eChapters

1. Introduction

There is an emergence of the concept of a Circular Economy as one of the transformational approaches toward sustainability, where greater emphasis is laid on rethinking and redesigning products and services for maximum resource efficiency. In contrast to the traditional linear model ("take, make, dispose"), CE emphasizes product reuse, recycling, and cascading with the clear ambition of closing the loop of product lifecycles through continuous recovery of resources. The key to this solution involves changing the paradigm, highly relevant to resolving pressing environmental issues facing the planet such as resource depletion, waste management, and climate change.

Conventionally regarded as information stores, libraries are increasing awareness of their potential to create an impact toward sustainable practices. Centres of information and meeting points for communities, libraries are at an advantageous position for leading by example in integrating the principles of CE into their system. This paper examines the current status of integrating the circular economy principles within library management and discusses a proposed conceptual framework that aims to guide libraries in adopting circular practices. The paper addresses the contribution that libraries might make to a more sustainable future by exploring the intersection between CE and library management through innovative practices and strategic plans.

I. PUBLIC POLICY CONCERNS

The working of the Trade Related Aspects of Intellectual Property Rights Agreement (TRIPS) Agreement, 1995 along with World Intellectual Property Organization (WIPO) and the jurisprudence developed for the last twenty years is a testimony to the fact that although TRIPS is geared to promote the protection of intellectual property rights internationally, yet some controversial public policy issues need to be addressed and solutions devised for addressing these public policy issues otherwise there is a possibility of seeing the demise of TRIPS Agreement or sidetracking it by some other methods which may not be conducive for maximizing the welfare of the international trading community. As all of us know that intellectual property and international trade are mutually inclusive and the minimum standards set forth in the TRIPS Agreement are economically value oriented. The trade offs among and between the members of the WTO need to be balanced on an even keel, otherwise the possibility of camaflouging the TRIPS Agreement and the attendant jurisprudence becomes real. The TRIPS Agreement addresses seven categories of IP rights such as; Copyright and related rights; Patents; Trademarks and Service marks; Geographical Indications; Undisclosed Information or Trade Secrets; Industrial Designs ; and Layout Designs of Integrated Circuits and read with other WIPO Treaties obligates the members of the World Trade Organization (WTO) to reduce distortions and impediments to international trade by taking into account the effective and adequate protection of intellectual property rights as well as the procedures for the enforcement should not become barriers to international trade. The TRIPS Agreement also recognizes the need to cater to the special needs of the developing and least developed countries.

Intellectual property (IP) encompasses creations of the human intellect, including inventions, artistic and literary works, as well as symbols, names, and designs. Intellectual property rights (IPR) are legal mechanisms thaprovide creators with exclusive rights to use, manage, and profit from their creations. These rights help protect the interests of innovators and promote continued creativity and innovation.
Intellectual Property Rights refer to the legal entitlements granted to individuals or organizations over their creative and intellectual works, such as patents, trademarks, and copyrights. These rights grant exclusive control or usage privileges for a specific duration, often creating a temporary monopoly. While the concept of intellectual property began to take shape in the 19th century, it was only during the 20th century that it was formally integrated into global legal frameworks.
Intellectual Property Rights (IPR) safeguard the creations of the human mind, such as inventions, artistic and literary works, brand names, logos, designs, and images used in commerce. These rights help foster innovation by allowing creators and inventors to benefit economically from their work.
Key Points
1. Intellectual Property Rights are legal protections acquired by creators or owners of intellectual works.
2. Intellectual property encompasses intangible assets that result from human creativity and thought.

Introduction
 
Intellectual property is a cluster of legally recognized rights associated with innovation and creativity, the works of the mind, as against physical products, land and other tangible resources. Even though it is intangible, intellectual property (IP) is often recognized as personal property, to be sold and traded like other forms of property. Under intellectual property law, owners are granted certain exclusive rights to various markets, machines, musical, literary, and artistic works; discoveries and inventions; and applications. But national laws normally don’t define ‘intellectual property’ as a single property right. Instead, ‘intellectual property’ is used as a convenient general term describing a range of distinct legal rights, relating to literary, artistic and scientific works; performances of performing artists; phonograms, and broadcasts; inventions in all fields of human endeavour; scientific discoveries; industrial designs; trademarks, service marks, and commercial names and designations; protection against unfair competition and all other rights resulting from intellectual activity in the industrial, scientific, literary or artistic fields.

