eChapters

Abstract

Biological Pathways represent system level sophistication of organisms. An exclusive peek into the extent of sophistication of an organism may provide proof of its past evolutionary struggle and position in the developmental trend from pathway point of view. This knowledge may not align with the traditional universally acknowledged standard 16s rRNA trend. Analyzing the reasons behind such discrepancy can constitute an important research work. To map sophistication of one organism with respect to other organisms, phylogeny is required. Here, we are mapping the level of sophistication in terms of enzymes among a set of TCA cycles belonging to different species, arbitrarily chosen from the KEGG/PATHWAY database. For this purpose, we converted the metabolic pathway information into simplified enzyme-enzyme relational (E-E-R) graphs. Similarity scores obtained from inter-genus, intra-genus as well as inter-species comparison of these graphs are used for phylogenetic tree construction. These phylogenetic trees throw light on the trend of development of metabolic pathways among different species. But peculiarly, these trees have less similarity with the conventional evolutionary phylogenetic trees constructed from NCBI taxonomy data. In this paper, we have tried to find some justifications for this dissimilarity among phylogenetic trees, by considering their habitats.

AI-based content creation refers to the use of Artificial Intelligence technologies to generate, edit, optimize, and publish content automatically or semi-automatically. Content can include text, images, videos, audio, presentations, reports, emails, and social media posts. AI content creation tools use technologies such as Machine Learning, Natural Language Processing (NLP), Computer Vision, and Deep Learning to create content that looks like it was created by humans. In the modern digital world, content plays a very important role in education, business, marketing, communication, and entertainment. Earlier, content creation was completely manual and required a lot of time, effort, and human resources. Writers, designers, editors, and analysts had to work for many hours to create a single report, article, or presentation. But now, with the help of AI tools, content can be created in minutes instead of hours

Symbolic systems provide logical reasoning and explicit knowledge representation, whereas sub-symbolic models offer learning, adaptation and pattern-recognition capabilities. This chapter explains how combining these approaches can create more accurate, adaptive and interpretable intelligent systems. It discusses rule-based systems, logic programming, ontologies, neural networks, fuzzy systems, evolutionary computation and swarm intelligence. Neural-symbolic, neuro-fuzzy, rule-augmented and ontology-aware architectures are examined, along with loose, tight and deep integration strategies. Applications in healthcare, cybersecurity, manufacturing and knowledge-based information systems demonstrate their practical potential. Challenges involving integration complexity, scalability, knowledge engineering, transparency and continuous updating are considered, together with future directions in human-centred hybrid intelligence.

4.1 Introduction and Measuring

A measure of central tendency exhibits only one of the important characteristics of distribution, namely they typical representative figure to the whole set of its values. This measure alone will not adequately describe a set of observation unless all the observations are alike. Moreover one cannot understand completely about the data set with a measure of central tendency.  Because in real practical situation there may exits a number of sets of observations (or different distributions) whose measures of central tendency are same but they may differ individually from each other in a number of ways.  In particular two sets of values have the same mean but whilst one set of data is grouped close to the mean the other one is more spread out.  The following examples will illustrate this view point clearly.

Evolution is a self-occurring natural phenomenon by which new organisms develop gradually as a result of change in genetic pool of a population. It is a very widely and historically studied phenomenon which still surprises scientists due to its underlying complexity. The three processes which contribute most to evolution are genetic drift (also known as random drift or allelic drift), natural selection and mutations. Genetic drift and natural selection act at a population level, whereas mutations act at individual level. For evolution to occur, the changes these processes bring about must be heritable, which they are.
 
Genes are present on chromosomes inside nucleus of the cell and the collection of all the genes in a cell is known as the genome of that cell. Genome size and complexity varies from species to species. Simple organisms, such as bacteria and protozoans have a very simple genome, with very few genes, whereas complex higher organisms such as humans have a large genome, with thousands of genes. The position of a gene on a chromosome is known as a locus. Organisms may have more than one copy of a chromosome in the cell.

