eChapters

Climate change affects livestock through heat stress, water scarcity, unstable feed supplies, altered disease patterns and extreme weather. This chapter examines how artificial intelligence supports climate-smart and sustainable animal husbandry. Machine learning, predictive analytics and IoT-enabled monitoring can optimize feeding, detect disease, assess welfare and improve the use of water, energy and other resources. Intelligent technologies can also assist in reducing greenhouse-gas emissions and developing adaptive production strategies. The chapter evaluates the contribution of AI to productivity, climate resilience and environmental responsibility while addressing problems such as insufficient data, limited infrastructure, implementation costs and ethical concerns. Future integration with sustainable agricultural frameworks is also discussed.

Genetic improvement creates permanent, heritable gains in milk production, growth, fertility, disease resistance and environmental adaptation. This chapter explains how artificial intelligence complements conventional phenotypic and pedigree-based selection by analysing large genomic and performance datasets. Machine-learning models reveal genotype-phenotype relationships, predict breeding values and support earlier, more reliable selection. Applications include genomic selection, precision breeding, reproductive technologies, bioinformatics and digital twins. AI can reduce generation intervals and improve the efficiency of breeding programmes, but its success depends on data quality, representative populations and interpretable models. Ethical concerns, genetic diversity and long-term sustainability must also be considered when designing AI-supported breeding systems.

Genetic improvement creates permanent, heritable gains in milk production, growth, fertility, disease resistance and environmental adaptation. This chapter explains how artificial intelligence complements conventional phenotypic and pedigree-based selection by analysing large genomic and performance datasets. Machine-learning models reveal genotype-phenotype relationships, predict breeding values and support earlier, more reliable selection. Applications include genomic selection, precision breeding, reproductive technologies, bioinformatics and digital twins. AI can reduce generation intervals and improve the efficiency of breeding programmes, but its success depends on data quality, representative populations and interpretable models. Ethical concerns, genetic diversity and long-term sustainability must also be considered when designing AI-supported breeding systems.

Artificial Intelligence (AI) is technology which can store, retrieve, manipulate, manufacture, transmit or receive, analyze any digital information and can apply machine vision for increasing efficiency and workability of the system. The AI permits systematically scope of ‘3S’ i.e. Safety, Security and Surveillance, which is also known as manmade intelligence. In fact, it needs more reliable and accurate data or information for processing and correction for predicting performance of any system and devices.

Introduction

Avian medicine is a rapidly growing field, with an increasing number of exotic bird species being kept as pets. However, the unique anatomy, physiology, and behaviour of these birds present several challenges as well as new opportunities for veterinarians and researchers.Now a day’s birds are gaining popularity over dogs and cats because of various reasons listed below:

a. They are more suited in present life style as can be suited well in small spaces.

b. They require low maintenance with relative low cost of maintenance and live longer life.

c. They form very strong bonds with their owners.

Birds can be classified into different families, such as Psittaciformes, Passeriformes, Columbiformes, Piciformes, Anseriformes, Galliformes etc. containing numerous species (>10,000). The first documentation of parrot as a pet was found in Rig Veda, written more than 3000 years ago. Evidence of bird keeping is observed in ancient civilizations of Greece, Rome, China and Egypt. In Greek mythology, Raven (a crow) was mentioned as a symbol of good luck and was associated with God Apollo (God of prophecy). Crows are also considered as ancestors in Hindu mythology and food (pinda) is offered to crows during ritual of paying homage to dead persons. In most of the countries psittacine and passerine birds are preferred as pet birds.

1.1 Introduction

Python is an interpreter, high level, general purpose programming language developed by Guido Van Rossum and its first version was released in 1991. There are two major Python versions: Python 2 and Python 3. Both are quite different.

1.2 Beginning with Python Programming

1.2.1 Finding an Interpreter

Before we start Python programming, we need to have an interpreter to interpret and run our programs.

Windows: There are many interpreters available freely to run Python scripts like IDLE (Integrated Development Environment) that comes bundled with the Python software downloaded from http://python.org/.

