eChapters

Introduction

Digitalization is transforming the digital economy, offering opportunities for entrepreneurs and businesses, and transforming production and trade. The rapid advancement of technology and information systems has led to significant changes in both individuals and companies, particularly in the realm of digitalization in commerce (BarNir et al., 2003). Digitalization uses digital technologies to revolutionize business models, simplify transactions, and enhance value-producing opportunities in processes like customer service, shipping, buying, and selling (Zhang et al., 2013). All over the world, businesses are transitioning from manual to e-commerce for benefits such as efficiency, productivity, security, competitiveness, cost reduction, marketing, promotion, and information distribution (Daniel & Wilson, 2002; Gefen, 2000). E-commerce, a key feature, is reshaping global production and trade (Koh et al., 2017; Scalco, 2017). Moreover, the e-commerce industry has seen a substantial transition over the last few decades, due to rapid technical improvements and shifting customer behaviors.

Introduction

The advent of Industry 4.0 marks a pivotal era of transformation in the industrial sector, characterized by a fusion of technologies blurring the lines between the physical, digital, and biological spheres. This paper aims to delve into the bibliometric analysis of Industry 4.0, offering a comprehensive overview of the current state of research, predominant themes, and emerging trends within this revolutionary domain. By systematically mapping out the scholarly landscape, this study will not only highlight the intellectual contours that shape Industry 4.0 but also provide insights into the collaborative networks and influential regions propelling this field forward. Through this bibliometric scrutiny, we endeavour to discern the patterns of publication, citation trajectories, and the evolution of topics over time, thereby furnishing stakeholders with a strategic vantage point from which to foresee future directions and opportunities in Industry 4.0. This introduction sets the stage for a nuanced exploration of the intricate tapestry of research that constitutes the backbone of Industry 4.0, laying the groundwork for understanding its impact on the industrial paradigm and its potential to drive innovation, efficiency, and sustainability in the Fourth Industrial Revolution.

Introduction

The Fourth Industrial Revolution or Industry 4.0 is the aggregation of advanced digital technologies into manufacturing and production processes. These technologies include Cyber-Physical Systems (CPS), the Internet of Things (IoT), big data, cloud computing, artificial intelligence (AI), and autonomous systems, which collectively facilitate smart manufacturing and interconnected industrial operations. When examining Industry 4.0’s wider ramifications, contradictions or fascinating facts surface. Its impact is widespread in society and the economy, even if it mostly concentrates on manufacturing. It may be seen in the service industry, urban development, and agriculture, among other areas (Ane & Yasmin, 2019; Kraus et al., 2022). Furthermore, the phrase encompasses not only the development of technology but also the evolution of organizational leadership and the significance of security and privacy in the digital era (Beattie & Wyer, 2019; ONIK et al., 2019). Overall, Industry 4.0 represents a transformative era where digital technologies are harnessed to create intelligent, interconnected systems that revolutionize traditional industrial practices.

Introduction

Artificial intelligence is an effective technology that makes it possible for computers and other devices to simulate human intellectual abilities and ability to solve problems. Comprehensive study on artificial intelligence may be traced back to Alan Turing’s development of the renowned Turing Test in the 1950s, which examined whether machines are capable of thinking. (Turing, 1950), and John McCarthy came up with the phrase “artificial intelligence” in 1955 when he organised the 1956 Dartmouth Summer AI Research Project. They suggest that “making a machine behave in ways that would be called smart if a human were doing so” is an issue with AI (McCarthy et al., 1955). Compared to humans, artificial intelligence can analyse data, both organised and unstructured, substantially faster and more accurately.

Introduction

The term "artificial intelligence" was originally coined in 1956 at a conference hosted by Dartmouth College. While the aspirations and dreams of the attendees were sky-high, the reality was sobering. The thought of citizens in space or self-driving cars within a decade was ancient science fiction. The building blocks of the required computing infrastructure, as well as AI, notably deep learning and statistical machine learning methods, were not even in the distant future. The aim was to create an artificial brain for a machine, where the "brain" is the so-called "expert system", a software program that can integrate a large knowledge base and use it to solve problems in the same manner that a human expert could. The intellectual merits and achievements of a few AI systems designed solely to narrowly excel in a specific problem became the actual problem of the nascent field.