I. INTRODUCTION

Nations are growing with pace in technology and technological advancements. Till the end of 20th century development was the priority of entire planet earth but in the ending of the 20th century it was realized by almost all countries that development cannot go alone but it has to have the social, economic and environmental concerns. It was realized by the United Nations in Rio Conference on Environment and Development, 1992 that there is need of sustainable development. Now in 21st century the technological advancements are needed to be utilized for saving the planet earth from the curse of climate change and global warming reducing the carbon emissions across the entire planet earth. The argument in this paper is based on, how far technological advancements and technology transfer, by developed countries to developing countries can be facilitated in reduction of the carbon emission and greenhouse gases, through legally binding norms? How there should be synergy in between technologies reducing carbon emission and IPR issues?

Introduction 
India’s digital transformation over the past decade has emerged as a defining feature of its governance and developmental strategy, propelled by the ambitious Digital India initiative launched in 2015 by the Ministry of Electronics and Information Technology (MeitY). With a vision to transform India into a digitally empowered society and knowledge economy, the initiative seeks to harness the power of information and communication technologies (ICT) to make governance inclusive, efficient, transparent, and responsive. Digital India marks a paradigm shift in how public administration operates, embedding technology at the core of policy delivery, citizen engagement, and institutional coordination across sectors and tiers of government. Creating a robust and scalable digital infrastructure is at the heart of this transformation. Major projects like BharatNet aim to connect 2.5 lakh Gram Panchayats via high-speed optical fiber, of which over 1.5 lakh were connected by 2020. Simultaneously, establishing more than 3.7 lakh Common Service Centres (CSCs) has extended digital service delivery to rural and remote areas. These CSCs function as inclusive access points, offering health, education, agriculture, banking, and public utilities services in a culturally and linguistically localized format, catalyzing rural digital entrepreneurship and last-mile inclusion. India’s approach to digital governance extends beyond infrastructure to encompass citizen-centric digital services and platform-based public service delivery models. Platforms such as UMANG, eHospital, BHIMUPI, DigiLocker, eCourts, Tele-Law, and eWay Bills have simplified citizen interaction with the state, enhancing the ease of living and improving administrative efficiency. The Government eMarketplace (GeM) has transformed public procurement by making it more transparent, efficient, and accessible for small businesses and start-ups. The Jan Dhan–Aadhaar–Mobile (JAM) trinity has revolutionized welfare delivery by facilitating Direct Benefit Transfers (DBT) into verified bank accounts, eliminating intermediaries, and saving over ?1.7 lakh crore by 2020.

Modern farm planning involves nonlinear, discontinuous and multi-objective problems that conventional optimization methods may not solve effectively. This chapter examines Genetic Algorithms, Particle Swarm Optimization and Differential Evolution for improving agricultural resource allocation. These nature-inspired techniques can simultaneously address crop yield, water use, fertilizer application, environmental effects and financial viability. Their integration with deep-learning forecasts and crop-simulation systems enables more realistic optimization. Multi-objective evolutionary algorithms identify Pareto-optimal alternatives, allowing decision-makers to balance productivity and ecological sustainability. The chapter also emphasizes explainable artificial intelligence, economical implementation and farmer usability as necessary conditions for transferring evolutionary optimization methods from experimental research to practical farm management.

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.

A microarray, as the name suggests, is a two dimensional array of microscopic probes which captures certain molecules in an environment by a process known as hybridization, and are used to detect quantities of these molecules in the environment. The probes can vary according to requirement of experiment, and so can the environment, which in technical terms is an extract of tissue under study.

1. Introduction

The advent of computers, the Internet and other ICT technologies have enhanced the nature of tasks. No one is immune from technology. Technologies have paved the way for information transformation. Today every section of society is making full use of technologies to fulfil their needs. Electronic devices have affected human life to a great extent. These devices have changed the working styles of human beings. Working with electronic devices is much better than manual work. Therefore, every sector of human working society is updating itself by using or implementing electronic devices. The higher education department is no exception to this. Today every sector of higher education department is using these modern tools and technologies not only for official works but also for teaching. Library is the most important part of Higher Education Institutions and today the entire structure of the library has changed. Today computersare being used in every section of the library and the main work of the library- issue/return is also being done through the use of computers and the Internet. The resources available in the library have also become electronic. Every resource like books, magazines, research journals, dictionaries, glossaries, bibliographies, biographies, who’s who etc, is now being published in electronic form and the library has to purchase these resources for its users.