Linux: Python comes preinstalled with popular Linux distros such as Ubuntu and Fedora. To check which version of Python you’re running, type “python” in the terminal emulator. The interpreter should start and print the version number.

macOS: Generally, Python 2.7 comes bundled with macOS. You’ll have to manually install Python 3 from http://python.org/.

Abstract

The Basic Local Alignment Search Tool, or BLAST, is an online tool provided by the National Center for Biotechnology Information (NCBI) that allows users to search for matches between their query sequences and the sequences stored in NCBI databases. BLAST can be used to search for nucleotide sequences against nucleotide sequences, protein sequences against protein sequences, and protein sequences against translated nucleotide sequences in the databases. BLAST is widely used to study sequence similarity, identify target sequences, analyze the functional aspects of molecular sequences, and investigate evolutionary relationships. This chapter details on the BLAST and its applications.

In this chapter, the essentials of basic statistics are explored, with a focus on obtaining all key measures of basic statistics with just a single click. Additionally, the process of creating informative data distribution graphs is also explained.

Basic Statistics

Basic statistics offer fundamental tools that help us to make sense of data. Below, you’ll find concise descriptions of key basic statistical measures.

i. Arithematic mean (average)

The average represents the central value of a dataset, calculated by summing all values and dividing by the number of data points.

ii. Median (middle value)

It is the middle value in a dataset when all values are arranged in ascending order. It’s less sensitive to outliers.

iii. Mode (most common value)

The mode is the value that appears most frequently in a dataset.

iv. Variance

Variance measures how much individual data points deviate from the mean. A higher variance indicates a greater spread.

v. Standard deviation

The standard deviation is the square root of the variance. It provides a measure of how spread out the data is.

vi. Range

It is the difference between the maximum and minimum values in a dataset.

M. D. SURANAGI

Software is a tool that allows the hardware to process and manipulate data. It is essentially a list of commands; therefore it is not something you can touch. Software is stored in the different drives in a computer. Software, like data, can be stored and shared through disks. Unlike hardware, software is not permanent. It can be added, removed, and changed very easily. Individual software pieces are normally called programs. There are different types of programs. The three major categories of software are System software, Application software and Programming software.

• System software like device drivers and utilities enables the application software to interact with the computer hardware.
• Programming software like compilers, debuggers and interpreters provides tools to assist a programmer in writing computer programs.
• Application software is what most users commonly use to interact with their computers and carry out various daily tasks.

It is the most significant international convention in the field of protecting copyright. The Berne Convention deals with the protection of works and the rights of their authors. It is based on three basic principles and contains a series of provisions determining the minimum protection to be granted, as well as special provisions available to developing countries that want to make use of them. The Berne Convention, signed on 9 September, 1886, was revised at Paris in 1896 and at Berlin in 1908, completed at Berne (Switzerland) in 1914, revised at Rome in 1928, at Brussels in 1948, at Stockholm in 1967 and at Paris in 1971, and was amended in 1979.

Revolutionizing skin cancer detection
The diagnostic process for skin cancer is evolving. Physicians have in the past relied upon physical exams to be followed by scanning and biopsies taking anywhere between 15 days to return results. Although most abnormalities turn out to be non-cancerous, this is distressing to patients and contributes to healthcare costs. Additionally, certain growths that turn out to be cancerous remain undiagnosed since they have not been visible.
Breakthrough startup Damae Medical is set to reverse this process. The startup has developed an advanced probe that is able to take 3D images of the skin to unveil the full extent of abnormalities in real time. This enables doctors to diagnose in an instant, enabling improved personalized treatment. When an operation is required only diseased tissue is removed to prevent unnecessary treatments and improved outcomes in patients.
The Origins in the Institut d’Optique
The beginnings of Damae Medical can be traced to Palaiseau-based Institut d’Optique where in 2013 two photonics students envisaged potential in an emerging imaging tech. Together with the inventor himself in 2014, they cofounded Damae Medical by taking an exclusive license to the core family of patents held by three partner institutions. While this contract was beneficial in launching the company to an early growth spurt, in the long run created difficulty in exercising control. This was resolved by taking an equity-forcontrol purchase in 2019 by Damae to acquire the foundational patent

