eChapters

Introduction
Information Technology (IT) is indispensable in transforming national policies, governance frameworks, and administrative structures, making them more efficient, transparent, and responsive. In policy transformation, IT is a powerful enabler that can streamline bureaucratic processes, improve access to public services, facilitate data-driven decision-making, and enhance citizen engagement. As countries strive to modernize their governance systems, integrating IT into policy frameworks is vital in ensuring inclusive growth, social equity, and sustainable development. This integration is especially critical for a country likeIndia, which, with its diverse population, vast geographical expanse, and complex socioeconomic challenges, requires innovative policy tools that are both scalable and adaptable. The potential of IT in policy transformation is multifaceted and can significantly improve the efficiency, accountability, and inclusivity of public governance.

Introduction
Climate change mitigation refers to the efforts and strategies to reduce or prevent the release of greenhouse gases (GHGs) into the atmosphere to limit global warming and its subsequent impacts on ecosystems, human health, and economies. This process is critical to achieving the global climate targets set under frameworks like the Paris Agreement. Technology, particularly Information Technology (IT), has emerged as a vital tool in the fight against climate change, providing a range of solutions to monitor, manage, and mitigate the environmental impacts caused by human activities. IT-driven strategies enable real-time monitoring of environmental variables, optimising resource use, and developing innovative solutions across various energy, transportation, agriculture, and urban planning sectors.

Introduction
Integrating Information Technology (IT) into waste management has revolutionized how societies address the growing challenges of waste generation, collection, segregation, recycling, and disposal. Rapid urbanisation and industrialisation have escalated the global waste burden, necessitating efficient and sustainable solutions. IT is pivotal in enabling smart waste management systems through real-time monitoring, data analytics, and automation. This chapter explores the transformative impact of IT on waste management, highlighting key technologies, their applications, challenges, and prospects.

Introduction
The economic transformation through information technology (IT) has emerged as one of the most influential drivers of modern economic development across the globe. In the 21st century, IT has catalyzed sweeping changes in industrial processes, global markets, and the overall economic structure, impacting everything from productivity to business models, labor markets, and governmental policy. The proliferation of IT has led to profound shifts in economic paradigms, influencing sectors ranging from agriculture and manufacturing to services and finance. The integration of IT into these various domains has spurred growth by fostering innovation, reducing inefficiencies, improving resource management, and creating new opportunities for economic development.

Incentives are a powerful stimulus to transfer of technology. They can encourage researchers to get their results to market, a new career path for many of them. They can create a culture that attracts and retains entrepreneurial staff, scientists, students and technology transfer professionals (TTPs) that view the university as a career path and a vehicle for making a contribution to society. Working in a pro-entrepreneurship team of colleagues can also lead to getting people to think in terms of making a connection between their work and its application. Incentives can be short-term or long-term in nature, and can be tailored to individual or collective requirements. As it is likely to be challenging for universities to design incentives that cater to every researcher’s or TTP’s desire, they can design incentive programs to cater to various aspects of human. Nature.
12.1 Why incentives matter in this ecosystem
Incentives can be either financial and non-financial. These are needed to ensure that researchers and TTPs remain motivated, aligned with institutional goals, and committed to the long-term process of technology transfer and commercialization. Incentive programs serve several important purposes:
• Motivation: Incentives energize individuals to pursue innovation and commercialization, often over long- and uncertain-time horizons.
• Retention: By offering recognition, rewards, and career advancement opportunities, institutions can retain high-performing researchers and skilled TTPs.
• Alignment: Well-designed incentives ensure that the personal goals of stakeholders align with institutional and societal objectives.

Dashboards play a crucial role in the fields of Machine Learning and Artificial Intelligence by providing a visual and interactive interface through which complex data, model outputs, and performance metrics can be easily understood and monitored. In data-driven environments, where vast amounts of information are processed continuously, dashboards serve as an essential bridge between technical systems and human decision-making. They transform raw data and analytical results into meaningful visual representations, enabling users to interpret insights quickly and effectively. In the context of Machine Learning and Artificial Intelligence, dashboards are used at multiple stages of the workflow. During data exploration and preprocessing, dashboards help visualize trends, patterns, and anomalies in datasets, allowing data scientists to make informed decisions about feature selection and data cleaning. Once models are developed, dashboards

SQL, or Structured Query Language, provides a powerful framework for querying, aggregating, and transforming financial data stored in relational databases, which is essential for constructing accurate and dynamic financial models. Unlike spreadsheet-based models, SQL enables automation, repeatability, and scalability, making it particularly useful when dealing with complex datasets or real-time financial information. The first step in financial modeling with SQL is understanding the structure of the database. Financial data is often stored across multiple tables, including transactions, accounts, customers, and market data. Effective financial modeling requires familiarity with relational database concepts such as primary keys, foreign keys, and normalization. By using SQL joins, analysts can merge these tables to create a unified dataset that captures all necessary information, such as revenues, expenses, and cash flows, which are essential for forecasting and valuation exercises. Data extraction in SQL

AI-driven animal husbandry is advancing through machine learning, computer vision, IoT, big-data analytics, wearable devices and automated equipment. This chapter considers future applications for monitoring animal health, behaviour, nutrition, reproduction and environmental conditions. Predictive systems can support early disease detection, precision feeding, improved breeding and reduced production costs. However, high implementation costs, data privacy, inadequate infrastructure and limited digital knowledge remain significant obstacles, particularly in developing regions. Future research should prioritize affordable, user-friendly and explainable technologies suited to smallholders. Ethical AI, climate adaptation, multimodal data integration, omics analysis and interdisciplinary collaboration among animal scientists, engineers, policymakers and farmers are identified as essential research directions.

