eChapters

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.

15.1 Sample Objective Question

1. Which one is related to deposition of Micro-organism?
   1. Madrid Agreement
   2. Berne Convention
   3. Budapest Treaty
   4. Patent Cooperation Treaty.
2. WTO was officially commenced in the year...........
   1. 1970
   2. 2003
   3. 2005
   4. 1995
3. Which one is related to protection of Trademarks
   1. Madrid Agreement
   2. Berne Convention
   3. Budapest treaty
   4. None of these
4. One of the four patent office is...........
   1. Patna
   2. Banglore
   3. Varanasi
   4. Mumbai

This chapter describes various multivariate techniques which are useful in animal breeding. The genetic correlation with multiple response variables is already explained in the chapters on Correlation and Genetic Parameters. The other techniques including MANOVA, Principal Component Analysis, Factor Analysis, Cluster Analysis and Discriminant Analysis are explained.

Multivariate analysis is referred to a set of data with multiple response variables in general and such data is encountered often in animal breeding analysis. The simplest form of multivariate analysis is the genetic correlation where two or more traits are analysed together as response variables with factors affecting them on the right-hand side (RHS) and details of these are already explained in earlier chapters.

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.

2.1 Definition and Concept

An operating system (abbreviated as ‘OS’) is the program that, manages all the other programs in a computer. Every general-purpose computer must have an operating system. It provides support for the loading and execution of other programs which are known as Application Programs.

5.1 Introduction

According to Croxton and Cowden, “Either for one’s own use or for the use of other, the data must be presented in some suitable form”.

Now – a – days everybody uses tables in one form or another, and accountants use them quite often. The work become tedious, especially if you had to do for 12 columns for the 12 months of the year, and more so if there were some modification to be done after having set up the entire table. The Americans have a special name for such tables- spreadsheet which makes life easy.

Steve Jobs and Steve Wozniak two young Americans in 1976 AD developed the first desktop computer – The Apple which was not user friendly and needed programming for useful work. Dan Bricklin, Robert Frankston and Dan Fylstra proposed a program to turn the computer’s memory into a giant virtual spreadsheet where necessary corrections and modifications could be done fairly, quickly and easily and marketed the program in 1978 under the trade name VisiCalc. VisiCalc was soon succeeded by Sugercal and other computer programs which were more user friendly. The Lotus 1-2-3 designed and developed by Larry Kaplan put further step, which set the standard for many applications under the IBM, Now a days Microsoft under the leadership of Bill Gates had been working on the sidelines perfecting graphical user Interface (GUI) packages for the PC range of computers that would widen its reach. Now Microsoft office program Suite the integrated word processing, spreadsheet, presentation and data base management.

The spreadsheet package in Microsoft office is called Excel – our basic software, or program for this manual.

GPS stands for Global Positioning System, basically Navstar GPS is a radio based space aviation system developed by United state government which was operated by United State Air Force. It provides geographic location and time related information to GPS receivers under global navigation satellite from anywhere of earth where unobstructed line of sight to 4 or more GPS satellite co-ordinate (Anonymous, 2018). Even GPS not required any user for transmitting data, with working independently on any telephonic or internet receivers; through these technologies we can enhance usefulness of the GPS location based information. The GPS serves as critical positioning capacity to various fields like army operation, civil aviation, and commercial scope to wider scope in around world.

Solution:

Steps used for plotting the bar graph are as:

Step 1: Open Excel sheet and feed the above data as given in figure. 9.1.

Step 2: Select data and go to insert menu and select column chart. (figure 9.2)

Step 3: Select 2-D column, option first from dialog Box (figure 9.3).

Step 4: The desired output will be as shown (figure 9.4).

Remark The data labels, axis name, title of the graph etc. can be inserted through selecting option formatting chart area and add data labels by right clicking on the graph output (figure 9.5).

SQL, or Structured Query Language, is a fundamental component in the domains of Machine Learning and Artificial Intelligence, serving as a powerful tool for managing, retrieving, and manipulating data stored in relational databases. In an era where data drives intelligent systems, SQL plays a crucial role in preparing and organizing datasets that form the backbone of machine learning models and AI applications. Its ability to efficiently handle large volumes of structured data makes it indispensable for data scientists, analysts, and engineers working in these fields. Originally developed in the 1970s at IBM by researchers Donald D. Chamberlin and Raymond F. Boyce, SQL was designed to provide a standardized way to interact with relational databases. Over time,