1. Introduction

The Online Dictionary of Library and Information Science (ODLIS) defines best practice as, “in the application of theory to real-life situations, procedures that, when properly, applied consistently yield superior results and are therefore use as reference points in evaluation of the effectiveness of alternative methods of accomplishing the same task. Best practices are identified by examining empirical evidence of success”. Commonly, best practices are those services, which satisfy the respective users’ community on their desired level. Earlier, best practice term was used in the manufacturing industries for implementation of benchmarking methods so that ultimate quality results may be achieved. But later on the term “best practice” has also been used in academia and other fields as well. The mandate of any library is to work according to the informational needs of the user’s and other facilities, which are desired by the users. The service delivery pattern has totally been changed gradually due to the technological impacts and ever changing needs of the users. Now library users’ wants quality information services, which satisfy their needs so it becomes necessary for any library to provide the satisfactory services in the best and desired way. Best practices can play a major role in the comprehensive development of any academic institute and the agricultural university libraries are not exception to it.

1. Bibliometric

Bibliometrics is the application of quantitative techniques to analyze the volume, the movement, the frequency and the characteristics, etc of published documents and the related activities. Alan Pritchard was first to use the word “Bibliometrics” in an article “Statistical Bibliography or Bibliometrics” published in the 1969 issue of the “Journal of Documentation”. He defines Bibliometrics as “the application of mathematical and statistical methods to books and other media of communication.” In a later article, Pritchard explained Bibliometrics as the “metrology of the information transfer process and its purpose is analysis and control of the process”. Bibliometrics is a set of methods used to study or measure texts and information (Wikipedia, 2011).

Electronic health records contain extensive clinical, demographic, diagnostic and treatment information that can support medical research and decision-making. This chapter examines the characteristics of healthcare big data and the challenges associated with EHR storage, integration, quality and analysis. It presents an analytics pipeline covering data ingestion, secure storage, preprocessing, integration, pattern discovery, visualization and clinical decision support. Machine learning and deep learning methods are discussed in relation to clinical phenotyping, risk stratification, predictive epidemiology and adverse-outcome prediction. The chapter also addresses data privacy, governance, regulatory compliance, explainability, scalability and bias, highlighting the need for secure and interoperable EHR-mining systems that generate clinically useful knowledge.

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.

With the invention of bitcoin in 2008, the world was exposed to a new technology paradigm called blockchain. Blockchain was conceptualized by an unknown person named as Satoshi Nakamoto in 2008. The identity of Satoshi Nakamoto is still not known. Nakamoto proposed the design of blockchain which uses digital signature and timestamps to enable secure and trusted online transaction without the need of any trusted regulatory or third party.

20.1 Unit Testing in Python Programming

The process of testing wheather a particular unit is working properly or not is called unit testing. A unit test checks small component in your application

Example

def testsum1():

assert sum([1,2,3])==6 def testsum2():

assert sum([1,1,1])==6 #assertion error

testsum1()

testsum2()

20.1.1 Step Involved in Unit Testing

(1) Import unittest from library.

(2) Create a class for test and inherit (unittest.TestCase).

(3) Write the test function in the class.

(4) Call unittest.main()

Budgeting is a fundamental component of financial modelling programs and data analytics, serving as a structured approach to planning, allocating, and controlling financial resources within an organization. It involves estimating future revenues and expenses based on historical data, current trends, and strategic objectives. When combined with data analytics, budgeting becomes more dynamic, accurate, and responsive, enabling organizations to make informed financial decisions and adapt to changing conditions. The budgeting process begins with data collection and preparation. Historical financial data, including past revenues, operating costs, and capital expenditures, is gathered from various sources such as accounting systems and databases. Data analytics tools help clean, organize, and validate this information, ensuring consistency and reliability. This step is crucia

Business efficiency refers to the ability of an organization to use its resources— such as time, money, labor, technology, and materials—in the most effective way to achieve maximum output with minimum waste. In simple terms, business efficiency means doing work in the best possible way with the least amount of effort, cost, and time while maintaining high quality. Efficiency is not only about working faster; it is about working smarter. A business is considered efficient when it produces more results with fewer resources. For example, if a company can produce 100 products in 5 hours instead of 8 hours by using better machines or automation, then the company has improved its efficiency.

