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Surviving Under Stress: Exploring Zea mays Adaptive Responses to Cadmium Toxicity and Mitigation StrategiesA Review
Cadmium (Cd) toxicity poses a significant threat to Zea mays, disrupting its normal physiological functions and metabolic processes. This chapter summarises current studies on the sources of contamination, Cd intake mechanisms, and the effect of Cd toxicity on critical physiological systems in maize. It then thoroughly investigates Zea mays physiological and adaptive responses to Cd toxicity. The section outlines how Cd inhibits vital metabolic processes, such as photosynthesis and the absorption of nutrients uptake in maize plants, leading to a reduction in biomass, yield, and growth. The adverse impacts on plant growth and development are amplified by anatomical changes brought on by Cd exposure, such as modifications to the roots and leaves. Furthermore, a thorough examination of biochemical modifications is conducted, such as adjustments to protein composition, glucose metabolism, and amino acid levels. The chapter additionally examines how enzymatic activity responds to Cd stress, focusing on modifications in the activity of enzymes involved in antioxidants and metabolism. Under the influence of Cd toxicity, maize plants display a range of intricate adaptive responses. These include upregulation of genes linked to the production of ethylene and the synthesis of peptides that bind metals, such as phytochelatins. The study covers the effectiveness of several mitigation techniques employed to reduce Cd accumulation and improve Cd tolerance in maize crops. These techniques include microbial remediation, phytohormone administration, biostimulant treatments, and designed nanoparticles and mineral ions. With everything considered, this chapter offers insightful information about Zea mays physiological and adaptive responses to endure and mitigate the impact of cadmium toxicity. To ensure sustainable maize production and food security in areas polluted by Cd, it is essential to understand these mechanisms and create appropriate mitigation techniques. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Drones for Crop Monitoring and Analysis
Drones are becoming a vital tool for crop monitoring and analysis in contemporary agriculture. With the use of sophisticated sensors, these unmanned aerial vehicles (UAVs) can gather high-resolution pictures and data, giving farmers real-time insights into the growth and health of their crops. Thanks to technological advancements, drones can now more reliably and effectively collect a variety of data points than previous techniques, including plant health, moisture levels, and insect infestations. Drones are a useful tool for crop monitoring because they enable farmers to identify problems early on, such as nutrient deficits, water stress, and disease outbreaks, and take prompt action to optimize yields and avoid losses. Drones can also swiftly and affordably cover vast tracts of agriculture, giving a thorough picture of crop conditions. Farmers may use the information that drones gather to make educated decisions by choices about fertilization plans, pest control techniques, and irrigation schedules, eventually enhancing crop sustainability and output. Drone technology is projected to play an increasingly bigger role in agriculture as it develops, completely changing how farmers monitor and assess their crops. (Publisher name) (publishing year) all right reserved. -
Leveraging FinTech for the Advancement of Circular Economy
During the past six decades, there has been a lot of emphasis on increasing production and fulfilling the demands of the fast-growing population. As a result, there has been unprecedented utilization and depletion of natural resources and harm to the environment. It was rightly realized by government and policymakers that there is an indispensable need to align economic development with the environment. In other words, the world needs to pursue environmentally friendly economic development. In order to achieve sustainable development, the thought leaders devised a new approach called circular economy. The circular economy focuses on reusing and recycling materials to reduce the consumption of natural resources and minimize waste creation. In recent years, financial technology commonly known as FinTech has become a significant part of commercial activities across many industries. FinTech has benefited organizations and users in terms of cost and time saving with a high degree of reliability. This article outlines the ways in which FinTech supports the cause of a circular economy. It also explores the impediments in this path. 2024 Scrivener Publishing LLC. -
Green Minds, Green Future: Impact of Environmental Education on Students Attitudes and Intentions
The objective of this research is to examine the effect of environmental education on green behavior mediated through environmental awareness, environmental attitude, and behavioral intentions, as mediating variables. The sample population comprised of the students of various universities of Delhi, National Capital Region (NCR), as this region of the country has the highest level of environmental pollution and therefore it is the most appropriate population for this study. One thousand questionnaires were shared among students of Delhi, NCR via Google Form out of which 689 responses were received and analyzed using structural equation modeling (SEM). The results exhibited the association between environmental education and green behavior which was significantly mediated by awareness, attitude, and behavioral intention. The findings of the study have implications for both research and practice. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Exploring AI and ML Strategies for Crop Health Monitoring and Management