IoT networks and smart sensors enable continuous livestock monitoring, overcoming the delay and subjectivity associated with manual observation. This chapter reviews sensor types, communication systems, cloud and edge computing, and their applications in animal-health and behaviour tracking. It proposes a framework integrating multimodal sensors with IoT networks and machine-learning models such as CNNs and LSTMs. The system can detect abnormalities, predict health conditions and provide real-time decision support. Practical benefits include earlier intervention, improved welfare, reduced labour and more efficient production. The chapter also addresses implementation problems, costs, connectivity, data security, technical reliability and ethical considerations associated with constant digital monitoring of animals.

Livestock farms generate large quantities of physiological, environmental, behavioural and production data through sensors, IoT devices and management records. Raw information must be processed before it can support dependable analysis. This chapter explains data cleaning, missing-value treatment, noise reduction, anomaly detection, integration, normalization, transformation and dimensionality reduction. It also discusses feature extraction, categorical encoding, variable transformation and temporal-feature analysis. Machine learning, AutoML, Python, R and other analytical platforms are introduced as tools for preparing livestock datasets. Applications include disease prediction, yield improvement and movement monitoring. The chapter concludes by examining scalability, data quality, privacy and ethical challenges in intelligent livestock analytics.

Precision feeding aims to supply animals with appropriate nutrients according to their changing physiological state, growth, production and environment. Conventional feeding plans are often static and may lead to waste, increased costs and environmental losses. This chapter introduces an AI-enabled framework integrating animal data, feed composition, growth characteristics and external factors. Regression, ensemble learning and other machine-learning models predict feed intake and weight gain, while linear programming and metaheuristic optimization generate efficient allocation plans. A decision-support system provides recommendations in real time. The approach can improve feed conversion, nutrient utilization, profitability and sustainability while reducing overfeeding, resource wastage and the environmental impact of livestock production.

In the foregoing experiments performed either in CRD or RBD or LSD. We were primarily concerned with the comparison and estimation of the effects of a single set of treatments like varieties of paddy, different methods of cultivation etc., and such experiments which deal with one factor only may be called simple experiments. In factorial experiment the effects of several factors of variation are studied and investigated simultaneously in a single experiment, such experiments are known as factorial experiments. In these experiments an attempt is made to estimate the effects of each factors and also the interaction effects i.e. the variation in the effect of one factor as a result to different levels of other factors.  These experiments provide an opportunity to study not only the individual effects of each factor but also interactions.  In many biological and clinical trials factors are likely to have interaction. Therefore, factorial experiments are more informative in such investigations.

ABSTRACT
 
The term rural development also represents improvement in quality of life of rural people in villages. As per Chambers (1983) “Rural Development is a strategy to enable a specific group of people, poor rural women and men, to gain for themselves and their children more of what they want and need.”  ICT is an integral part of development strategies of both developing and developed countries. It has great potential to bring in the desired social transformations by enhancing access to people, services, information and other technologies (Dutton et al., 2004). ICT applications can enhance poor people’s opportunities by improving their access to markets, health and education. Furthermore, ICT can empower the poor by expanding the use of government services and reduce risks by widening access to micro finance (Cecchini and Scott, 2003). The uses of ICT for development are actively promoted for economic development, job-creation, rural development and poverty-alleviation. The term ‘ICTs’ can be interpreted as including a wide range of media, ‘new ICTs’ is used to denote “the use of computers and communication systems between computers” (CTA 1999:4).

Introduction

Training is the crucial and expensive component of HRD, intended to improve the efficiency and effectiveness of performance of the members of organization through planned learning efforts, transfer of such learning at work place and in turn improvement of the organizational performance. Given such objectives, most of the training programmes fail to inculcate the transfer component in trainees (Subedi 2004). Hence, there is an increasing concern over training transfer at work place and the resultant improved organizational performance in terms of productivity, efficiency, effectiveness and turn over etc (Howard 1995, Huselid 1995, Martocchio and Baldwin 1997 and Salas et al.. 2000, Thayer1997).