Cost analysis enables organizations to understand cost behavior, improve efficiency, and support strategic decision-making. When integrated with data analytics, cost analysis becomes more precise, data-driven, and adaptable, allowing firms to optimize resource allocation and enhance profitability. The process begins with the collection and classification of cost data. Costs are typically categorized into fixed, variable, and semi-variable components. Fixed costs remain constant regardless of production levels, while variable costs fluctuate with output, and semi-variable costs exhibit characteristics of both. Data analytics tools assist in organizing and cleaning large volumes of financial data from accounting systems, ensuring accuracy and consistency. Proper classification is essential, as it forms the basis

Easykrishi is a young enterprise that uses digital interventions with an aim to change agricultural practices from Reactive Mode to Proactive Mode. It is a consortium of 12 FPOs and 150 Ground Organizations. 14 In a world of declining land-holdings, we realize aggregation of agricultural resources is the only way to keep this planet sustainable for human survival. Aggregation ensures viability and profitability of Agriculture. Easykrishi’s baby steps in the entire cycle of aggregation of agricultural resources have made significant impact for Food Producer Corporations across Karnataka and they have made a mark on organizations and individuals that are dedicated for the betterment of Agriculture and farmers.

Key words : CAVD, Drug Design, Bioinformatics, CADD, Co-factor, Vaccinology, infectious diseases, Jenner, immunity, Molecular modeling, Immunoinformatics, Antigen, Antibody, Live Vaccine, Alive Vaccine, Protein-peptide, Enzyme, Tools for Vaccine design,
 
Vaccine – Prevention is Better than Cure

A vaccine is a biological preparation that improves immunity to a particular disease. A vaccine typically contains an agent that resembles a disease-causing microorganism, and is often made from weakened or killed forms of the microbe, its toxins or one of its surface proteins. The agent stimulates the body’s immune system to recognize the agent

13.1 Challenges in the technology transfer process
Technology transfer (TT) is more complex and involves the complex interrelationships between universities, research institutes, the private sector, the state, and society. Though TT is all about translating research findings into purposeful solutions that can support the economy and society, it is not a linear process. The challenges can be described as legal, institutional, cultural, financial, policy, and human resource. All this is accompanied by bottlenecks that inhibit or decelerate the diffusion of promising technologies.
13.1.1 Legal and intellectual property challenges
The vital component of technology transfer is the proper management of intellectual property (IP), which is often the most challenging aspect.
• Ambiguities in IP ownership: During multi-institutional or shared funder research, ownership is quite controversial. As an example, when a government lab and a university collaborate on the project, there is ambiguity as to who would have the ultimate licensing authority over the technology: the university, the lab, or the funding agency. This kind of fight hinders commercialisation and scares off industry partners.
• Lengthy patent filing and prosecution timelines: In several jurisdictions, the filings, examination, and granting of a patent may take between three and five years or more. Such delay is not compatible with the rapid cycle of invention in other fields such as ICT or biotechnology, where patent claims may become invalid before they are granted.
• High costs of IP protection: International direct patenting, like under the Patent Cooperation Treaty (PCT), is costly and involves legal services, fees, and upkeep costs. These costs are excessively high, which cannot be easily compensated by universities and startups, and where technologies are not secured or limited to local markets.

ABSTRACT

World is suffering from coronavirus infection since the end of 2019. Corona virus disease spraying rapidly throughout the world from the beginning of 2020, for that cause it is declared as pandemic by WHO in march 2020. As no proper remedy found for this particular new disease till the date it is become oblivious to maintain social distancing and lock down. Due to the situation each and every sector including the education system totally stopped from March 2020. But we have to maintain the flow of education. Many of the educational institution of India promoted e learning for their students. To avoid coronavirus infection social contact as well as to carry out classes in regular manner, it is become necessary to promote e learning everywhere. But in India the situation is different because Steel we are a poor country where a good number of people do not have a smart phone even. There are many challenges in promotion of e-Learning during this coronavirus pandemic situation in India. In present paper we are going to discuss about some challenges in promotion of e-learning due to coronavirus pandemic situation.