Intelligent healthcare is advancing through the integration of multimodal data, trustworthy AI, federated learning, digital twins, IoT devices and real-time decision-support systems. This chapter reviews the evolution of healthcare-data analysis and examines emerging methods for intelligent diagnosis and predictive care. It discusses explainability, privacy-preserving analytics, interoperability, data governance and ethical frameworks necessary for responsible implementation. Wearable technologies and connected sensors support continuous monitoring, while human–AI collaboration improves clinical decision-making. Case studies illustrate practical healthcare applications and implementation strategies. The chapter also considers global-health requirements, particularly in developing countries, and identifies future opportunities for scalable, equitable, secure and clinically effective intelligent healthcare systems.

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.

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

AI-enabled livestock technologies can improve productivity, health monitoring, animal welfare and environmental performance, but they also raise significant ethical and governance concerns. This chapter examines data ownership, privacy, algorithmic bias, transparency, accountability, cybersecurity, socioeconomic inequality and animal well-being. Precision livestock systems continuously monitor animals through sensors, computer vision, machine learning and IoT networks, enabling preventive decisions and automated interventions. However, excessive dependence on algorithms may reduce human oversight and treat animals merely as data-producing units. The chapter advocates an interdisciplinary framework for responsible AI that aligns innovation with animal-welfare standards, farmer interests, social values, equitable access, regulatory requirements and long-term environmental sustainability.

12.1 Introduction
 
The design of the experiment includes (i) planning of the experiment (ii) obtaining the relevant information from it regarding the statistical hypothesis under study and (iii) making a statistical analysis of the data. Design of experiment may be defined as the logical construction of the experiment in which the degree of the uncertainty with which the inference is drawn may be well defined. It may also be redefined as the choice of treatments, the method of assigning treatments to experimental units and arrangement of experimental units in varies patterns to suit the requirement of particular problems, are combined known as the design of experiment.

Epigenetics is the study of reversible heritable changes in gene function that occur without a change in the sequence of nuclear DNA and Gene-regulatory information that is not expressed in DNA sequences is transmitted from one generation (of cells or organisms) to the next. Epigenetics has become an integral part of the study of evolution and development during the past 50 years. It was originally defined by Conrad Hal Waddington in 1942 (Waddington, 1942), but it is currently referred to any mechanism that affects genes without changing the nucleotide sequence. These mechanisms are involved in deploying the genetic program for the many processes operating during the lifespan of a cell: development, differentiation, stress response, and pathological conditions, and by the environment to adapt to those situations (Rakyan and Beck, 2006). Epigenetic mechanisms include covalent chemical modification of DNA (methylation) and chromatin (covalent histone modifications), non-coding RNAs, and polycomb group (PcG) genes, and are ultimately related to the regulation of gene expression and chromatin structure.

Some basic and most common analysis tools used in research are summary statistics. These can be analyzed in Ms-Excel through the following ways.

8.1 Summary Statistics

The following data are the yield (q/ha) of 60 varieties of paddy maintained in a research station for breeding trails:

53 ,74, 89, 65, 88, 65, 45, 70, 25, 80, 75, 85, 47, 63, 78, 44, 42, 51, 59, 86, 76, 48, 30, 38, 67, 78, 74, 68, 58, 59, 36, 76, 89, 48, 67, 69, 35, 46, 89, 75, 79, 56, 42, 41, 60, 65, 68, 69, 61, 63, 95, 94, 85, 83, 76, 71, 68, 62, 64, 62.

Calculate the summary statistics for the study of the data.

Solution: The summary statistics of the data using MS – excel can be obtained / analyzed through following steps:

Step 1: Open Excel sheet and feed the data as given in Fig. 8.1.

Step 2: Go to data menu and select Data Analysis. Sometimes this menu is not available in the data, it can be added through add in → check in, analysis tool pack → go. It will appear as the way showing in Fig. 8.1.

7.1 Meaning of Sampling
    
Before we consider sampling theory, we first define the terms population and sample.
 