Introduction

Zymomonas mobilis, a gram negative, anaerobic, micro aerotolerant, ethanologenic bacterium metabolizes glucose, fructose, and sucrose. One of the major factor that limits the usage of Z. mobilis is the narrow substrate range. Moreover, there are no obvious genes reported for using lactose, maltose or cellobiose. Our preliminary sequence analysis of the Zymomonas mobilis ZM4 genome indicated the presence of a putative beta-galactosidase (bga) gene. However, the mechanism of beta-galactosidase (Bga) function is still unknown. Hence, Bioinformatics analysis was made to predict the physicochemical, biochemical and biophysical properties, domains, secondary and tertiary structures of the putative beta-galactosidase. These studies suggested that the putative beta-galactosidase gene was poorly expressed from its native promoter in Z. mobilis. The absence of beta-galactoside transacetylase and permease genes in Z. mobilis led to its inability to metabolize lactose. Hence, function predictions of Bga are performed because of the fact that it has a homologous partner.

Abstract
This paper presents a lucid and application-oriented delineation of the theory of probability which has proved itself as a basic tool in the theoretical and applied researches in almost all disciplines of science, specially, in the problems encompassing the real-life issues. Bio-informatics has recently emerged as a potential discipline of science and, needless to mention that the application of the probability theory has delved deep to elucidate the laws under its ambit. The inference procedures based on biochemical systems require the formulation of stochastic models (owing to the prevalence of low to moderate levels of stochasticity and noise in their dynamics) which, in turn, are developed using the probability laws in abundance. In Systems Biology, the estimation of parameters poses a prime task in biokinetic models developed on experimental data, and such estimation procedures have been possible only by applying appropriate tools derived using probability. Likelihood-free Bayesian inference that explicitly accounts for discrepancies between the model and the data, termed Approximate Bayesian Computation under model uncertainty is an off-shoot of the celebrated, Bayes' theorem of probability.

The Internet is a worldwide network of networks connecting millions of users, spread across continents, exchanging thousands of gigabytes of information, accessing numerous databases that cover everything from cooking to astronomy, all relatively inexpensively. The Internet offers virtually unlimited amounts of information related to animal medicine and provides all the tools to access it. ‘www’ is probably the most famous three letters of the last century, signifying a series of servers that are interconnected with each other through hypertext transfer protocol or ‘http’. A significant part of the credit for increasing the popularity of the Internet belongs to the World Wide Web. It is always better to use evaluated web resources as the quality of websites is a problem in cyberspace.

I. ROLE OF INTELLECTUAL PROPERTY

The international community has set itself the target of reducing the proportion of people below poverty line to half by 2015, along with associated specific targets for improving health and education and environmental sustainability. The Millennium Development Goals, adopted at various international fora, recognize the crucial importance of reducing poverty and hunger, improving health and education, and ensuring environmental sustainability. It is important to know whether and how intellectual property rights (IPRs) could play a role in helping the world meet these targets — in particular by reducing poverty, helping to combat diseases, enhancing access to education and contributing to sustainable development, protecting our cultural heritage, safeguarding the food security for the country. It is also necessary to deliberate whether and how they create obstacles to meeting those targets and, if so, how those obstacles can be removed.

There are strong views from both sides on the protection and enforcement of IPRs though there is no data to check these views objectively. Some argue strongly that IPRs are necessary to stimulate economic growth, which in turn, helps in poverty reduction. By stimulating invention and new technologies, IPRs help to increase agricultural or industrial production, promote domestic and foreign investment, facilitate technology transfer and improve the availability of medicines necessary to combat diseases. They are also of the view that there is no reason why a system that has worked for developed countries would not work for developing countries. Others argue equally vehemently the opposite. IPRs do little to stimulate invention in developing countries, because the necessary human and technical capacity may be absent there. In assessing these opposing arguments, it is important to remember the technological disparity between developed and developing countries as a group. What role the intellectual property can play to plug this gap is a serious subject for every developing nation? For this, it is also pertinent to know about the IPRs, the challenges they pose to the legal system in general and to the Indian legal system in particular, and their basic rationale to take a considered approach in their implementation.

I. INTRODUCTION

Procedural remedies for the infringement of one’s right are not enough by themselves unless they result in some reliefs of substantive nature whereby it can reasonably be said that these reliefs, temporary or permanent, as the case may be, are enough in the circumstance to make good or, say, compensate the genuine loss of the aggrieved party to an infringement suit. The substantive remedies available to a successful plaintiff at any stage in any suit for patent infringement could, however, be derived from the express as well as by necessary implication of the provisions of the Patents Act, 1970 dealing with the “Reliefs in suit for infringement”. Its Sec. 108 declares in terms quoted below:

Big Data refers to extremely large, very fast, highly diverse, and complex data that cannot be managed with conventional data management systems, tools or processes. In the perfect world, Big Data may include all kind of data, which can deliver the right information, to right person, in the right quantity, at the right time, to help in making the right decisions.

15.1 Data Visualization

Data visualization is the graphical representation of information and data. By using different visual elements like charts, graphs and maps. Data visualization tool provides easy way to see the understand the data and related trend/pattern.