Data cleaning is one of the most important parts of machine learning. It plays an important role in building a machine learning model. Data quality is a significant aspect to train the ML model. Inaccurate data can have an impact on results. Data quality problems can occur anywhere in information systems. A technique that helps to convert improper data into meaningful data. Machine Learning is datadriven. With the data cleaning techniques, your Machine Learning model will perform better. So, it is important to process data before use. Without quality data, it is unwise to expect a correct output. Data cleaning refers to identifying and correcting errors in the dataset that may negatively impact a predictive model. It is used to refer to all kinds of tasks and activities to detect and repair errors in the data. These problems can be solved by using various data cleaning techniques. The process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. When combining multiple data

The growing integration of Large Language Models into healthcare, education, business and cybersecurity has created new opportunities while introducing significant security and privacy risks. This chapter provides a comprehensive examination of vulnerabilities across the LLM lifecycle, from training and inference to deployment and integration with external applications. It discusses threats including prompt injection, jailbreak attacks, model extraction, membership inference, privacy-attribute inference and adversarial prompting. Deployment environments introduce additional risks involving unauthorized API access, misconfiguration, insecure integrations, logging-related data leakage and supply-chain attacks. The chapter also considers availability and integrity threats, including denial-of-service attacks and attempts to manipulate or remove AI-generated content watermarks. Privacy protection mechanisms such as differential privacy, anonymization, de-identification and encryption are examined as potential safeguards. Legal and regulatory considerations are also addressed, including data protection requirements and organizational governance. Because LLM applications may process sensitive personal and business information, security cannot be treated as an isolated technical feature. The chapter emphasizes continuous monitoring, secure architecture, privacy-preserving techniques, access controls and responsible governance for trustworthy LLM deployment.

The rapid adoption of Large Language Models requires organizations to manage not only model performance but also ethical, regulatory, operational and lifecycle risks. This chapter presents an integrated perspective on governance, optimization and lifecycle management of LLMs. It examines governance principles based on fairness, accountability, transparency, human-centered design and responsible AI. Regulatory developments and policy gaps are considered alongside the challenges of auditing opaque models, controlling hallucinations and establishing accountability across organizations and jurisdictions. The chapter also explores optimization methods such as pruning, quantization, model compression, instruction tuning, LoRA and QLoRA to improve efficiency while maintaining useful capabilities. Lifecycle management extends from data collection and model design through training, evaluation, deployment, monitoring, updating and eventual decommissioning. Real-world cases demonstrate the importance of continuous testing, feedback, security controls and governance throughout the model lifecycle. Emerging threats, including prompt injection, data poisoning, misinformation, privacy leakage and malicious use, further emphasize the need for coordinated oversight. The chapter ultimately advocates a lifecycle-based governance framework combining technical optimization, regulatory compliance, ethical safeguards and continuous monitoring

The main purpose of this lecture note is to provide basic information on patent drafting skills and train applicants for drafting a patent in clear and concise manner.

We will see the components of Complete Specification through the following Case studey (1) of a solar water heater. The figures are provided below for reference and better understanding.

A Patent is a written document explaining an invention with exclusive right. It is used to protect an inventor/scientist and to prevent other from making profits without permission. It is also used as an authority paper permitting license holders to duplicate the results. A patent application requires a combination of legal documentation and technical papers. This issue makes patent writing a very different writing style.

In India, patent applications can be filed by a patent agent or an individual having some scientific and technical background to understand the invention. However, no one can understand the invention better than the inventors themselves. Document preparation requires healthy discussions between the inventor and agent. For inventors, a good understanding on patent writing style can greatly facilitate the documentation process. Before filing the patent application inventor must be sure about that the subject is eligible for patent protection, the invention has something new, the invention is not obvious and is not easily predictable from general consensus, and the invention has practical application.

Introduction

Geomorphology is the study of earth’s landscapes over time and space, and involves an analysis of both its surface features as well as the processes responsible. Historically, the subject evolved through monumental studies on supposed evolution of the landscape, based on intuitive knowledge of the researchers on landscape response to endogenic and exogenic forces, as well as visions of possible changes over a time scale of centuries to millennia or more. Too much emphasis on the study of possible landscape evolution in an era of little technological back-up facilities meant that the discourses were largely philosophical in nature, with fewer measurable parameters to substantiate the concepts or the reasoning, although reflecting deep understanding of the researchers involved. As older concepts began to be confronted with newer ones, and applied aspects started receiving greater attention, a need was felt for quantification of the features and processes. This was more so for an understanding of the fluvial landscape, although aeolian and other process-based studies also gradually incorporated quantitative analysis as a means of understanding the landscape. Despite the awareness, lack of adequate equipment hindered the progress of quantification in the pre-World War II period.

