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Trade in Pollution-intensive Products: Evidence from India
This article explains Indias trade from an environmental perspective. Besides explaining trends and patterns of trade in pollution-intensive products, we investigate Indias comparative advantage in these products and discuss the emerging issues. The exercise based on the UN Comtrade dataset reveals that much of Indias exports happen under this category with better revealed comparative advantage (RCA) values, which do have high environmental concerns. We sum up the article by arguing that there is a need to attend to sector-specific problems encountered by these industries and have a well-knit environmental policy, so that trade and industrial expansion do not have a major environmental concern. 2022 Management Development Institute. -
Trade in renewable energy technologies: A comparison of India and China
[No abstract available] -
Trade Integration and Export Aspiration: Evidence from India's Trade in Goods with BRICS Countries
The purpose of this study was to examine the dynamics of trade between India and the BRICS countries as well as to gauge the relative strength of Indian exports to those nations. The trade integration patterns among BRICS countries were also analyzed. To quantify the extent to which India's exports correspond to the needs of its BRICS counterparts, a novel export aspiration index was constructed. The index of trade integration patterns has also been employed to quantify India's trade integration pattern with other BRICS members. Further, the gravity model of trade has been employed to analyze the fundamentals of India-BRICS trade. The export aspiration in individual BRICS countries shows a diverse pattern. However, India's export aspiration in these countries has improved, although marginally in the long run. Such empirical evidence substantiates that the relative strength of India's exports within its BRICS counterparts has marginally improved over time. Moreover, the trade integration index indicates a similar trade integration pattern among the BRICS countries and corroborates the presence of inter-industry trade. Added to the conventional variables of the gravity model, India's outward multilateral trade resistance and BRICS inward multilateral trade resistance significantly promote India-BRICS trade. Hence, the relative strength of Indian exports will increase substantially if India's commodity composition is diversified by including more commodities in its export baskets that correspond to the needs and changing conditions of the BRICS economies. Copyright 2022 Mudaser Ahad Bhat, Aamir Jamal, Mirza Nazrana Beg. -
Trade vis-vis Human Rights of Farmers Under TRIPS
The Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS) plays a crucial role in shaping the global intellectual property system that significantly impacts farmers' rights. In agriculture, TRIPS grants intellectual property protection in the form of patents and plant variety protection. Such protection conflicts with the farmer's rights to save, sow and exchange the seeds. This raises human rights concerns related to farmers such as access to genetic resources and their cultural rights, food security, livelihood that are enshrined under various international human rights instruments such as International Covenant on Economic, Social and Cultural Rights (ICESCR) and Universal Declaration on Human Rights (UDHR). This intersection between TRIPS and the human rights of farmers requires a more inclusive approach that balances human rights with innovation. In light of the above perspective, this chapter analyzes farmers' human rights integration under the domain of intellectual property rights protected under TRIPS. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Trademark Confusion in the Era of Big Data Algorithmic Branding: Consumer Decpetion and Conpetition Law Challenge
This chapter, per the authors, examines how Big Data and AI have transformed trademark deception from sign-based imitation to algorithmically driven perception distortion. It explains how digital platforms collect and analyze massive behavioral datasets to rank, recommend, and position brands in ways that influence consumer belief about origin without copying any mark. Algorithmic practices such as competitor keyword bidding, recommendation bias, and ranking manipulation generate large-scale confusion by shaping what consumers see first and trust most. While global jurisprudence, including the LOrl v. eBay decision, recognizes platform-facilitated deception, Indian law still interprets confusion through traditional frameworks distinguishing infringement of the mark from deception of consumer belief. This chapter, per the authors, argues that AI-mediated market architecture produces deception without infringement, creating evidentiary gaps and competitive distortions that require algorithmic transparency, marketplace accountability, and an updated trademarkcompetition interface Copyright 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development. -
Traditional and Online Bullying Victimization Among School-going Adolescents: The Role of Sociodemographic Factors
Background: Bullying in school is a vital issue as researchers increasingly find that it can be detrimental to students well-being and academic excellence. From an ecological systems theory perspective, examining school bullying reveals that numerous psychosocial factors influence student behavior. Hence, the present study aims to investigate the role of sociodemographicsat the individual (age, gender), microsystem and mesosystem (grade level, school type), exosystem (region), macrosystem (socioeconomic status), and chronosystem (shift from traditional to virtual classroom during COVID-19) levelsin the victimization of traditional and online bullying. Methods: After obtaining ethical clearance, an online survey form was deployed to collect sociodemographic information and assess traditional and online bullying victimization among 120 school-going adolescents from the Southern regions of India. Results: A linear regression analysis showed age and region (urban and rural) predicting traditional and online victimization, respectively. Other sociodemographic factors of gender, school type, and grade level did not predict either victimization type. Conclusions: Sociodemographic factors play a significant role in the victimization experiences of students both in traditional and online. 2024 The Author(s). -