1. There are multiple tools available for data visualization using which we can perform data visualization.

2. In python we can use a library called “matplot” for data visualization.

3. Python based plotting library “matplotlib” enables us to produce 2D as well as 3D graphics i.e. charts/graphs.

4. Pyplot interface of matplotlib is used for representing 2D graphs.

In this chapter, the focus is on understanding the importance of experimental design and being familiar with key terminology. You’ll also learn about the three fundamental principles of experimental design and their roles in conducting experiments. Additionally, you’ll learn how to do randomization of treatments in various experimental designs using R.

Experimental Design

Experimental design is the methodical planning of scientific experiments for reliable results by controlling variables and minimizing biases. It serves as the foundation for effective research. Some key definitions to enhance understanding of this concept:

Key terminologies

• Experiment: Structured investigation for knowledge acquisition or validation.

• Experimental unit: Entity receiving treatment (e.g., land plot, pot).

• Treatment: Specific procedure for assessment and comparison.

• Experimental error: Variation among identically treated units.

Principles of Experimental Design

Experiments are vital tools in agricultural research, aiming to assess treatment effectiveness and quantify their impact. However, extraneous factors can confound results. To mitigate these issues, we depend on three core principles of experimental design: replication, randomization, and local control.

Basics of Technology Incubation
Technology incubation is a fascinating process that fosters the growth of innovative startups and businesses with high-potential technology. It provides a supportive environment and resources for early-stage companies to develop their ideas, validate their business models, and navigate the challenges of commercialization.
Here’s a breakdown of the key aspects of technology incubation: What happens in a technology incubation?
Pre-incubation: Startups with promising ideas can receive initial guidance and support to refine their concepts and develop a business plan.
Incubation: Selected startups gain access to shared office space, mentorship from experienced professionals, networking opportunities, and access to funding sources.
Post-incubation: Graduated startups continue to receive support in areas like marketing, legal advice, and investor relations as they scale their businesses.
Benefits of Technology Incubation
Reduced risks: Startups can test their ideas and validate their market potential before investing significant resources.
Access to resources: Incubators provide vital resources like office space, equipment, and software, reducing the financial burden on startups.

Introduction
In global environmental challenges, sustainable products have gained considerable prominence. Sustainable products are those designed, manufactured, and disposed of in ways that minimize environmental impact, promote resource efficiency, and
contribute to a circular economy. Integrating information technology (IT) into sustainable products’ design, production, and lifecycle management has emerged as a crucial enabler of innovation and efficiency in achieving environmental sustainability. IT-enhanced sustainable products leverage technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), blockchain, and data analytics to reduce waste, optimize resource use, extend product lifecycles, and enhance transparency. By incorporating IT into the lifecycle of products, manufacturers and designers can make smarter decisions that have far-reaching benefits for both the environment and the economy. This detailed analysis will
explore how IT contributes to creating sustainable products, focusing on product design, resource optimization, manufacturing processes, consumer engagement, and end-of-life management.

Introduction
In the modern age of digital transformation, platforms have become critical enablers of sustainability. These digital infrastructures, which facilitate connectivity, resource sharing, and collaboration, are pivotal in accelerating the adoption of sustainable practices across various sectors. Platforms enable the effective integration of resources, stakeholders, and technologies, offering scalable solutions that can help achieve long-term environmental, economic, and social sustainability goals. Whether through promoting circular economy models, enabling efficient resource use, fostering innovation in green technologies, or driving policy changes, platforms serve as central hubs for fostering sustainable development.

Introduction
Given increasing demands, dwindling resources, and climate-induced uncertainties, water resource management is a critical challenge in the 21st century. Integrating Information Technology (IT) into water resource management offers transformative solutions to optimize water usage, monitor resources, predictshortages, and enhance distribution efficiency. IT tools such as remote sensing, geographic information systems (GIS), artificial intelligence (AI), Internet of Things (IoT), and big data analytics are at the forefront of this evolution, enabling more effective planning, decision-making, and resource sustainability.

Introduction
Sustainable agriculture refers to farming practices that meet current food production needs without compromising the ability of future generations to meet their own needs. It aims to balance environmental health, economic profitability, and social equity, ensuring that agricultural systems can be productive, resource-efficient, and resilient over time. The role of Information Technology (IT) in achieving these objectives has become increasingly crucial as it provides innovative solutions to optimize agricultural practices, minimize resource consumption, and improve overall productivity while safeguarding the environment. Through the integration of IT tools such as Geographic Information Systems (GIS), Internet of Things (IoT), Big Data analytics, Artificial Intelligence (AI), and cloud computing, sustainable agriculture can be advanced in ways that were previously unimaginable. These technologies facilitate precision farming, enhance resource management, monitor crop health, predict weather patterns, and provide farmers with the information to make data-driven decisions that foster sustainability.