Introduction
Scaling Information Technology (IT) solutions for sustainability presents a complex set of challenges that hinder the ability of organizations and governments to deploy these technologies at scale. As the world faces pressing environmental challenges, such as climate change, resource depletion, and biodiversity loss, the  potential of IT solutions to drive sustainable development has garnered significant attention. Technologies such as artificial intelligence (AI), blockchain, the Internet of Things (IoT), and big data analytics can play a crucial role in creating smarter, more efficient systems that optimize resource use, reduce waste, and drive environmental stewardship. However, several barriers must be addressed before IT can be fully harnessed to promote sustainability on a global scale. These barriers include technological, economic, infrastructural, regulatory, and social challenges that impact the scalability of IT solutions for sustainability.

We have seen the procedures of testing hypothesis about population parameters in chapter 11. Frequently our interest is to test whether a set of observed values are in agreement with those which would occur if some specified hypothesis were true. The X² statistic provides a measure of agreement between such observed and expected frequencies.

The X² distribution has a number of applications in testing of hypotheses. It is used to test, (1) the independence of attributes, (2) the  goodness of fit, (3) the homogeneity of variances, (4) the homogeneity of correlation coefficients, and (5) the linkage in genetic problems. For applying X² test procedure we have to use only the actual observed frequencies and not the percentages or ratios. Further, the samples should be independent and the observations within a sample should be non-overlapping. The expected frequency in each category should be more than 5. The number of observations should be large, say, more than 50.

2.1 Quantitative Classification

It the data are classified on the basis of phenomenon which is capable of quantitative measurement like age, height, weight, prices, production, income expenditure, sales, profits, etc., it is termed as quantitative classification. The quantitative phenomenon under study is known as variable.  For example the daily incomes of different retail shops in a town may be classified as under.

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

Climate change, a global phenomenon of present time, has emerged as the greatest challenge of the world in the twenty-first century. Rising earth’s surface temperature is one of the most threatening factors coming into view. The substantial changes in the earth’s climate have an impact on ecosystems, economics and societies in a variety of ways (Sharma et al., 2024). It is one of the important environmental, agricultural, human and animal health issue. Climate change is an association of multidimensional effects involving physical characteristics, causes and consequences (Visschers, 2018). The climate change has resulted in to increasing global average temperature of air and ocean, melting of snow, rising of ocean level, change in the rainfall pattern and extreme whether events such as drought, flood, heat waves, storms etc.

Climate change and resource scarcity require farming systems that anticipate risks and make proactive decisions. This chapter explains how predictive modelling, artificial intelligence and IoT technologies can strengthen climate-smart agriculture. Statistical techniques, Convolutional Neural Networks, Long Short-Term Memory networks and Transformer models are examined for forecasting weather, crop performance and resource requirements. These tools can improve irrigation and fertilizer management, raise productivity and reduce environmental impact. Adoption remains constrained by poor-quality data, infrastructure deficiencies, cost, limited interpretability and unequal access to digital technology. Farmer-centred design, federated learning, gradual deployment and scalable systems are recommended for developing fair, practical and climate-resilient agricultural solutions.

Introduction

A complete neurological examination is warranted whenever an equine patient displays signs consistent with neurological disorder or to establish that the horse is neurologically normal (e.g., pre-purchase examination). Neurological examination of equine patient is challenging due to their large size, which prevents veterinarian from using many of the tests used for small animals. The key to a successful neurologic examination is to do the examination in a consistent and organized fashion. It is important to develop a routine, then do it the same way each time. This approach ensures that some parts of the examination are not forgotten and it also increases the consistency of the exam as you examine more number of equine patients. A standardized examination form is a valuable aid in this and can be concise. The goals of the neurologic examination are to determine whether disease of the nervous system exists, to localize the lesion to a particular area of the nervous system, and to describe and record the responses as a baseline for future evaluations. In this paper, a complete neurological examination is described with special emphasis on field conditions.

Opportunities

India is home to largest milch animal population, totalling around 100 million cattle and buffalo (out of total population of 300 million) spread over 6.5 million census villages. India is the largest milk producer with CAGR of 4.5%, likely to go up to 5%, which is likely to create around 20 million jobs every year. The most significant factor is that dairy industry is likely to exceed IT industry in employment generation. In year 2018, India produced 146 million tonnes of milk contributing 26% of the agriculture GDP.