The word ‘population’ or ‘universe’ in statistics is used to refer to any collection of individuals or of their attributes or of results of operations which can be numerically specified. For example the assemblage of heights of all the individuals in the population is called a population of heights.  Similarly we may consider the population of weights, wages, yields of grain; prices of wheat, mileages of automobile tyres, etc. a population containing a finite number of individuals or members is called a finite population.  For instance the population f ages of the workers in a factory constitute a finite population of ages. A population with infinite number of members is known as infinite population.  For example the population of pressures of temperatures at various points in the atmosphere is an infinite population. 

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.

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.

Abstract

The field of bioinformatics emerged as a tool to facilitate biological discoveries more than 10 years ago. Structure prediction is fundamentally different from the inverse problem of protein design. Protein structure prediction is one of the most important goals pursued by bioinformatics and theoretical chemistry; it is highly important in medicine and biotechnology. Structure prediction is fundamentally different from the inverse problem of protein design. While the genome of many organism have been sequenced over the last few year, transforming such raw sequence data into knowledge remains a hard task. A great number of prediction program for genes have been developed hat try to address this problem, which consists of locating the genes along a genome.

Introduction

Today we are part of a highly complex and dynamic society. The complexity and dynamism are primarily due to the chain of changes induced by the scientific and industrial developments. The impetus provided by the information explosion coupled with scientific and technological developments to human life has triggered several unimaginable developments in the number, structure and functions of formal organizations. Realization of the supremacy of human mind over the tangible and exhaustible organizational resources like money, material and machine has led to the concept of ‘human capital’, which is now being considered as the most critical organizational resource to ensure organizational effectiveness and efficiency. Analogously huge investments are going in towards servicing and developing this critical resource of human capital in organizations. In this context, the concepts of human resource development (HRD) and training assume greater significance. In fact, training has been considered as a potential tool for HRD since ages. However, as a distinct profession, and also as a critical management tool, training is still in its early years. At the same time, there have been significant changes in the way training is conceived, understood and practiced.

Agricultural colleges and universities were first formed in the belief that farm production could be increased as a result of the systematic application of current technology and agricultural research findings. The mission of these early educational institutions was to scientifically study agriculture with the participation of the farming community; to carry the results to a broad range of farmers who could use them; and to train farmers, extension workers, agricultural teachers and researchers so that agricultural production could continue to be increased on a sustained basis. In many of the developing countries, agricultural education and training have failed to adapt and respond to the realities of rural societies. In response to the urgent need to review and adjust teaching and training in agriculture at all levels. The training is an important tool to overcome the problem faced by the rural people all over the world. The availability of the resource is the crucial determining factors to strengthen the training Programme for the needy people.

7.1. INTRODUCTION

The word ‘significance’ in statistics does not describe the strength of association. Statistical significance is simply related to the probability of the result being commonly or rarely encountered (If the probability of chance occurrence is low the result is likely to be determined by the set of circumstances under study and not by chance). If conclusions are to be drawn that a difference exists between two groups of subjects, then we have to ensure that they are equal in all respects except the one under test.

‘P’ values are used to assess the degree of dissimilarity between two (or more) sets of measurements. The ‘P’ value is the probability of obtaining a result as extreme as or more extreme than the one observed. The ‘P’ value is actually a measure of surprise and the smaller the value, the more surprising is the result. When ‘P’ value is between 0.05 and 0.01, the result is usually called statistically significant; when it is less than 0.01, it is usually called highly significant and if, values are lower than 0.005, then it is called very highly significant. Traditionally, the approach had been to indicate the level of significance by rounded off ‘P’ values e.g., P < 0.01. Now it has become a fashion to calculate the exact probabilities and thus arrive at a particular decision.

Biotechnology in the food processing sector targets the selection and improvement of microorganisms, including bacteria, yeasts and moulds with the objectives of improving process control, yields and efficiency as well as the quality, safety and consistency of bioprocessed products. They perform a series of processes such as fermentation, hydrolysis, etc. and even spoilage of food, depending upon the species of organisms, nature of raw material (food), environmental factors (temperature, pH, salt concentration, etc.), duration, etc.