Introduction 
The evolution of public administration in the digital age has been anything but linear. As governments worldwide grapple with waves of technological advancement, the boundaries between reform phases such as e-government, e-governance, digital government, and digital governance often blur. Transformations occur unevenly across sectors and regions, with older paradigms coexisting with or even outlasting newer innovations. As Dunleavy, Margetts, Bastow, and Tinkler (2006, p. 216) perceptively observe: “Defining periods in the evolution of any complex system, such as public management systems in advanced industrial countries, is tricky. New developments accumulate while older trends are still playing out and flourishing. Relatively established ideas move from leading-edge countries or sectors to implementation in previous laggard areas, even as the same ideas are being repudiated or reversed in the erstwhile pioneering locations. Moreover, a confusing welter of changes goes on simultaneously, amongst which it is difficult to distinguish ephemeral and hyped-up innovations from those that are fundamental and longerlasting”. This observation aptly captures the administrative reforms’ non-linear and layered character in the digital era. The coexistence of multiple reform paradigms ranging from early e-government initiatives focused on service delivery, to broader e-governance aimed at leveraging technologies for participatory governance, and finally to the strategic integration seen in digital governance illustrates how public sector innovation is cumulative and contested. Digital governance, in particular, cannot replace previous phases. Instead, it serves as their expansion and convergence, wherein technology, data, and citizen-centricity are embedded in policymaking, service design, and institutional accountability. To fully understand the rise of digital governance, examining the broader historical trajectory of administrative reforms is important. Since the 1960s and 1970s, such reforms have consistently remained a focal point for both

Introduction 
India’s digital governance journey represents a transformative reimagining of public administration in the 21st century. The integration of Information and Communication Technologies (ICTs) into governance structures has enabled a paradigmatic shift from hierarchical, rigid, and often exclusionary bureaucratic systems to more responsive, transparent, participatory, and citizen-centric governance mechanisms (Bhatnagar, 2004; Madon, 2009; Singh, 2013; Rao et al., 2020). This shift aligns with global trends in public administration, where digitalization is seen as a means to enhance state capacity, citizen engagement, and administrative efficiency. India’s progress in this domain is evidenced by its improved performance in international indices. For instance, the United Nations E-Government Development Index (EGDI) 2022 places India 105th out of 193 countries, with notable improvements in the Online Services Index (OSI) and Human Capital Index (HCI), making it one of South Asia’s top digital governance performers despite a recalibrated drop in global ranking (UN DESA, 2022). In the Network Readiness Index (NRI) 2023, India stood at 65th among 134 countries, reflecting advancements in digital infrastructure, skills, and public digital platforms like Aadhaar and UPI (Portulans Institute, 2023). However, challenges persist, as highlighted by the ND-GAIN Index, which underscores India’s vulnerability due to digital inequality and climate-sensitive infrastructure (Notre Dame Global Adaptation Initiative, 2023). These performance indicators reflect a dual narrative: India is at the forefront of digital public innovation, yet its trajectory remains marred by barriers to inclusivity, last-mile service delivery, and institutional adaptability. The conceptual framework of digital governance in India encompasses key principles and mechanisms that collectively aim to bridge these gaps and foster a digitally empowered society and knowledge economy. Digital governance extends beyond mere e-Governance or ICT deployment; it refers to the strategic, integrated use of digital tools to transform governance models, improve service delivery, enhance transparency, and facilitate citizen

Introduction
Understanding the concepts of technology transfer, licensing, and commercialization is crucial as it plays an important role in understanding how scientific discoveries leads to generation of new technologies further making them the impactful solutions that benefit industries and society. In the context of Indian HEIs, this especially important because a large volume of impactful research often remains underutilized due to limited commercialization channels. This chapter explains the terminology, essential components of technology transfer, outlines its significance for the innovation ecosystem. It also outlines the evolution of India’s technology transfer ecosystem, and emphasizes while understanding its potential to boost India’s economy, encourage entrepreneurship, and strengthen global competitiveness. From initial idea to a market-ready product is complex and multiphased process. The stepwise process is shown in Figure 1. It highlights a structured pathway consisting of five core phases: Idea/Concept, Technology development, Intellectual Property (IP) rights, Technology transfer, and Technology commercialisation.
Phase 1: Idea/Concept stage
This process of technology transfer starts with the Idea/Concept phase, where an invention is created. Each phase plays an important role in transforming raw scientific knowledge into innovative solutions that benefit society and generate economic value. In this phase breakthrough ideas emerge, within labs. Researchers explore and develop initial proof-of-concept or prototypes to validate their ideas. At this stage, the focus is primarily on knowledge creation rather than commercialization. This pathway of technology transition is summarized in Figure 1.