But the foundation of dairy industry is economically vulnerable as per animal productivity and fertility are very low leading to meagre profits and frequent demands to enhance procurement price. National schemes like Operation Flood, National Dairy Plan-I and National Dairy Plan II, largely focussed on milk collection, processing, distribution and organising farmers but the animal component has been neglected. Enhancing per animal productivity by empowering farmers to scientific management and breeding to achieve genetic improvement needs urgent attention. Although numerous technologies to improve health and genetics are available but the single most important gap is failure to deliver these to farmers. Unlike western world, India has not been able to develop modern veterinary service delivery models. In order to cater to breeding and health services of cattle and buffalo (300 million in numbers) and also pet animals in rural areas and small towns, huge manpower and infrastructure will be required.

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.

Abstract

Cloud computing is becoming a technology mature enough for its use in genome research experiments. The use of large datasets, its highly demanding algorithms and the need for sudden computational resources, make large-scale sequencing experiments an attractive test-case for cloud computing. So far I have seen cloud computing demonstrated using R. However, it remains to be seen a rigorous comparison of its performance using a BLAST search and its ability to cope with ever-increasing databases and open source frameworks such as bioperl or bioconductor.

Introduction

Information Technology (I.T.) with its multi advantageous services has influenced the working of every sector whether it is education, industries, livelihood, agriculture, and other sectors viz government, business, health, employment, environment and security etc. Consequently, taking the benefit of I.T.,  most of the organizations have automated their systems to perform their routine activities in a better way as well as to offer more improved services.

Introduction
Sustainability, in its truest sense, requires a multifaceted approach where various sectorsgovernment, private industry, civil society, academia, and individualswork together to address complex and interconnected global challenges. Collaboration across sectors is desirable and essential to meet the diverse and far-reaching objectives outlined in the United Nations’ Sustainable Development Goals (SDGs). The global challenges of climate change, resource depletion, biodiversity loss, and social inequalities cannot be addressed by isolated efforts from any single entity; they require concerted action across multiple domains. This collaboration hinges on a collective recognition of sustainability as a shared responsibility that transcends traditional boundaries, promoting the integration of diverse perspectives and expertise to develop more holistic and effective solutions.

Sending a message from one place to another place, which are physically separated from each other, is called Communication. The Communication Engineering or Communication Technology deals with the design and development of engineering systems to transmit information from its source, and at the same time, to receive the same information at a remote destination with reliability, accuracy and quality.

ABSTRACT

Information and Communication Technologies (ICTs) are for everyone and that does not mean for urban or rural communities. Recent developments in the fields of information and communication technology are indeed revolutionary in nature. By definition, Information and Communication Technologies (ICTs) are a diverse set of technological tools and resources to create, disseminate, store, bring value-addition and manage information. Knowledge thereby becomes the fundamental resource for all economic and developmental activities in the knowledge society, of which rural poor form an equal part. The process of synthesis of knowledge possessed across communities with the global pool of knowledge and with the scope for further enrichment provides the genesis for information sharing.

ABSTRACT

Academic libraries are unique, essential and strategic hubs inside our social cloth which are advanced primarily based totally at the requirements of the times, to help people in the purchase of good enough facts that could allow them to decorate their information, for the development in their wellness and that of the society at large. To attain their goal, they ought to generate, filter, control and diffuse correct and well timed human information which have been harvested and documented; for the purpose of enriching the highbrow needs in their clients. Like every other kind of library, academic libraries are growing organisms. Justification to the above statement has been captured aptly in the classic book of Ranganathan (1931) entitled ‘Five Laws of Library Science’; detailing the principles of the library profession. The philosophical postulation of Ranganathan’s fifth law is a classical position that offers much strength and support for the need to adequately plan for the best strategies to exhibit in academic library environments in order to provide effective services. In other words, the fore-stated law by Ranganathan has already signaled that academic librarians must be ready at all times to effectively satisfy the diverse, complex and ever-increasing information needs of their users from the explosive available information resources that spring on a daily basis. As such, addressing this issue would require a holistic management approach by academic libraries that must be internally efficient and externally effective.