This chapter offers a thorough examination of machine learning (ML) and artificial intelligence (AI) approaches designed especially for agricultural crop health monitoring. The story starts with a basic introduction to AI and ML ideas and then covers supervised and unsupervised learning approaches, the fundamentals of reinforcement learning, and the significance of high-quality data preparation in agricultural settings. This chapter explores the use of deep learning architectures and neural networks, explaining how they can be used to simulate human brain activity and how they can be used in picture identification to identify crop diseases. A detailed analysis is conducted of the practical aspects of ML for agriculture, encompassing feature engineering and model assessment methodologies. Additionally, the chapter highlights the ethical issues involved in the proper application of AI/ML models in agricultural contexts. These kinds of applications. In conclusion, the chapter discusses obstacles, offers predictions for future developments, and discusses new lines of inquiry for AI and ML research related to crop health monitoring. Through this thorough research, the chapter seeks to offer insightful information on the transformative potential of AI/ML approaches in supporting efficient and sustainable agriculture practices for improved crop health management. (Publisher name) (publishing year) all right reserved. -
Synthesis methods of chitosan nanoparticles: A review
Biopolymeric nanoparticles are found to be very effective in potential applications in different fields, especially in biomedical field. Chitosan nanoparticles (CSNPs) are one of those kind of nanoparticle with great research potential owing to its nontoxicity, biodegradability, and high permeability. The extraction of chitosan biopolymer and the production of CSNPs are both vibrant research areas. Emulsification, complexing, phase inversion etc. are the conventional synthesis methods; new synthesis methods are being developed to overcome the disadvantages of traditional approaches. Here we provide a glimpse into the methodological and mechanistic underpinnings of the synthesis methods of CSNPs. This chapter provides a complete overview of the synthesis methods used for the production of CSNPs, their benefits, drawbacks, and obstacles, as well as their future prospects. 2025 Elsevier Ltd. All rights reserved. -
Nifty index: Integrating deep learning models for future predictions and investments
The Indian stock market, led by the NSE and BSE, has witnessed remarkable growth, exemplified by the NIFTY 50 index surpassing INR 176 trillion in market capitalization. Post the transformative New Economic Policy reforms in 1991, the market underwent significant expansion due to increased accessibility. This chapter focuses on predicting Nifty index prices for the upcoming 10-day period, aiming to provide valuable insights for investment decisions. Despite the markets inherent complexity, exacerbated by various factors like economic conditions and investor sentiment, the objective of the research study is clear: to boost profitability, mitigate risk, and safeguard traders capital. Leveraging Long Short-Term Memory (LSTM) and Vector Autoregression (VAR) models, the research study rigorously evaluates prediction accuracy using the Root Mean Square Error (RMSE) metric. The study underscores the potential of deep learning techniques in achieving reasonable accuracy, especially for short-term forecasts, while acknowledging the markets inherent unpredictability. Notably, the findings demonstrate that the LSTM model excels in predicting Nifty Bank prices, with an impressive RMSE score of 242.55 compared to VAR models. Furthermore, optimal data splitting, at an 8:2 ratio, significantly enhances prediction accuracy across all models, emphasizing the critical role of high-quality data in training. In conclusion, this study unequivocally recommends LSTM as the preferred model for Nifty index price prediction, providing practitioners with a robust tool to navigate the complexities of the Indian stock market with enhanced precision and confidence. 2025 selection and editorial matter, Vivek S. Sharma, Shubham Mahajan, Anand Nayyar and Amit Kant Pandit; individual chapters, the contributors. -
Pandemic Resilient Organizational Behaviour: From the Lens of Stakeholder and Legitimacy Theory