A TTO needs a planned budget of five years to sustain it in the long term. This involves budgeting the infrastructure, manpower, research, and outreach programs. The budgetary needs of a TTO will be based on the size and scope of the TTO and the maturity of the parent institution it caters to. The initial years will be focused on capacity development, the appointment of experts, and the establishment of processes of intellectual property management. The mature TTO will generate income through the returns of the licensing agreements and the collaborative efforts with the industry. Nevertheless, external funding through grants and government schemes plays a vital part, particularly in the initial years. The planned budget ensures long-term stability and development of the TTO.
9.1 Budgetary heads
9.1.1 Operational expenditure
Operational expenditure refers to the ongoing costs incurred by a TTO to maintain its day-to-day operations. This includes the cost of maintaining the office, including rent, power and utility bills, computer and network infrastructure, patent management cost, and administration charges. The filing and legal charges also fall within this category. The overheads will be a heavy burden if not planned with finances properly. A TTO has to be provided with a properly planned operational budget that covers the daily operational expenses. The operational expenses help the office perform effectively, sustain the employees, and effectively manage IP. The operational expenses that the budget normally covers include:

Technology transfer (TT) is entering a transformative phase shaped by globalization, digitization, and the growing role of start-ups in the innovation ecosystem. Traditional linear models of TT where inventions flowed from academic laboratories to industry through licensing are being replaced by dynamic, multi-directional, and collaborative frameworks. This chapter explores emerging trends that will define TT in the coming decades, including open innovation platforms, digital TT ecosystems powered by artificial intelligence (AI) and blockchain, the rise of entrepreneurial start-ups and spin-offs, internationalization of TT through cross-border collaborations, and sector-specific shifts driven by 5G, biotechnology, drones, and green energy. Drawing from global and Indian contexts, the chapter highlights how universities, governments, and industries can leverage these trends to build inclusive, resilient, and sustainable innovation ecosystems.
Three global shifts which will redefine TT
1. Digitization of knowledge: With AI, big data, and blockchain, knowledge and IP can be shared, tracked, and commercialized faster and more transparently.
2. Entrepreneurial ecosystems: Start-ups, incubators, and accelerators are replacing traditional firms as vehicles of commercialization, particularly in deep-tech and frontier technologies.
3. Globalization of innovation: Challenges such as pandemics, climate change, and food security require cross-border TT collaborations, making TT an instrument of both economic and geopolitical significance.

Remote sensing and Geographic Information Systems provide spatial information for monitoring crops, land, soil and water resources. This chapter introduces their fundamental principles and explains developments involving satellites, UAVs, hyperspectral sensors, thermal imaging and cloud-based geospatial platforms. Analytical techniques include vegetation indices, time-series analysis, spatial modelling and artificial intelligence. Applications cover crop-health monitoring, yield forecasting, irrigation planning, drought assessment, pest and disease identification and soil-resource management. Integration with IoT, automation and other digital technologies improves the timeliness and resolution of agricultural information. These tools support evidence-based decisions responding to food-security, climate-change and natural-resource sustainability challenges.

Artificial intelligence offers practical solutions to agricultural problems associated with population growth, food demand, climate change and limited natural resources. This chapter introduces machine learning, deep learning, computer vision and natural language processing, together with enabling technologies such as IoT, remote sensing and cloud computing. It explains their applications in crop monitoring, disease recognition, yield forecasting, irrigation, soil assessment and livestock management. AI can improve decision-making, resource efficiency, automation and precision farming while reducing reliance on manual labour. The chapter also examines adoption barriers, including high costs, limited digital literacy, data-security concerns and weak rural infrastructure, and considers future developments in autonomous and climate-resilient farming.

Artificial intelligence can detect subtle changes in animal health before they become apparent through conventional observation. This chapter examines machine learning, deep learning and predictive analytics for livestock disease prediction and diagnosis. Models use physiological signals, diagnostic images, behaviour patterns, environmental conditions and historical records to recognize abnormalities and estimate disease risk. Wearable sensors and IoT devices enable continuous monitoring and early-warning systems, supporting preventive veterinary care and outbreak management. AI-based detection can reduce mortality, treatment costs and production losses while improving decision-making. The chapter also evaluates challenges involving inconsistent data, model interpretability, rural infrastructure, technical accessibility and the practical implementation of diagnostic systems.

Animal behaviour provides valuable information about health, welfare, reproduction, environmental adaptation and social interaction. Traditional observation is labour-intensive, time-consuming and vulnerable to observer bias. This chapter explains how machine learning automates behavioural analysis using video, sensor and bioacoustic data. It covers preprocessing, feature extraction, classification, clustering and recurrent neural networks for temporal analysis. Computer-vision techniques—including object detection, tracking and pose estimation—enable continuous and non-invasive monitoring. Case studies demonstrate applications in livestock management, wildlife conservation and laboratory research. The chapter also examines data labelling, model generalization, ethics, explainable AI, real-time processing and the integration of multiple behavioural data sources

Digital dairy farming integrates IoT, artificial intelligence, machine learning, big-data analytics, cloud computing, sensors and automation to improve herd management. This chapter explains how wearable devices and monitoring systems support early disease detection, welfare assessment, milk-yield analysis and precision feeding. Automated milking, feeding and other operations reduce manual labour, improve consistency and lower operating costs. The architecture of smart dairy systems—including sensors, wireless communication and decision algorithms—is examined alongside environmental and economic benefits. Barriers include high initial investment, limited digital skills, poor rural connectivity and cybersecurity risks. Future developments may include AI-enabled autonomous dairy farms and interconnected, sustainable agricultural ecosystems.