The chapter on completely randomized design (CRD) presents an overview of its theoretical principles, structure, and randomization methods. It includes the mathematical modeling of CRD and the analysis of ANOVA tables. Additionally, the chapter facilitates an understanding of how to analyze CRD and factorial CRD data using R, offering valuable practical insights for research applications.

Completely Randomized Design

Completely Randomized Design is a type of experimental design commonly used in statistical analysis and research. It is applied under specific conditions:

• Homogeneous units: CRD is suitable when the experimental units are consistent and uniform across the study area.

• Adherence to experimental design principles: This design conforms to two principal aspects of experimental design i.e. Randomization and Replication.

• Flexibility in replication: It allows for both equal and unequal replication of treatments within the experimental setup.

• Suitable for controlled environments: CRD is particularly effective for experiments conducted in greenhouses or laboratories, where conditions can be tightly regulated

It is emphasizing the systematic approach required for the successful planning and funding of projects:
Components of Project Formulation
1. Needs Analysis: Identifying and defining the needs that the project intends to address, typically involving stakeholder consultation and market analysis. This process involves systematically gathering and analyzing data about a community, a group, or a market to identify both the current conditions and the gaps between these conditions and desired outcomes. Effective needs assessment aids in designing projects that are relevant, targeted, and likely to produce significant impact. Here’s a more detailed exploration of how needs assessment is integral to project formulation:
Key Steps in Conducting a Needs Assessment
• Stakeholder Identification: Early in the project formulation, identify all relevant stakeholders, including direct beneficiaries, community leaders, funders, and other affected parties. Understanding who will be impacted by the project helps in targeting the needs assessment process more effectively.
• Data Collection: Utilize various methods such as surveys, interviews, focus groups, and field observations to gather comprehensive data about the current state and deficiencies experienced by stakeholders. This might involve looking at socioeconomic data, environmental conditions, or specific community challenges.
• Gap Analysis: Analyze the data to identify gaps between the current state and the desired outcomes. This analysis helps pinpoint the specific areas where interventions are needed.formulation: 

VIVEK M. PATIL & M.D. SURANAGI

A computer is an electronic device that has the ability to store, retrieve, and process data, and can be programmed with instructions that it remembers. Simply put, it is a very obedient servant which does whatever task it is commanded to do, in the manner in which it is trained/ programmed to carry out that task. Some of the terms commonly referred to while using computers are:

• Hardware : The physical parts that make up a computer eg. input devices, the central processing unit (CPU), memory and output devices.
• Software : A set of instructions that tell the computer what to do and how to perform a particular task is called a program, software program, or software. eg. Microsoft Word document management software enables us to type out a letter.
• Peripherals : Hardware devices connected to a computer i.e. any part of the computer outside the CPU and working memory. eg. keyboard, mouse, monitor, printer, scanner, disk and tape drives, microphone, speakers, joystick, plotter, and camera.
• Input : Whatever is typed, submitted or entered into a computer system. eg. typing a list of names.
• Data : The symbols that represent facts, objects and ideas on a computer. eg. representation of a list of names in binary language.
• Processing : Manipulation of data by the CPU of the computer or a microprocessor. eg. sorting of the list of names in alphabetic order.
• Output : The result produced by a computer after manipulating data. eg. printing out the list of names in alphabetic order.
• Memory : An area of the computer that temporarily holds data waiting to be processed or stored. eg. random access memory (RAM) and read only memory (ROM)

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.

Components of biodiversity are all the various forms of life on Earth including ecosystems, animals, plants, fungi, microorganisms, and genetic diversity. Ecosystems, species and genetic resources should be used for the benefit of humans, but in a way that does not lead to the decline of biodiversity. Substantial investments are required to conserve biodiversity, but it will bring significant environmental, economic and social benefits in return. To address these challenges various initiatives were taken in the past such as Ramsar Convention on Wetlands in 1971, Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) in 1973, 1979 Bonn Convention on Migratory Species (CMS) in 1979.