Fermentation
Is the process of bioconversion of organic substances by microorganisms and/or enzymes (complex proteins) of microbial, plant or animal origin. It is one of the oldest forms of food preservation which is applied globally.

Introduction

Functional genomics is a field of molecular biology that attempts to make use of the vast wealth of data produced by genomic projects (such as genome sequencing projects) to describe gene (and protein) functions and interactions. Unlike genomics and proteomics, functional genomics focuses on the dynamic aspects such as gene transcription, translation, and protein-protein interactions, as opposed to the static aspects of the genomic information such as DNA sequence or structures. Functional genomics includes function-related aspects of the genome itself such as mutation and polymorphism (such as SNP) analysis, as well as measurement of molecular activities. The latter comprise a number of “-omics” such as transcriptomics (gene expression), proteomics (protein expression), phosphoproteomics and metabolomics. Together these measurement modalities quantifies the various biological processes and powers the understanding of gene and protein functions and interactions. Functional genomics uses mostly high-throughput techniques to characterize the abundance gene products such as mRNA and proteins.

Computational intelligence is central to the development of proactive, personalized and connected healthcare systems. This chapter examines artificial intelligence, machine learning, expert systems, medical-image analysis, IoT, big-data analytics, cloud computing and blockchain as key enabling technologies. It presents intelligent architectures for diagnosis, monitoring, treatment support and secure health-data management. Privacy, cybersecurity, explainability, bias, fairness, scalability and deployment challenges are discussed in relation to practical healthcare implementation. The chapter proposes a research roadmap involving trustworthy AI, advanced privacy-preserving mechanisms, edge-cloud intelligence, digital twins, multi-omics integration and personalized health models. It also emphasizes accessible computational-intelligence solutions for rural and resource-constrained healthcare environments

Introduction
Genetic research has been transformed by technological developments, and has by necessity become extremely quantitative, as massive quantities of varying complex data types can now be generated very rapidly. High-throughput data are being used in a variety of basic science investigations that have implications in the diagnosis and treatment of human disease: identification and characterization of genetic variants associated with a particular disease within and across populations; discovery of gene expression signatures associated with disease phenotypes; identification and testing of potential disease biomarkers. Methodological advances in the statistical treatment and interpretation of these data are needed in order to meet the pressing need for powerful, efficient and robust analysis.

Productivity automation refers to the use of technology, software, and artificial intelligence to perform tasks automatically with minimal human effort. The main goal of productivity automation is to save time, reduce manual work, increase efficiency, and improve the quality of output. In simple words, productivity automation means using machines and digital tools to do work faster and more accurately. Productivity automation is used in many areas such as business operations, communication, data management, marketing, education, and personal productivity. For example, sending automatic emails, scheduling meetings, generating reports, managing tasks, and analyzing data are all examples of productivity automation. The concept of productivity automation is based on the idea that repetitive and routine tasks should be handled by machines so that humans can focus on creative,

Technology Assessment and Refinement
What is Technology Assessment?
Technology assessment serves as a method or tool for forecasting future technological needs. It helps in envisioning or predicting potential technological developments required by users and in identifying areas where existing technologies can be improved. In agriculture, evaluating a technology means understanding its worth or effectiveness. This evaluation can be done by researchers, farmers, or other relevant stakeholders. The knowledge obtained from such evaluations is essential for creating new technologies, improving existing ones, and ensuring their successful adoption by end users.
The approach to technology assessment varies depending on who is conducting the evaluation, the purpose behind it, and the specific characteristics of the technology being assessed. It is commonly defined as the organized and intentional use of knowledge, skills, and experience to carry out a task or deliver a service that contributes to the well-being of society.
Technology Assessment: Definition and Objectives
According to Strasser (1972), technology assessment is a structured approach to planning and forecasting that identifies various options and associated costs. It considers not only economic factors but also environmental and social impacts, both internal and external to the technology being evaluated. The assessment places emphasis on analyzing both the positive and negative consequences of technologies. As such, institutions involved in the development, refinement, and dissemination of agricultural technologies have a significant stake in conducting technology assessments.

8.1. INTRODUCTION

The word ‘non-parametric’ means “not for any specific parameter”. For example, in the student’s t test we use the parameter ‘mean’. In the non-.parametric tests we check the randomness and trend of the pattern of distribution of a variable directly, without using a population parameter for comparing.