Introduction

India has shaped its economic growth and transformed through information technology and digitalisation/ The Digital India initiative has tremendously improved its economic development. India’s efforts to revolutionize its information technology have taken the road ahead among the developed countries/ The contribution in the digital payment system has shifted in the banking and financial services sector. The emergence of technology upgradation through the internet has gained momentum in shifting towards internet and mobile banking technologies. The advent of technology has revolutionized the banking system through the evolution of financial technology with sustainable technological architecture through artificial intelligence which has enhanced ease of access for customers in the banking and financial sector. Artificial intelligence revolutionized various industries especially in the banking sector which has enhanced the way for interact the customers. The AI technology helps in reducing the cost for the Financial Sector. The Banks are welcoming the new age technology to serve betterment of the modern banking services.

Introduction

Virtual supply chain management is the digital representation of inventory data that the managers manage from anywhere in the world. In the 1980s, when the proliferation of computers gave more importance to the human resource, f inance, and manufacturing units communicating with each other for better quality work, that led to enterprise resource planning, and slowly each task of supply chain management started virtualizing in its full length.

A virtual supply chain has advantages such as better inventory control, shorter lead times, quicker order fulfilment, increased supply chain visibility, more accurate demand forecasts, and higher customer satisfaction. It gives businesses the ability to make the most use of their resources, cut expenses and lessen waste throughout the supply chain.

However, putting in place a virtual supply chain calls for thorough planning technology integration, and network-wide process alignment. Overcoming obstacles like data security, interoperability, and change management are all part of it. To fully reap the rewards of a virtual supply chain, businesses must also invest in technical infrastructure, training courses, and partnerships.

Introduction Data analytics is a systematic analysis of data or statistics. It is also the process of collecting, cleaning, transforming, interpreting, modelling and visualization the data. Such transformed data has to be further investigated to retrieve helpful information that may help conclude and support decision making. This study presents revising selected two relative valuation models (PE Model, PB Model) by replacing the most non-significant variables used in the model with the critical variable that is the potential cause for improving the model’s performance. The objective of relative valuation is to value assets based on how similar assets are priced in the market. This paper measures the valuation accuracy using Root Mean Squared Errors and refine the relative valuation models using the machine learning algorithm for predicting the value of a stock. The following steps helped to revise and refine the PE Model, PB model (Relative valuation models) to predict the value of Sensex stocks.

Changing Dynamics in HM

HM has transformed significantly; a lot because of changes in consumer behaviour, technological advancement, economic developments, regulatory changes, and growing consciousness of sustainability (Samaddar & Mondal, 2024). These changing dynamics have pushed customer’s expectations toward better and more personalized service (Law et al., 2024). Initially, the focus was on basic services and manual processes, but since then, the HM industry has embraced smart technologies like mobile apps, AI, IoT, and big data analytics. These developments have revolutionized the way customers interact; from merely checking in with their mobile phones, and controlling their room settings, to house-keeping and service delivery, thanks to AI-powered customization and automation. Using cloud services for managing properties and improving cyber security helps keep operations smooth and keeps data safe. Efforts to use energy and water more wisely show the industry's move towards being more sustainable. As the HM continues to evolve, industry stakeholders eye towards more personalized, efficient, and sustainable guest experiences. This would reflect the ongoing commitment to excellence and adaptation efforts to strive and evolve in the dynamic global landscape.

Introduction

Stocks from different firms can be traded and listed on the stock market. In addition, the stock market evaluates listed firms based on their worth, effectiveness, profitability, and capability. Anybody can trade shares on the stock market, which is an open global market. GDP is the total amount of profit produced in a country over a given period. To develop products and services in a country to a plausible degree, it is helpful to measure the monetary policy of completed goods and services that are acquired for a single end consumer. It comprises all earnings generated inside a country’s borders. The GDP of any nation may be used to determine the size and state of that nation’s economy. GDP is a measure of a nation’s total production and performance and is used as a gauge of economic health. Strong GDP growth frequently signals more consumer spending, corporate profitability, and company expansion, all of which are positive indicators of optimistic mood in the stock market. On the other hand, a slow GDP growth rate might undermine investor confidence and cause stock values to decline.