The Covid-19 pandemic spread on global map with unprecedented speed and created an environment of uncertainty, anxiety and disruption. India, being a densely populated country, had been looked upon with apprehension and later on with great admiration in controlling and managing the pandemic and its devastating effect. The study has built a thematic model for short-term and long-term pandemic resilient organizational practices based on stakeholder and legitimacy theory, which focuses on aligning business with societal values and stakeholder expectations. The foci have been stakeholder groups of employees, customers, suppliers and community. Sustainability reports of selected Indian companies based on GRI standards for FY from 2019 to 2022 are then scored based on the developed model. Further analysis explored changes in risk reporting framework in pandemic and post pandemic. The thematic coverage in sustainability reports for employees and community found a prominent place emphasizing the importance of these groups. The thematic disclosures for suppliers are the least disclosed, indicating areas for improvement in the business practices. Based on this thematic model, suggestions are also made for additional disclosure indicators in the GRI framework for stakeholder group of suppliers and customers. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
STRATEGIC PARTNERSHIPS AND REGIONAL RESILIENCE: Exploring the Evolving Landscape of India-Southeast Asia Relations
India and Southeast Asia share an elusive sphere of influence, yet face formidable challenges in realising ambitious goals set for the region. Over the years, Indias foreign policy has progressed from being principled to goal driven and objective oriented. Based on analysis of secondary sources of literature, this chapter traces through the relationship between India and Southeast Asia, highlighting a shared landscape of experiences, weaving socio-cultural practices and further boosting economic and international relations. These historical references have found avenues for remodelling in contemporary times in the form of diplomatic success in varied dimensions of engagements. Drawing from these developments and taking the transformations in the geopolitics of Indo-Pacific region into cognizance, this chapter envisions the future prospects for India and Southeast Asia through the lens of building community resilience, promoting its potential to guide regional development and explore the sustainability of social, economic and environmental systems to manage change. This renewed line of thought supports a new analytic of governance which advocates that the local define the configurations and prospects for sustainability of policy frameworks and agreements in the global system. Thus, in the background of the rising traditional and non-traditional challenges, this chapter contributes to a better understanding of change and complexity through a revitalised scope for coordination, cooperation and pragmatism in partnership between the countries. 2024 Taylor & Francis. -
Machine Learning-Based Driver Assistance System Ensuring Road Safety for Smart Cities
Technologies around smart city and green computing are gaining more and more interest from diversified workforce areas. The transportation system is one of them. The transportation vehicles are operating day and night to provide proper support for the need. This is really tiring for the transportation workers, especially the drivers who are driving the vehicle. A slight negligence of a driver may cause a huge loss. The increasing number of road accidents is therefore a big concern. Research works are going on to comfort the drivers and increase the security features of vehicle to avoid accidents. In this chapter, a model is proposed, which can efficiently detect drivers drowsiness. The discussion mainly focuses on building the learning model. A modified convolution neural network is built to solve the purpose. The model is trained with a dataset of 7000 images of open and closed eyes. For testing purpose, some real-time experiments are done by some volunteer drivers in different conditions, like gender, day, and night. The model is really good for daytime and if the driver is not wearing any glass. But with a glass in the eyes and in night condition, the system needs improvements. 2025 selection and editorial matter, Yousef Farhaoui, Bharat Bhushan, Nidhi Sindhwani, Rohit Anand, Agbotiname Lucky Imoize and Anshul Verma; individual chapters, the contributors. -
AI Applications Computer Vision and Natural Language Processing
Artificial intelligence (AI) applications in computer vision and natural language processing (NLP) have made major advances in recent years, challenging a number of sectors and areas. This multidisciplinary topic combines NLP, which examines the study of human language, and computer vision, which concentrates on the understanding of visual data. This study examines the wide range of applications that are included within this convergence, highlighting the revolutionary potential of AI technology. AI has made it possible to make significant advances in autonomous systems, object identification, and image recognition in the field of computer vision. These developments have stimulated innovation and increased efficiency, revolutionizing sectors including healthcare, autonomous vehicles, and security. Meanwhile, AI-driven advances in NLP have produced strong language models that can produce, comprehend, and translate text. These approaches have been utilized to improve accessibility and efficiency of communication in chatbots, sentiment analysis, and language translation services. This chapter explores the basic ideas and advancements in these two fields, emphasizing the opportunities and novel challenges that arise from integrating computer vision and NLP. Additionally covered are data privacy, ethical issues, and the possibility of prejudice in AI applications. The study also highlights the ongoing need for these fields' advancement and investigation in order to solve real-world problems and fully utilize AI's potential in the computer vision and NLP industries. 2025 The Institute of Electrical and Electronics Engineers, Inc. -