Abstract

Intellectual property right (IPR) is the most serious, new branch of law and a legal right conferred by federal government in accordance with International conventions and treaties. IPR protects some of the finer manifestations of human achievement. IPR safeguards the application of ideas and information that are of commercial value. The exploitable ideas become more sophisticated and hope for a successful economic future depends increasingly upon the superior corpus of new knowledge and fashionable conceits.  Copyright law is one of the effective tools of IPR. Copyright law provides an incentive to creative activity of author and permits the society to benefit from those inventions. However, the technological advancement has made violation of copyright very easy and regulation very difficult. This paper aims at familiarizing  copyright laws and the powers enshrined in copyright laws.

I. INTRODUCTION

The Websites are soaked in information, much of it with varying degrees of copyright protection. In fact, the reality is that almost everything on the Net is protected by copyright law. E-business Websites are a composition of materials, often consisting of words, graphics, audio, and video that are expressed to the consumer as information content. The owners and Website developers carefully select the content to sell the company’s product or service. The subject matter expressed in the site is an electronic publication of this content. Since, designing, producing, and maintaining a sophisticated Website is very expensive, protecting content ownership is extremely important. Electronic commerce will continue to be highly competitive. As Websites become more and more interactive with consumers, their creation, design, and maintenance place enormous demands on innovative marketing techniques that should be legally protected.

Never before has it been so easy to violate a copyright owner’s exclusive right to copy the material. Everyone with a computer and an Internet connection creates his own Web pages and thus become a publisher. Hence the rules that once applied to only a few companies bind million of people now. Imagine having paid a Website developer hundreds of thousands of rupees to launch a killer application and acquiring millions of Rupees from a venture capitalist firm, only to discover that another e—business is using your online material. Or consider a scenario where your Website is linked to an e-business that displayed copyrighted material without the owner’s consent. Could your company be liable under a theory of copyright infringement? This paper highlights the scenario when contents of your Website are exploited by others without your permission or knowledge. The discussion is centered on copyright issues involved in the practices of linking, inlining and framing technologies which are normally being used on the Internet.

10.1 Introduction

In previous lessons we have been dealing with problems connected with variation occurring in any one variable, say the income of a person, the height or weight of an individual, the wage of a worker in a factory, the sales of a shop, demand of a commodity etc.  The values of a single variable were obtained from every member of the population and the different characteristics were described by certain descriptive constants after grouping into frequency distribution.  In this lesson, we shall be dealing with problems wherein there are paired variables and every individual or member of the population exhibits two values, one for each of the variables under consideration.  Example of the two variables which are to be studies together are (i) the income and expenditure of a household, (ii) the price and demand or supply of a commodity, (iii) the height and weight of a student, and (iv) the grain yield and straw yield of paddy, (v) Height of father and height of son, etc.

This chapter begins with an introductory overview, providing a basic understanding of correlation and its types. It guides readers through in-depth analysis using R, demonstrating how to apply these concepts practically.

Correlation

Correlation analysis is a statistical method used to evaluate the strength and direction of the relationship between two variables. This analysis is vital in identifying and understanding how variables are interconnected within a dataset, providing insights into their mutual influences.

Key aspects of correlation include

• Range of the correlation coefficientThe value of the correlation coefficient (“r”) falls within the range of −1 to +1.

• Independence from origin and scaleThe correlation coefficient (“r”) remains consistent regardless of any changes in the origin or scale of the variables.

• Unit independenceThe correlation coefficient is independent of the units of measurement

of the variables.

• Relationship with independenceWhile uncorrelated variables are generally independent, the reverse is not necessarily true; independent variables will always be uncorrelated.

efficiency, and support strategic decision-making. When integrated with data analytics, cost analysis becomes more precise, data-driven, and adaptable, allowing firms to optimize resource allocation and enhance profitability. The process begins with the collection and classification of cost data. Costs are typically categorized into fixed, variable, and semi-variable components. Fixed costs remain constant regardless of production levels, while variable costs fluctuate with output, and semi-variable costs exhibit characteristics of both. Data analytics tools assist in organizing and cleaning large volumes of financial data from accounting systems, ensuring accuracy and consistency. Proper classification is essential, as it forms the basis for meaningful cost analysis and modeling.q