, and limited time and competence of the faculty. Strengths of CC training in India and USA are mutually informative in generating recommendations for enhancing the training in both countries. The Author(s) 2024. -
Training multi-layer perceptron with enhanced brain storm optimization metaheuristics
In the domain of artificial neural networks, the learning process represents one of the most challenging tasks. Since the classification accuracy highly depends on the weights and biases, it is crucial to find its optimal or suboptimal values for the problem at hand. However, to a very large search space, it is very difficult to find the proper values of connection weights and biases. Employing traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local optima. Most commonly, back-propagation is used for multi-layer-perceptron training and it can lead to vanishing gradient issue. As an alternative approach, stochastic optimization algorithms, such as nature-inspired metaheuristics are more reliable for complex optimization tax, such as finding the proper values of weights and biases for neural network training. In this work, we propose an enhanced brain storm optimization-based algorithm for training neural networks. In the simulations, ten binary classification benchmark datasets with different difficulty levels are used to evaluate the efficiency of the proposed enhanced brain storm optimization algorithm. The results show that the proposed approach is very promising in this domain and it achieved better results than other state-of-the-art approaches on the majority of datasets in terms of classification accuracy and convergence speed, due to the capability of balancing the intensification and diversification and avoiding the local minima. The proposed approach obtained the best accuracy on eight out of ten observed dataset, outperforming all other algorithms by 1-2% on average. When mean accuracy is observed, the proposed algorithm dominated on nine out of ten datasets. 2022 Tech Science Press. All rights reserved. -
Trait-Driven Persuasion: Investigating the Role of Personality in Shaping Advertising Effectiveness
This study examines how the Big Five personality traits influence consumer responses to different advertising appeals persuasive, rational, emotional, humour, and fear. A descriptive research design with snowball sampling was used to collect data from individuals (n = 120). Standardized self-report questionnaires were administered, including a demographic information form, a Big Five personality measure (assessing extraversion, agreeableness, conscientiousness, neuroticism, and openness), and scales evaluating perceived effectiveness of five advertising appeals; overall internal consistency was acceptable for exploratory research (Cronbachs ? = .65). Correlation analysis and structural equation modelling (SEM) were conducted. Results reveal distinct associations: agreeableness aligns with humour appeal, neuroticism with emotional and fear appeals, conscientiousness with rational and persuasive appeals, extraversion with fear appeal, and openness with humour appeal. These findings contribute to personality-driven marketing research by providing empirical evidence on how individual differences shape advertising effectiveness. The study highlights implications for advertisers seeking to design targeted and psychologically congruent campaigns based on personality segmentation. Advertisers can apply personality-driven segmentation to design psychologically congruent campaigns. 2026, Institute of Applied Psychology, University of the Punjab Quaid-eAzam Campus. All rights reserved. -
Trajectories forSpace Missions: Bridging Tradition andInnovation
Spacecraft trajectory optimization has always been a determining factor in successful space missions as it should be precise and efficient in automatically exploiting new opportunities present in the complex and dynamic environment. Traditional optimization algorithms cannot meet the increasing demand for fast computation, adaptation ability, or overcoming real-time constraints. A recently developed technique called reinforcement learning is quite promising in dealing with such issues by proposing innovative solutions for trajectory optimization. This paper surveys cutting-edge reinforcement learning solutions for optimizing spacecraft trajectory problems. Comprehensive and pragmatic analysis based on different aspects of currently available solutions, and concise reports are generated to get the latest update on this field, as well as provide reference on designing future-related solutions. The survey suggests that more efforts from the research field should be spent on reinforcement learning solutions especially when applied in the real mission scenario because there are still many challenges unattended by the community that were pointed out before being delivered at the end-user level. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Transfer Learning based Analysis of Chest X-rays for Accurate Lung Disease Detection and Interpretation