Robotics is transforming livestock production by automating repetitive, labour-intensive and precision-dependent operations. This chapter examines automated milking, robotic feeding, manure removal, health monitoring, drones and autonomous herd-management systems across dairy, poultry, swine and sheep production. Integration with AI, IoT and sensors creates connected environments capable of continuous monitoring and data-driven action. Benefits include higher productivity, lower labour requirements, timely animal care and more efficient resource use. Challenges include substantial investment, technical complexity, maintenance and farmer acceptance. Emerging developments such as autonomous farms, swarm robotics, solar-powered equipment and climate-smart robotic systems may further strengthen animal welfare, environmental sustainability and global food security.

Healthcare organizations require collaborative learning methods that protect sensitive patient information. This chapter introduces federated learning as a framework for training models across decentralized institutions without transferring raw clinical data. Horizontal, vertical and hybrid federated architectures are examined alongside communication protocols, threat models and security requirements. Privacy-enhancing methods—including differential privacy, secure aggregation, cryptographic techniques and trusted execution environments—are discussed. Applications in medical imaging, electronic health-record analysis, wearable devices and remote patient monitoring demonstrate the framework’s practical value. The chapter also evaluates accuracy, privacy, communication cost, scalability and robustness while addressing data heterogeneity, fairness, explainability, legal compliance and ethical challenges in collaborative healthcare analytics.

Reliable artificial intelligence depends on accurate, timely and multidimensional livestock data. This chapter examines the transition from manual record-keeping to sensor-based systems that capture physiological, behavioural, production and environmental information. Data sources include wearable devices, IoT monitoring networks, computer-vision systems and environmental sensors. Their applications include monitoring health, welfare, movement, feeding and productivity. The chapter also considers the integration of heterogeneous datasets and persistent challenges involving missing values, noise, interoperability, scalability and system reliability. By connecting data generation with intelligent analysis, it emphasizes that robust acquisition protocols and dependable infrastructure are essential for developing effective, evidence-based and responsive livestock-management systems.

Cloud computing and Edge AI provide complementary infrastructure for intelligent livestock management. Cloud platforms offer scalable storage, centralized analytics, model training and integration of data from sensors, cameras, automated feeders and farm-information systems. Edge AI processes information close to the farm, reducing latency, bandwidth consumption and response time. This chapter explains their architectures, workflows and applications in dairy, poultry, swine and small-ruminant production. Uses include disease detection, estrus prediction, behavioural monitoring, feeding and environmental control. Hybrid cloud-edge systems balance centralized intelligence with local autonomy. Challenges involving connectivity, interoperability, cybersecurity, cost, model reliability and ethical animal surveillance are also critically examined.

It has been a general notion that if an experiment is conducted under identical conditions, values so obtained would be similar. But the experiences of people have dispelled this belief. Observations are always taken about a factor or character under study, which can take different values, and the factor or character is termed as variable.
Hence, we have a set of outcomes (sample points) of a random experiment. A rule that assigns a real number to each outcome (sample points) is called random variable. The rule is nothing about a function of the variable, say, X, that assigns a unique value to each sample point of the sample space.

8.1 Statistical Hypotheses

in attempting to reach decisions about the populations, it is useful to make assumptions or guesses about the populations involved. When the assumption or statement about a phenomenon occurring under certain conditions is formulated as proposition it is called a scientific hypotesis. We can construct criteria by which a scientific hypothesis may be ‘the  yield of a new paddy variety will be 3500 kg per hectare’. In statistical language it may be stated as ‘the random variable (yield of paddy) is distributed normally with mean 3500 kg/ha’. Such a statement or proposition about the probability distribution of a random variable is called the statistical hypothesis.

ABSTRACT

The Village Knowledge Centre programme was started in January 1998 in the Union Territory of Pondicherry (now Puducherry) in the east coast of South India with the support of IDRC and CIDA. In order to ensure access to all, the VKCs are located in public buildings (Panchayats, SHG’s building, Community Hall, School, Farmers and Fishermen Associations, Women Self-Help Groups, Community Based Organization, etc.,). The information content is developed in close interaction with the local people in a bottom-up manner.

Introduction

Saffron is a naturally derived plant product from the dried stigma of the Crocus sativus flower  (Gresta et al., 2009).The saffron (Crocus sativus L.) is an Iridaceae family member, herbal perennial, sterile (2n=3x=24) geophyte, traditionally propagated by vegetative corms. Its cultivation dates back to late Bronze Age in the Mediterranean region (Negbi, 1999) in Anatolia since the ancient times (Arslan et al., 2007) and for at least 3500 years in Egypt and Middle East. Saffron contains more than 150 volatile and aroma yielding compounds. The chemical components mainly include carotenoids, crocin, picrocrocin, safranal etc (Abdullaev, 1993, Tarantalis et al., 1995, Karaoglu 
et al., 2007). Saffron is mostly used as spice, food colorant, dye or perfume. The dried stigmas introduce unique color, taste and fragrance and thus making it the world’s most expensive spice (Baghlian et al., 2010). Saffron has been used in folklore herbal medicines for centuries (Basker and Negbi, 1983).