Clinical decision support systems assist healthcare professionals in diagnosis, treatment planning and patient management. This chapter introduces intelligent clinical decision support systems and explains how computational intelligence overcomes the limitations of conventional rule-based approaches. It examines artificial neural networks, fuzzy logic, evolutionary algorithms, swarm intelligence and deep-learning models. A computational architecture integrating clinical data, learning algorithms, fuzzy inference and optimization is presented. Applications in heart-disease diagnosis, diabetes-risk prediction and medical-image analysis illustrate the practical value of these systems. The chapter also addresses interpretability, interoperability, data quality, privacy, legal responsibilities, real-time processing and the challenges involved in deploying intelligent decision-support tools in clinical environments.

Early and accurate crop-disease diagnosis is essential for preventing production losses and reducing unnecessary pesticide use. Manual diagnosis can be slow, subjective and difficult to apply across large agricultural areas. This chapter introduces computational approaches using artificial neural networks, fuzzy logic, support vector machines, deep learning and hybrid optimization models. It discusses image acquisition, preprocessing, feature extraction, pattern recognition and classification of disease symptoms from leaves, crop canopies and sensor data. Integration with precision-agriculture systems, mobile advisory applications and real-time monitoring enables timely intervention. Challenges involving data quality, field variability, model interpretation and large-scale deployment are examined through practical examples and case studies.

Decision Support Systems convert farm data into recommendations that improve livestock productivity, health and sustainability. This chapter explains data-driven, model-driven and knowledge-based systems and their principal components: data management, analytical models and user interfaces. Technologies such as IoT, artificial intelligence, machine learning and cloud computing support applications in feed optimization, disease prediction, reproductive management, milk production and animal-welfare monitoring. Dairy and poultry examples demonstrate how DSS can reduce operational costs and strengthen farm decisions. Implementation challenges include inadequate data quality, high costs, limited technical expertise and maintenance requirements. Future systems are expected to integrate blockchain, robotics, Edge AI and increasingly autonomous management technologies.

Computational crop-disease detection uses machine learning, neural networks and image-processing techniques to provide timely and accurate diagnoses. This chapter presents a complete framework covering agricultural intelligence, disease categories, image acquisition, preprocessing, model training and classification. It examines datasets collected in fields or through drones and considers deployment through mobile devices, cloud platforms and other digital systems. Evaluation measures are introduced for assessing model accuracy and reliability. The chapter also considers limitations caused by insufficient or poor-quality data and variation in field conditions. Future directions include multimodal data integration and explainable models that provide transparent recommendations for sustainable crop protection and improved food security.

Abstract

Describes the genesis, organization and Internet representation of International Patent Classification (IPC). Provides the outline of seventh edition and useful URLs related to it.

Keywords: International Patent Classification, Patent Classification, Classification, Internet representation

Introduction

The International Patent Classification, commonly known as the IPC, is based on an international multi-lateral treaty administered by World Intellectual Property Organization (WIPO). This treaty is called the Strasbourg Agreement concerning the IPC, which was concluded in 1971 and came into force on October 7, 1975. The Agreement aimed to overcome the difficulties caused by using diverse national patent classification system. The common classification system defined by the Agreement is applied to patent and utility model documents. The Agreement is open to States party to the Paris Convention for the Protection of Industrial Property.

The integration of Internet of Things technologies with artificial intelligence is creating increasingly connected and data-driven agricultural systems. This chapter examines how IoT and AI can support smart farming through continuous data collection, analysis and decision-making. It discusses technological infrastructure for advanced agriculture and improvements in field operations enabled by connected sensors and intelligent systems. Applications in precision farming, including orchard and vineyard management, are considered alongside emerging technologies and high-throughput phenotyping. IoT devices can provide information about environmental and crop conditions, while AI methods transform these data into useful management insights. The chapter concludes by examining future directions and research challenges associated with integrated smart farming systems.

The Internet of Things supports continuous monitoring of soil, crops, weather and other field conditions through distributed sensors and connected devices. This chapter proposes an edge-cloud architecture designed to address scalability, heterogeneous data, communication latency and energy requirements. Edge nodes conduct local processing and anomaly detection, while cloud platforms provide storage and run advanced machine-learning models. Applications include crop-status evaluation, irrigation scheduling, pest and disease detection and yield forecasting. Explainable artificial intelligence improves transparency by helping users understand model recommendations. The integrated architecture can reduce response time compared with conventional centralized systems and provides a foundation for scalable, efficient and responsive next-generation smart-farming operations.

Internet of Things (IoT) is networking of physical objects, devices, machines, systems etc to interconnect each other for exchanging data through standard internet protocols. The IoT is also termed as Machine-to-Machine (M-to-M) communication. The term ‘Things’ in IoT includes objects embedded with sensors, actuators, mobile phones, routers, hubs, computers, wearable’s and smart devices etc.