Traditional beliefs and practices associated with relieving psychological problems of pregnant women of the Zeliang tribe
In Indigenous and resource-limited communities, emotional distress during pregnancy is often understood and managed through culturally grounded belief systems rather than biomedical frameworks. This qualitative study explores how pregnant women of the Zeliang tribe in Benreu village, Nagaland, perceive, interpret, and cope with emotional distress using traditional beliefs and practices. Guided by community psychology, cultural safety frameworks, and Lazarus and Folkmans Transactional Model of Stress and Coping, semi-structured interviews were conducted with ten pregnant women and two traditional healers. Data were analyzed using reflexive thematic analysis. Three interconnected themes were generated. First, emotional vulnerability and cultural conceptions of pregnancy revealed that fear, sadness, and emotional instability were interpreted through spiritual and ancestral meanings rather than psychiatric categories. Second, healing practices as emotional regulation tools illustrated how ritual chanting, fumigation, protective threads, and herbal remedies functioned as embodied coping mechanisms supported by intergenerational kin networks. Third, traditional healers roles in psychosocial support highlighted their function as trusted interpreters of distress who provide narrative explanation, reassurance, and culturally congruent guidance. Participants also described a complementary care pathway in which biomedical services were used for physical monitoring while emotional and spiritual concerns were addressed through traditional systems. The findings indicate that traditional healing within the Zeliang community operates as a culturally embedded model of perinatal emotional care integrating spiritual, relational, and symbolic dimensions of well-being. The study underscores the importance of culturally safe maternal mental health approaches that respect Indigenous explanatory systems and encourage collaboration between biomedical providers and community-based healing structures. The Author(s) 2026. -
Traditional Ecological Knowledge Repository in the Indian Himalayas: An Overview
Traditional ecological knowledge (TEK) refers to a body of informa-tion that is also referred to as local knowledge, traditional knowledge, native knowledge, and indigenous technological knowledge. A number of studies show the role of traditional ecological knowledge in decision-making in social-ecological systems that support sustainability and resilience. International agencies have also highlighted and emphasised the importance of TEK practises in the preservation of biological variation. For instance, the UN Convention on Biodiversity, Article 8 (j), makes it very plain that respect, maintain, and promote innovation and practises of indigenous and aboriginal populations connected with sustainable use of biolog-ical diversity are essential. The benefits of TEK for sustainable forest management were acknowledged in the 2005 Millennium Ecosystem Assessment Report by the World Bank. As environmentalists, anthropologists, and arborists share interests in TEK for academic, social, or economic reasons, this highlights the significance of TEK in difficulties relating to biodiversity protection. Numerous components of TEK are seen favourably by experts in fields of forestry, irrigation, architecture, ethno-biology, irrigation, agriculture, medicine, sun and water conservation, conventional weather prediction, adaptation to climate change, and disaster risk reduction. Indian Himalayan Region (IHR) is predominantly populated by indigenous peoples and local societies, which are quite diverse in terms of socio-culture and race. The region has nearly 40% of all of Indias indigenous tribes. This area is also special for its tradi-tional ecological knowledge. Many of the TEK-based practices have supported local communities in earning a livelihood. The indigenous peoples expertise and expe-riences are said to play a crucial part in preventing climate change, and they may give important information on the implications of climate change. Hence, sustaining biodiversity in the IHR is also a means of defending indigenous peoples rights. By making the TEK the focal point of governance systems at the IHR, the variety of options for sustainable growth and even the co-production of the body of knowl-edge would be expanded. Therefore, it seems sensible to get knowledge from the TEK before it is lost to the onslaught of modernity. However, there are numerous problems or issues with traditional ecological knowledge in India, including igno-rance in considering conservation policies by the Indian government and the lack of effective documentation of this priceless knowledge. To develop sustainable and culturally suitable management techniques, it is currently a challenge to combine indigenous knowledge standards and management methods with Western science. Realising the above, this chapter attempts to comprehend the concept of TEK and its application throughout a variety of resource management contexts throughout a variety of resource management scenarios. Further, it will explore various issues and challenges and examine the regulations thereof. Lastly, this chapter concludes by highlighting the strategies and suggestions for an effective repository of traditional ecological knowledge in the Indian Himalayan Region. 2024 The Author(s). -
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.