Chromosome number and nuclear DNA amount are important biodiversity characters together provide a very powerful method for determining evolutionary relationships. Furthermore provide an estimate of relatedness between species that in turn suggest reasons underlying the success or failure of crosses, even for interploidy hybridization. It is expected that species that are cytogenetically most similar will be easiest to hybridize and produce viable interspecific offspring (Thomas, 1993). Hence, it is important to study karyotypes and other characters of the genome of the species involved in a breeding programme.

According to Jackson (1971) and Stebbins (1950) the karyotype is the phenotypic appearance of the somatic chromosomes in contrast to their genic content.

Introduction

Microbes (bacteria, archea and eukarya) are single celled organisms that rule the planet. Microbes occupy every possible niche, from the high atmosphere to the deep ocean, from hot springs to artic ice, from hot deserts to deep forests including many regions that cannot yet be accessed for study. The biosphere contains between 1030 and 1031 microbial genomes, which is about 2-3 times more than the number of plant and animal cells combined (67). In fact, studies have shown that microbes associated with the human body are about ten times more than the number of human cells (5).  Microbes play a critical role in the life cycle of all living organism and environment in general.  Some microbes cause disease, but the most of them are either innocuous or play a role in physiology and cellular response including immune response, digestion and vitamin production of various organisms.

Since the dawn of the Industrial Revolution, mankind has been introducing numerous hazardous compounds into the environment at an exponential rate.  Environmental pollutants have become a major global concern. The modern growth of industrialization, urbanization, modern agricultural development, energy generation, have resulted in indiscriminate exploitation of natural resources for fulfilling the human desires and need, which have contributed in disturbing the ecological balance on which the quality of our environment depends.
 
Technological revolution has brought new changes in products and processes in industry. The waste generated from the development of products and processes are of concern to the environmental list. A variety of pollutants such as heavy metals, xenobiotics, polycyclic aromatic hydrocarbons (PAHs), chlorinated and nitro-aromatic compounds are found to be highly toxic, mutagenic and carcinogenic for living organisms (Zhang and Bennett. 2005; Samanta et al., 2002).

Introduction
DNA barcoding is a molecular technique that uses a short section of DNA (genetic marker) extracted from a standardized region of tissue to identify it as belonging to a particular species. A desirable locus for DNA barcoding should be standardized and that should present in most of the taxa of interest and sequencable without species-specific PCR primers. A large database of sequences for the desirable locus needs to be developed. The desirable locus should be short enough to be easily sequenced with current technology, and provide a large variation between species yet a relatively small amount of variation within a species. Although several loci have been suggested, but the mitochondrial Cox1 became a gene of choice for animals and many other eukaryotes. However, the DNA regions that are being used are chosen to optimize the resolution for their specific experimental purposes.

Technology transfer is an important commercial tool for leveraging and optimizing the value of intangible assets in the present era of globalization which has deepen the process of integration of the economic resources including the free movement of goods, merchandise, services, capital and intellectual property rights. This economic integration is facilitated by a legal framework of World Trade Organization (WTO) under various agreements including the Trade Related Aspects of Intellectual Property Rights (TRIPS). There are other International Institutions i.e., World Intellectual Property Organization (WIPO), World Customs Organizations (WCO) and United Nations Conference on Trade and Development (UNCTAD) to regulate and provide guidance for the protection and enforcement of IPRs. The present global economy is geared and triggered by knowledge and almost 80 % of the global wealth comes from knowledge based intangible assets and helps optimization of the limited global resources. Technology transfer is a tool for leveraging the value of IPRs and also for sharing knowledge around the world. Knowledge is weightless and in today’s era, knowledge is driven by Information and Communication Technology (ICT) which adds to productivity in all aspects of economic life.

The term “Technology transfer” broadly connotes transfer of knowledge from one legal entity to another legal entity within and outside the national territories of the legal owner of the Patent holder at a consideration, known as license fees or technical fees or royalty. This may happen as a part of Foreign Direct Investment (FDI) or as a part of pure technological collaboration. In other words, Transfer of Technology (TOT) and Technology Commercialization allows the transfer of the technology by a patent holder to a user for further development and exploitation of the technology into new products, processes, applications, material or services. Patent provides an exclusive right to the patentee to exclude others from using an invention without his consent and the technology transfer is the process to include the licensee as the legal user without infringing the rights of the patentee. The exclusive right granted to a patentee is the right to prevent others from making, using, selling, or distributing the patented invention without his permission. The technology transfer allows the user to use the patented technology with the consent of the patent holder.