Understanding Binary Employees Awareness Toward LGBTQ Inclusion atWorkplaces
The LGBTQ [Lesbian, Gay, Bisexual, Transgender, and Queer] community does not comply with the conventional categorization of gender identity and sexual orientation. While there are laws that provide reservations to transgender employees, the other members of the LGBTQ community still find job security as a significant career threat if the member is open about their respective gender identity or sexual orientation. Individuals who belong to the LGBTQ community are facing several forms of discrimination in the workplace. Obtaining jobs has also become problematic. The study aims to understand the gender binary employees awareness, perspective, and support toward the LGBTQ community. The study is exploratory. The sample consisted of 238 respondents; data was collected from gender-binary employees working in white collared jobs in Bengaluru City. Gender has an impact on the awareness of binary employees regarding the LGBTQ community, sexual orientation, and sexual identity. With inclusion practices, diversity policy, and pride celebration, the world is moving toward an inclusive and welcoming sphere. Still, the absence of awareness and support will hinder the development and welfare of the LGBTQ community. The findings denote the need to increase awareness and broaden the horizon of inclusion practices. Applying inclusion awareness at all employment levels is imperative to create an equity-driven and inclusive workplace. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Internet of Senses: immersive eating and traversing into the metaverse
[No abstract available] -
Energy efficiency and conservation using machine learning
This chapter explores the fascinating nexus between machine learning (ML), energy efficiency, and conservation, concentrating on a captivating case study that makes use of the oneAPI framework. Optimizing energy consumption has become crucial due to the increased interest in sustainable practices. By investigating the use of oneAPI in energy efficiency projects, we examine the possibility of ML techniques to overcome this difficulty. We demonstrate how ML algorithms can accurately model and anticipate energy usage patterns through a thorough analysis of real-world data. Additionally, we discuss the importance of feature engineering, algorithm selection, and data pretreatment in creating accurate energy consumption models. The case study emphasizes the wider implications of utilizing ML to support energy-saving initiatives in addition to demonstrating the effectiveness of oneAPI. 2025 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
Educational Management System Using Hybrid Blockchain Network
This work explores integrating blockchain technology into the education system, focusing on academic Records and administrative control systems. It addresses challenges in data handling and security in centralized education administration. The proposed framework entails a Hybrid blockchain network that integrates elements of both private and public blockchains. In this architecture, the public network functions as a university network, while individual nodes within it represent distinct colleges, each equipped with its exclusive private network. Private blockchain manages registration, secure hash generation, and student assessment, while public blockchain handles record management. Adding a randomized mining algorithm to public blockchains and a proof of authority consensus mechanism strengthens transaction security. Introducing a voting miner selection algorithm in private blockchains further enhances security measures. This integration represents a strategic evolution in education management systems, unlocking new security, scalability, and performance possibilities. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Sustainable materials for urban streets: trends, challenges, and case studies
Urban planners face a growing need for efficient, smart, and sustainable projects. One of the dynamic urban elements of cities is its streets, which accommodate the majority of the public realm. This study aims to identify sustainable materials that are employed in the construction of urban streets and analyze the potential for other sustainable materials in future street design. We conduct a thorough literature review through case studies and identify sustainable materials currently in use in the construction of urban streets across the world. This study focuses on existing and potential sustainable materials for urban streets suitable for Qatar. Hence, the objectives of this study are: (1) to identify sustainable materials in the construction of urban streets; (2) to analyze challenges to using sustainable materials in making urban streets more sustainable; (3) comparative analysis of the case studies. The study concludes with sustainable urban street design guidelines derived from Qatar. 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Early stage detection of osteoarthritis of the joints (hip and knee) using machine learning