In contrast to the parametric tests, where many assumptions are made regarding the distribution of the parameter in the population (i.e. Gaussian features), here we do not make stringent assumptions about the population from which samples are drawn. These techniques are, therefore, also known as ‘distribution free’ tests.

We shall be discussing about 10 such non-parametric tests.

Advanced artificial-intelligence models can achieve high predictive performance, but their black-box nature limits clinical trust and accountability. This chapter examines interpretability and explainability as essential requirements for trustworthy healthcare analytics. It reviews intrinsically interpretable models, post-hoc explanation methods, example-based approaches and network-based explanations. A framework combining data preparation, model selection, ensemble learning, calibration, uncertainty estimation and explanation validation is presented. An explainable sepsis-risk prediction case study demonstrates logistic modelling, ensemble methods, Bayesian uncertainty, SHAP attribution and network analysis. The chapter also addresses causal reasoning, adversarial robustness, temporal and multimodal data, human-centred explainability, ethical responsibilities and regulatory requirements for dependable clinical AI.

Introduction
At the beginning of the 21^st century, with a population of 6.1 billion in 2000 and expected to be headed for 9.1 billion by 2050 (UN News Centre, 2005), the challenge of yet again increase food production by many folds only in 50 years has become a daunting task in itself. The situation is further worsen because now, we need to increase food production in sustainable way by 2050 on approximately the same area of arable land using less resources, particularly, fossil fuel, water and nitrogen, at a time when we must also mitigate some of the enormous challenges associated with climate change. Furthermore, there is the critical and urgent humanitarian need to alleviate poverty, hunger and malnutrition which is afflicting more than 1 billion people

“Knowledge Management is the process by which an organization identifies, creates, manages and delivers information to enhance workforce performance. The purpose of knowledge management is to provide information collected from employees, best practices, clients, competitors and other sources in a format that employees can use to the productivity and quality of their work”

Before attempting to address the question of knowledge management, it’s probably appropriate to develop some perspective regarding this stuff called knowledge. Consider this observation made by Neil Fleming [1996] as a basis for thought relating to the following points.

Training helps people to become qualified and proficient in doing some jobs (Dahama, 1979). Usually an organization facilitates the employees’ learning through training so that their modified behaviour contributes to the attainment of the organization’s goals and objectives.
 
Training needs refer to the gaps between desired and expected levels of performance. Needs may be numerous. Besides they also need group, affection, recognition and fulfilment of their talents.

Training need may be defined as difference between existing and desired performance of person(s). In other words it is a gap between job requirement and job performance. That means it is gap between the existing and desired level of competency. Thus, the attitude, knowledge and skills of a person are lower than desired if the gap is significant there is a need for training.

In India’s National Extension Programme, professional workers constitute the link between the people and the institute created to advance economic and social change upon the character philosophy, technical knowledge and compliance of these workers largely rests for the success of the programme. Hence the quality of these workers and their training is the important element to be dealt with properly and guided wisely.

In early fifties, the Gramsevak / Village Level Workers (VLW’s) served as a key-man in Community Development Programme and therefore, the Government of India aimed at imparting him by better training and supervisory assistance. As a step in this direction, Gramsevak Training Centers were started to impart training to GramSevaks / VLW’s and Extension Officers were appointed in block for better supervisory assistance.

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.

Computation has become an essential tool in life science research. This is exemplified in genomics, where first microarrays and now massively parallel DNA sequencing have enabled a variety of genome-wide functional assays that require increasingly complex analysis tools. The problem of accessibility of computational tools has long been recognized. Without programming or informatics expertise, scientists needing to use computational approaches are impeded by problems ranging from tool installation; to determining which parameter values to use; to efficiently combining multiple tools together in an analysis chain. The severity of these problems is evidenced by the numerous solutions to address them. Kowledge on software  and web-based interfaces for tools all improve the accessibility of computation. These approaches each have advantages, but do not offer a general solution that enables a computational tool to be easily included in an analysis chain and run by scientists without programming experience.However, making tools accessible does not necessarily address the crucial problem of reproducibility.