Introduction

In recent years, there has been an exponential increase in the volume of datasets generated across various application domains. However, a prevalent issue within these datasets is class imbalance, wherein the distribution of samples is biased towards the major class, resulting in an extremely skewed dataset. Number of samples in the major class far exceeds those in the minor class, sometimes with an extreme ratio like 1/1000 or even higher. While traditional machine learning models trained on such imbalanced datasets often yield high accuracy for the overall dataset and the major class samples, their ability to correctly predict class labels for minor class samples is signicantly compromised. In many real-world scenarios, the minor class samples hold greater signicance and importance compared to the major class samples. Therefore, researchers are increasingly focusing on addressing the challenges posed by class imbalanced datasets and exploring solutions to improve the performance of machine learning models in handling such imbalances

Introduction

Industry 4.0 is essentially the digital revolution of artificial intelligence and subsequent production processes utilizing contemporary smart technology. Cyber-physical systems, or intelligent machines, are the cornerstone of this revolution (Scoop, 2018). The key features of Industry 4.0 include the integration of IoT, increasing automation technology, and enhanced communication and self-counseling techniques (Tay et al., 2018). According to Schwab (2017), new developments in artificial intelligence (AI), robotics, nanotechnology, biotechnology, the Internet of Things (IoT), decentralized systems, 3D printing, fifth-generation communication technologies, quantum computing, and autonomous cars will all be part of this revolution. Early in the 2010s, Germany saw the emergence of Industry 4.0 as a result of government initiatives to advance manufacturing computerization as part of its high-tech strategy project. The concept of Industry 4.0 has been implemented in a variety of contexts and rebranded by various industries or businesses as needed.

Introduction

The 21st century has witnessed unprecedented changes in the global job mar ket, driven by technological innovation, globalization, and evolving industry needs. As traditional job roles become obsolete and new ones emerge, skill development has become paramount for both individuals and organizations. This paper explores the concept, importance, key elements, methodologied and challenges in skill development.

Objectives of the study

This study carried on the basis of following objectives

1. To study the concept of Skill Development.

2. To know the Importance of Skill Development.

3. To know Key elements of Skill Development.

4. To know Methodologies of Skill Development.

5. To know Challenges in Skill Development.

Introduction

In the dynamic realm of contemporary work environments, the psychological contract has emerged as a cornerstone concept, influencing the fabric of employee-employer relationships and organizational dynamics. While traditional employment agreements delineate formal terms and conditions, the psychological contract encapsulates the implicit understanding between individuals and their employers regarding mutual expectations, obligations, and entitlements. This symbiotic agreement, rooted in perceptions and beliefs rather than written documentation, shapes the essence of the employment relationship, impacting employee engagement, organizational commitment, and overall workplace satisfaction.

The significance of the psychological contract has become increasingly pronounced in recent years, propelled by a confluence of factors such as rapid technological advancements, demographic shifts, and evolving socio economic trends. In this era of unprecedented change, the nature of work itself has undergone a metamorphosis, with traditional notions of employment giving way to flexible arrangements, remote work options, and gig economy platforms. Consequently, the traditional employer-employee dynamic has evolved, necessitating a deeper understanding of the implicit expectations and reciprocal obligations that underpin modern work relationships.

Introduction

In recent years, the convergence of digital technologies has ushered in a new era of industrial transformation known as Industry 4.0, characterized by the fusion of physical, digital, and biological systems. While much attention has been focused on its implications for urban industries and global supply chains, the potential of Industry 4.0 to reshape rural economies remains relatively unexplored. This paper seeks to address this gap by examining how Industry 4.0 technologies can optimize supply chains in rural contexts, with a focus on the unique challenges and opportunities present in rural India.

Rural economies play a crucial role in the socio-economic fabric of nations, particularly in countries like India, where a significant portion of the population resides in rural areas. According to the World Bank, over 65% of India’s population lives in rural regions, where agriculture remains the primary source of livelihood for millions (World Bank, 2020). However, rural communities in India face a myriad of challenges, including inadequate infrastructure, limited access to markets, and low levels of technological adoption. These challenges not only impede economic growth but also contribute to disparities in income, education, and healthcare between rural and urban areas.

Introduction

A Brief Overview of Industry 4.0

It is widely agreed that foundational technologies such as 5G, IoT, Robotics, Nano-Tech, Brain-Computer Interface (BCI), Neural Networks, Quantum Computing, Cost-efficient and low-emission Transportation, and Sustainable Power have coalesced to give birth to industry 4.0.

Industry 4.0, sometimes also designated as the fourth wave of Industrial Revolution, is all about transforming traditional manufacturing and related industrial practices through various digital technologies. It represents a paradigm shift characterized by the amalgamation of multiple technologies such as computing, the Internet of Things (IoT), convergence, artificial intelligence (AI), big data, and automation into a unified protocol for application in the industrial processes.