This study explores the developing relationship between health care and technology, with a special emphasis on the use of machine learning (ML) algorithms to detect early stage osteoarthritis (OA) in the hip and knee joints. OA, a substantial worldwide health problem, requires improved diagnosis techniques. In this analysis, we illuminate the limitations of traditional methods, emphasizing the inherent subjectivity of clinical assessments and the delay in detection using routine imaging techniques. The research investigates the potential of ML to bring about significant changes. It focuses on combining various algorithms with extensive datasets and highlights the need to select relevant features and prepare the data to improve the accuracy of the models. The use of ML is closely connected to ethical issues, which include the protection of data privacy and the capacity to comprehend the models used. To bridge the gap between theory and practice, the chapter presents concrete examples of ML's practical use in detecting OA, opening possibilities for customized therapy and enhanced patient results. The chapter also highlights potential areas for future study, emphasizing the urgent requirement for additional progress in ML-based early detection techniques to alleviate the worldwide impact of OA. 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Integration of Intelligent System and Big Data Environment to Find the Energy Utilization in Smart Public Buildings
Buildings are the leading consumer of energy in the setting of smart cities, and public structures such as hospitals, schools, government offices, and additional institutions have high energy needs owing to their frequent use. However, there needs to be adequate use of the latest innovations in machine learning inside the big data context in this field. Controlling the energy efficiency of public subdivisions is a crucial aspect of the smart city concept. This chapter aims to address the challenge of integrating big data platforms and machine learning algorithms into an intelligent system for this purpose to forecast how much energy various Croatian government buildings will consume, prediction models were constructed using deep learning neural networks, Rpart regression tree models, and random forests using variable reduction techniques. The evaluation of all three techniques considered critical aspects, and the random forest methodology yielded the most precise model. The MERIDA intelligent system aims to enhance energy efficiency in public buildings by integrating big data and predictive algorithms. This research examines the technological requirements for a platform that facilitates public administration in planning public building reconstruction, reducing energy consumption and expenses, and connecting intelligent public buildings in smart cities. Digitizing energy management may improve public administration efficiency, service quality, and environmental health. 2025 Scrivener Publishing LLC. All rights reserved. -
AI-based online interview bot with an interactive dashboard
In recent years, video interviews have become increasingly popular in the recruitment process due to their convenience and efficiency. However, evaluating a candidates communication skills and perceived personality traits from a video interview can be challenging. The agent utilizes natural language processing and computer vision techniques to analyze the candidates verbal and nonverbal behavior during the interview. Specifically, the agent focuses on linguistic features such as fluency, grammar, and vocabulary, as well as nonverbal cues such as facial expressions and body language. Based on these features, the agent predicts the candidates communication skills and perceived personality traits. To validate the effectiveness of the agent, a Talk was conducted with a group of participants who completed video interviews with and without the agent. The results show that the agents predictions of communication skills and perceived personality traits are highly correlated with the ratings given by human evaluators. Additionally, the agent is able to provide valuable insights into the candidates performance that may not be immediately apparent to human evaluators. Overall, the intelligent video interview agent proposed here has the potential to improve the recruitment process by providing more accurate and objective evaluations of candidates communication skills and perceived personality traits. 2025 selection and editorial matter, A. Vadivel, K. Meena, P. Sumathy, Henry Selvaraj, P. Shanmugavadivu and Shaila S. G.; individual chapters, the contributors. -
Regression Approach for Predictive Analysis in Cognitive Decline
Cognitive decline refers to the deterioration of cognitive abilities, including memory, thinking, and reasoning, often associated with aging or neurological disorders like Alzheimer's disease. Machine learning (ML) methods can be used for predicting cognitive decline. Techniques such as Generative Adversarial Networks (GANs), feed-forward neural networks, supervised, and unsupervised learning process and analyse data patterns to forecast cognitive changes. By analyzing large datasets, ML algorithms can identify subtle cognitive shifts and predict future decline, enabling early intervention and personalized healthcare strategies. These diverse ML methods provide valuable tools for understanding, detecting, and potentially mitigating cognitive decline, advancing our ability to address cognitive health challenges. Some of these methods have been discussed later. In this research paper, a model to predict cognitive decline using principles of logical regression is proposed. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.