This is a research paper based on a transfer learning approach with a primary aim at the analysis of chest Xrays for accurate detection and interpretation of lung diseases. The proposed method relies heavily on the use of pretrained deep learning models to enhance diagnostic accuracy and reduce the time and computational resources taken during training. Applying transfer learning to a large chest X-ray dataset, the model successfully detects key patterns associated with common lung diseases, such as pneumonia and tuberculosis. The manuscript encompasses data preprocessing, model finetuning, and performance evaluation and demonstrates huge improvements over the traditional methods both in terms of accuracy and interpretability. It has been experimentally proven that the model is competent enough to provide localization of disease areas, as it can be visualized through heatmaps obtained from predictions, which might further help the radiologists perform their diagnosis tasks. This work advocates for medical imaging automation for the early and efficient detection of lung disease. 2025 IEEE. -
Transfer learning in multimodal settings
A powerful machine learning technique in the multimodal environment allows the transmission learning model to adapt to information from one domain to another, which promotes more effective learning in different types of data, including lessons, images, speeches, speeches, and sensor data. This method increases the model's adaptability, reduces the requirement for large marked datasets, and increases performance across domains. It has been used in several domains where multimodal integration is important, such as healthcare, autonomous systems, and natural language processing. Despite the benefits, transmission learning has disadvantages, including high data costs, data shortages, and domain changes. To meet these challenges, model architecture, adaptation strategy, and improvement in dataset growth techniques are necessary. This study examines basic ideas, procedures, and transfer of transfer to multimodal references, and provides insight. Practical use and new development. We show the developing role to learn transfer in improving artificial intelligence (AI) applications by looking at current studies and case studies. As the area develops, a combination of knowledge from many methods will be necessary to create scalable, reliable, and effective AI systems that can handle the problems in the real world. 2026 Elsevier Inc. All rights reserved. -
TRANSFER LEARNING TECHNIQUES AND APPROACHES FOR PREDICTIVE MODELING OF DISEASE OUTCOMES
Aim/Purpose In this research work, we have developed a predictive model that focuses on utilizing knowledge from the related domains. Background A serious public health issue, especially in tropical and subtropical regions, is dengue fever, a viral infection passed by mosquitoes. Accurate early prediction of disease outcomes is essential for both efficient patient management and ef-fective use of resources. More complex methods are required since conven-tional prediction models could be faulty with limited labeled data and complex feature interactions. Methodology We propose a new strategy integrating deep attention mechanisms with trans-fer learning to enhance prediction modeling of dengue disease outcomes. First pre-trained on a large, linked dataset of common viral illnesses, a deep neural network enables the model to learn generic properties. We then iteratively im-prove our pre-trained model using a specific dengue dataset. Incorporating a deep attention mechanism allows for the focus on the most relevant features, improving interpretability and accuracy. Contribution Among logistic regression, random forests, and basic deep learning methods, current models reveal poor accuracy and dependability in forecasting dengue disease outcomes. These models sometimes fail to sufficiently depict the com-plicated interactions among clinical variables, especially under conditions with limited data. Findings The proposed method outperforms more traditional models pretty strongly. Our model acquired in the training phase an accuracy of 0.92, precision of 0.91, recall of 0.90, and F1-score of 0.90. It maintained high performance on testing with an accuracy of 0.91, precision of 0.90, recall of 0.89, and an F1-score of 0.89. Similar patterns were indicated by an accuracy of 0.90, precision of 0.89, recall of 0.88, and an F1-score of 0.88 validation results. The model also demonstrated a lowered loss (0.21, 0.23, 0.24 in training, testing, and vali-dation, respectively), higher true positive rates (0.90, 0.89, 0.88), and lower false positive rates (0.10, 0.11, 0.12). Deep attention methods and transfer learning offer a robust and effective strategy for predictive modeling of dengue disease outcomes, therefore considerably boosting accuracy and dependability. This approach offers considerable possibilities for dengue-endemic patient manage-ment and resource allocation. Recommendations for Researchers Investigations should prioritize the validation of the algorithm in various healthcare environments to assess its efficacy in clinical application. Future Research In future research, this work can be enhanced using several deep learning algo-rithms to achieve better accuracy and performance. This article is licensed to you under a Creative Commons Attribution-NonCommercial 4.0 International License. When you copy and redistribute this paper in full or in part, you need to provide proper attribution to it to ensure that others can later locate this work (and to ensure that others do not accuse you of plagiarism). You may (and we encourage you to) adapt, remix, transform, and build upon the material for any non-commercial purposes. This license does not permit you to use this material for commercial purposes. -