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.

ABSTRACT
  
The present study was conducted with 100 internet users in Lahowal CIC and Barbaruah CIC in Dibrugarh district of Assam to critically analyze the various aspects of internet utilization pattern. Results showed that majority (66%) of the respondents were between 18 to 26 years of age and were students (50%). Education qualification of majority of the respondents (32%) was above seventh standard and below graduation and seventy per cent were above graduation and below post graduation and also undergone training in computer.

11.1 Concept of Internet

The internet is not a computer network. It is a network of networks, linking computer to computers. Each runs software to provide access and view information.

In other words, internet is the connection of two or more networks so that a computer on one network can communicate with a computer on another network. The communication between two computers takes place such that the user does not have to worry about the technology used by the networks. The router, also called gateway, acts as an interface between two networks. The internet covers the entire globe and includes large, international networks as well as many smaller, local area networks (LANs). A user can access data, graphics, sound, software, text and people through a variety of services and tools for communication and data exchange. To get, provide and compile information, internet is the cheapest and fastest medium. Some of the popular tools and services available for communication and data exchange are as follows:

Animal husbandry faces increasing pressure to produce safe milk, meat and eggs while improving animal welfare and reducing environmental impacts. Traditional observation-based management is increasingly inadequate for large and complex livestock systems. This chapter introduces digital transformation through Precision Livestock Farming, which combines IoT devices, smart sensors, wearables, computer vision, artificial intelligence, machine learning and digital twins. Continuous animal-centred data enables timely decisions concerning health, reproduction, feeding, behaviour and welfare. The chapter explains how digital technologies can support early disease detection, efficient resource use and improved productivity across commercial and smallholder farms, creating more profitable, sustainable and welfare-conscious livestock systems.

Computational intelligence is enabling agriculture to evolve into a data-driven, adaptive and increasingly autonomous system. This chapter introduces its concepts, scope and role in smart agriculture by presenting farms as interconnected cyber-physical-biological environments. It discusses artificial intelligence, data analytics and decision-support systems and explains the progression from traditional farming to precision and smart agriculture. IoT, remote sensing, cloud platforms and digital agricultural ecosystems are examined as enabling technologies. The chapter also considers socioeconomic, environmental and scalability challenges. It establishes the conceptual and architectural foundation required to understand how computational intelligence can improve productivity, sustainability, resilience and decision-making in next-generation agricultural systems.

1.1 Introduction

The word ‘computer’comes from the word ‘compute’, which means to calculate or perform a sequence of operations. Hence, a computer is normally considered to be a calculating device, which can perform arithmetic operations at enormous speed.

In other words, a computer takes information (or input), processes it according to a set of instructions (or process), and gives back a result (or output). In this respect, it is very similar to a calculator, but obviously somewhat more complex.

A computer is a multi-function platform. The same machine can perform many different tasks by using different programs. The programs (also termed as applications) are written to do everything imaginable. In other words, we can say that a program is a set of instructions issued to a computer in order to get the desired output.

Definition

A Computer can defined as an electronic device that takes input from its user, stores, processes data and generates the required output as per the processing instructions given to it by the user.

To understand IoT “Internet of Things” it is important to understand internet first. Internet is connecting a device to other device anywhere in the world. When two devices are connected with the Internet, they can send and receive all kinds of information such as text, graphics, voice, and video. The high-speed, fiber-optic cables are used, through which the bulk of the Internet data travels. The Internet has revolutionized the communication world. In today’s scenario the internet is an important part for people around the world. The Internet is a global network of billions of electronic devices. The World Wide Web (www) is most widely used part of the Internet.

The “Internet of Things is a potentially integrated part of the ‘Future Internet’. IoT can be defined as a dynamic global network with self - configuring capabilities based on communication protocols where physical and virtual ‘Things’ interact with each other.

The Internet of Things (IoT) is inter-connecting the networks of smart devices. Smart devices may be embedded with vehicles, buildings, or other electronics object with software, sensors, actuators, and network connectivity. Network connectivity enables smart devices to collect and exchange data. The Internet of Things (IoT), also referred to as the Internet of Everything (IoE) which comprises of the web-enabled devices. IoT has its applications in smart city, smart surveillance, automated transportation, smarter energy management systems, water distribution, urban security and environmental monitoring. Agriculture is another new area of IoT application.

Due to advancement in technology the processing power and storage capacity has been increased, in the recent years. At the same time technology is making the devices pervasive, wearable and mobile. Smart devices are fitted with sensors, actuators which enable devices to sense, compute and act intelligently. They are able to exchange information with environment.