Analytical testing provides objective evidence of the composition, safety, physical structure, and sensory quality of ice cream. This chapter introduces the major analytical approaches used in industrial ice cream manufacturing, including chemical, physical, microbiological, and sensory evaluation. Important parameters such as fat, milk solids-not-fat, protein, sugars, ash, acidity, pH, overrun, meltdown, fat destabilization, texture, and product weight are discussed. The chapter explains how different tests support routine quality control, product development, troubleshooting, research, regulatory compliance, and shelf-life studies. It emphasizes that no single analytical parameter can define overall quality. Effective product evaluation requires integrated interpretation of chemical, physical, sensory, microbiological, and storage data.

In an era defined by rapid technological advancements and an ever-changing climate, the need for precise, adaptive, and actionable weather intelligence has never been greater. As global weather patterns become more erratic and extreme events increasingly disrupt lives and livelihoods, the integration of real-time field response into weather intelligence systems represents a transformative opportunity. Just as companies like Google and Tesla leverage real-time user inputs to continuously update their software, refine artificial intelligence (AI) models, and provide timely road-and-traffic guidance, weather intelligence systems that are continuously refined by feedback from farmers, fishermen, and other local stakeholders offer the promise of dynamic, “live” insights that can revolutionize agricultural practices and community resilience (Fig.10.1). This chapter delves into the critical importance of collecting and synthesizing feedback from those who are on the frontlines of weather impacts. It explores the methodologies and benefits of integrating real-time field data into weather intelligence systems, drawing parallels to the iterative processes observed in hightech industries. In doing so, we highlight how these innovations can maximize

Cotton is often called as “white gold”. True to its name cotton supports the largest agro based industries in India and cotton industry ranks first in the agro based industries and engages 4 to 5 million people in cotton growing. The industrial sector employs 1.0 million people. At present there are 3660 ginning and 1570 textile mills in India. India earns foreign exchange to a tune of Rs. 2500 million through exports of cotton textiles and products. Cotton is cultivated for ‘lint’ which is used for preparing threads, yarns and medical cotton mixed with synthetic fibre and for various other purposes.

I. INTRODUCTION

Over the last few decades, protection of intellectual property rights (IPRs) has become a global issue. The existing international intellectual property rights regime has been governed by the Trade- Related Aspects of Intellectual Property Rights (TRIPS) Agreement.1 Inspite of maturity of intellectual property laws in post TRIPS era it still desiderate to established coherence with human rights laws particularly right to health and maintain a distance with the socio-economic dimension of intellectual property protection. On the other hand the international human rights instruments from the very beginning appear to be considerable to make certain links with intellectual property rights.2 The issue that is now debated that the adoption of the TRIPS Agreement provisions have serious impact on the realization of certain basic human rights such as the right to health because of non availability and affordability of much needed medicines for the poorer populations of the world particularly in majority of developing countries. Meanwhile TRIPS flexibilities as confirmed by the Doha Declaration on Public Health were adopted in 2001 by the global community to address medicines as public health issue.3 Keeping in view the above developments the problem of right to health and the relationship between medical patents and access to medicine in the case of the HIV/ AIDS and T.B epidemics is complex challenge in the context of the 2030 Agenda for Sustainable Development Goals in 2015 adopted by Member States of the United Nations in Sep, 2015. One of the aims of the agenda is to ensure healthy lives and promote the well-being of people of ages.4 In such a situation it is high time to revisit the existing intellectual property regime in the broader context to protect everyone who is likely to be negatively affected by intellectual property standards imposed by developed nations. Under this backdrop the present paper wishes to examine the various kinds of potential impacts of intellectual property regime on the realization of access to medicine as part of human right to health as universal entitlements amongst the most disadvantaged and marginalized peoples and communities in the broader context of sustainable development goals.

5.1. Research methodology

Research methodology is a thorough investigation plan and the manual for conducting the research. This chapter’s examination of methodology aims to help readers comprehend the ideas, strategies, and tactics used to plan a study, gather data, analyse it, and interpret it in order to reveal reality and develop theories. This chapter has been classified under various sub headings for clear understanding:

(i) Locale of research.

 (ii) Pilot study.

 (iii) Methods of sampling.

 (iv) Variables and their empirical measurement.

 (v) Preparation of the interview schedule.

 (vi) Tools and techniques of data collection.

 (vii) Statistical analysis and interpretation of data.