Transfer Learning-Based Osteoporosis Classification Using Simple Radiographs
Osteoporosis is a condition that affects the entire skeletal system, resulting in a decreased density of bone mass and the weakening of bone tissues micro-architecture. This leads to weaker bones that are more susceptible to fractures. Detecting and measuring bone mineral density has always been a critical area of focus for researchers in the diagnosis of bone diseases such as osteoporosis. However, existing algorithms used for osteoporosis diagnosis encounter challenges in obtaining accurate results due to X-ray image noise and variations in bone shapes, especially in low-contrast conditions. Therefore, the development of efficient algorithms that can mitigate these challenges and improve the accuracy of osteoporosis diagnosis is essential. In this research paper, a comparative analysis was conducted Assessing the accuracy and efficiency of the latest deep learning CNN model, such as VGG16, VGG19, DenseNet121, Resnet50, and InceptionV3 in detecting to Classify Normal and Osteoporosis cases. The study employed 830 X-ray images of the Spine, Hand, Leg, Knee, and Hip, comprising Normal (420) and Osteoporosis (410) cases. Various performance metrics were utilized to evaluate each model. The findings indicate that DenseNet121 exhibited superior performance with an accuracy rate of 93.4% Achieving an error rate of 0.07 and a validation loss of only 0.57 compared with other models considered in this study. 2023, International journal of online and biomedical engineering. All Rights Reserved. -
Transference of Love-type Wave Through Cobalt Ferrite Cofe2O4 Layer Structure, Governed by an Imperfect Interface
An analytical discussion of the wave transmission in a piezomagnetic (Cobalt ferrite) thin plate resting on an elastic substrate is presented in the problem. It is presumed that the geometrys interface is not ideal. The flaw of the considered structure is used to describe by following the linear spring model. The calculative method of the upper material is Direct Sturm-Liouville. Dispersion relations are drived for each of the magnetically open and magnetically short cases. Love-type wave velocity profiles have been depicted on graphs for various influencing factors, such as heterogeneity in the substrate, layer thickness, and interface imperfections. It has been demonstrated that raising these parameters raises the Love waves phase velocity. Furthermore, it is found that compared to substrate heterogeneity, layer thickness has less of an impact on the waves velocity profile. Additionally, it has been shown how the aforementioned cases compare when imperfect parameters are varied. It is discovered that the velocity in the open case is greater than that in the short case. The results have potential applications in the design of piezomagnetic semiconductor devices controlled by electric fields and are of great significance for developing surface acoustic wave (SAW) gyroscopes. 2024 American Institute of Physics Inc.. All rights reserved. -
Transformation of hydrocarbon soot to graphenic carbon nanostructures
Graphenic carbon nanostructures were synthesized from different precursors of petroleum and agricultural origin by oxidative scissoring. In the present study soot, an environmental pollutant is converted to a value-added product by facile synthesis techniques. The physicochemical changes of the nanostructures are investigated by means of XRD, AFM, FTIR, Raman spectroscopy, XPS analyses SEM-EDS and TEM analysis. XRD analysis confirms the formation of few layer oxidized carbon nanostructures with smaller lateral dimensions. Raman spectra reveal the existence of graphenic layer with a fewer defect. AFM and SEM analyses reveal the formation of stacked tiny fragments of graphenic carbon lamellae. XPS and IR analyses confirm the incorporation of oxygen functionalities into the carbon backbone. 2018 by the authors. -
Transformation of India as investor of outward fdi: A systematic investigation of literature
Besides the economic transformation and industrial up-gradation, Indian enterprises have steadily intensified their overseas investment venture during recent years. A systematic literature review performed to inspect the strategic motives and Outward FDI (OFDI) impact on emerging economies like India. This paper explores relevant theories, strategic rationale, and economic policies that propel the present OFDI trend from India. The effort taken by the Indian government to promote innovations were Cross border commercial and industrial collaboration. These efforts flagged the way for more Outward FDI possibilities in the future (Welch, 1988). This study comprises the literature works till the year 2019, which includes research journals and reports. The analysis observes that knowledge-based industries drive India's Outward FDI and examine whether knowledge-based industries contribute to sustaining long-term domestic and international growth (Pradhan J.P., 2005; Narayanan, 2016). Indian Institute of Finance. -
Transformation of service sectors through Augmented Reality (AR) and Virtual Reality (VR)
Human interactions with actual and virtual environments are changing as a result of recent advancements in technology like Augmented Reality (AR) and Virtual Reality (VR). The sectors like retail, eCommerce, transport, tourism, real estate, healthcare, education, interior design, customer relationship management, etc. uses this innovative and adaptive technology. The aim of this chapter is to highlight the transformation of different service sectors through the use of Augmented Reality (AR) and Virtual Reality (VR) and also to see the recent innovations in this field. It was found that, AR and VR have an impact on various service sectors like retail and eCommerce in the form of virtual testing of products, tourism sector through 360-degree virtual tour, real estate sector through tour of the surrounding without being physically present, healthcare through physicians treating the patient virtually, education in the form of virtual classrooms, etc. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Transformational educational leaders inspire school educators commitment
Introduction: Transformational school leaders play an important role in promoting educational innovation and restructuring by creating a vision for the future, building a culture of collaboration, and empowering others to become leaders themselves. Through their leadership style, they inspire and motivate others to work towards a common goal, leading to positive change and growth within the educational system. The aim of this study is to measure the impact of transformational leadership on various types of commitment that school teachers have in Bengaluru, India. Methods: A survey was conducted using standardised instruments to measure the leadership style of principals and personal commitment of teachers. The data was collected from 1,173 school teachers through a questionnaire and analysed using SPSS V23 statistical software. Results: The study found that transformational leadership had a significant impact on the different types of commitment that teachers possess in school education. The three domains of commitment - commitment towards the institution, student development, and self-development - were positively influenced by transformational leadership. Discussion: Transformational school leaders play an important role in promoting educational innovation and restructuring by creating a vision for the future, building a culture of collaboration, and empowering others to become leaders themselves. This study provides evidence that transformational leadership has a positive impact on different types of commitment among school teachers in Bengaluru, India. Leaders of school management are advised to take into account the three domains of commitment of their teachers to facilitate organisational learning through more integrative methods. Copyright 2023 Kareem, Patrick, Prabakaran, B, Tantia, M. P. M. and Mukherjee. -
Transformational Impact of COVID-19 on Savings and Spending Patterns of Indian Rural Households
COVID-19 has spread across the globe at a shocking level and significantly affects the world economy. The pandemic has significantly impacted rural households, the primary workforce for industrialized urban areas, in every sector of rural businesses, including agriculture. Furthermore, the dearth of employment in the primary industry has also adversely influenced rural inhabitants livelihood and financial decisions. COVID-19 changed the perception of people regarding their income and expenditure. This study is intended to analyse the transformation of savings and spending of rural households during COVID-19. A questionnaire was developed using a Likert scale to elicit study variables, and the collected data were analysed using structural equation modelling. The results showed that all types of savings had a positive and significant relationship with the savings motive of rural households during COVID-19. Further, customary and spontaneous spending had a positive and significant relationship spending pattern of rural households. Rural inhabitants were interested in compromising their spending and other forms of savings to have more emergency savings. Earlier studies have examined either the savings or the spending pattern of rural households, and studies on both savings and spending by rural households are very few. The present study thus adds to the existing literature in the field. The Author(s) 2022.