At its core, Industry 4.0 emphasizes the interconnectedness and intelligent communication between machines, products, and people. It revolutionizes production and operational methods by enabling machines to operate semi autonomously and make decentralized decisions. This allows for increased f lexibility, efficiency, and customization within manufacturing processes.

Key elements of Industry 4.0 include the digitization of manufacturing, smart factories, and the use of data-driven technologies to optimize production. It incorporates concepts such as predictive maintenance, where machines predict their own maintenance needs, and the Industrial Internet of Things (IIoT), allowing devices and systems to communicate, analyze, and optimize processes in real-time.

Introduction

The technology behind Internet search engines is advancing quickly, and this has an impact on how individuals find the information they need online as well as how search engines improve their algorithms (N. Zafar et al. 2022). Search engines are playing a prime role in generating leads for businesses (Olbrich and Schultz, 2014), Because of this, search engines are regarded as the most recent online communication channels for dynamic marketing (Olbrich and Schultz, 2014). To examine how academic institutions (in higher education) use the web to facilitate student relations and identify how prospective students are using the Internet to research academic institutions, Will et al. conducted a study that involved the analysis of more than 3000 university websites. The web continues to be the primary research resource for prospective students, counselors, and parents. The research was undertaken (Sampath Kumar and Kumar, 2013) to determine whether or not academics utilized search engines to access scholarly content from the internet. 285 (95%) of the 300 respondents used search engines to find academic content, compared to just 5% who didn’t. To access the Ed-tech companies that are playing a role in academia, comparable assessment is crucial.

Introduction

The value of data privacy in the linked world of today cannot be emphasised. As digital technologies proliferate and personal data is collected, stored, and analysed on a large scale, people and organisations are more susceptible to data exploitation and privacy violations. In the modern era, privacy of data are vital because of following reasons:

1. Safety Regarding Personal Information: Fundamental People’s freedom to manage their personal data protected by data privacy. In a time when everyone has a digital footprint, maintaining one’s liberty and dignity requires protecting private information such as financial records, health records, and internet activity

2. Trust Management is Critical for Organisations and Companies: Maintaining data privacy improves reputation and brand loyalty by building trust with stakeholders, clients, and consumers. On the other hand, privacy violations and data breaches can seriously harm an organization’s reputation by undermining people’s confidence in its ability to protect sensitive data.

3. Compliance with Laws and Regulations: Strict data privacy laws have been passed by governments all over the world to uphold people’s rights and make businesses responsible for handling personal information. Adherence to regulatory mandates similar to the General Data Protection Regulation (GDPR) in the EU and the California Consumer Privacy Act (CCPA) in the US not only necessary from a legal standpoint but also morally required for moral business practices.

Introduction

Micro, small, and medium enterprises (MSMEs) dominate the Indonesian economy. These enterprises account for around 99 percent of all businesses in Indonesia and employ more than 95 percent of the total workforce across the country. They should be the primary engines of economic growth, although they generate only approximately 56 percent of the overall gross domestic product (GDP) of Indonesia. Around 80 percent of MSMEs are in the trading sector involved in buying and selling existing products/services). Within the MSME group, micro and small enterprises (MSEs), including those in the manufacturing industry, called micro and small industries (MSIs), i.e. business units with up to a maximum of 500 million Indonesian rupiah (IDR), are the ones that receive the most attention from the Indonesian government because they account for almost 99 percent of total MSMEs. So, compared to medium and large industries (MLIs), MSIs are the largest job creators in the country. These enterprises also have high resilience in various previous crises, including the COVID-19 pandemic. Their high resilience has played a role as a cushion for the economy because of their ability to survive periods of pressure and grow back faster and higher after pressure.

1. Introduction

Have you ever dreamt of exploring an entirely different realm? Envision a place where an alternate version of yourself can freely roam and pursue various activities without the constraints of reality. In this realm, you can engage in work, socialize, acquire possessions, conduct transactions, embark on novel experiences, and revel in unprecedented adventures. Now, imagine that major technology companies are actively developing this virtual world to make it a tangible part of your life. The once fantastical concept of the Metaverse is now poised to become the next major innovation(Jung et al., 2024). It is widely acknowledged by scholars and researchers that the Metaverse is an idea of technology that attracts itself as a three-dimensional monotony of the Internet. Moreover, as noted earlier, definitions of the metaverse in existing literature and among professionals reflect the integration of physical objects in virtual space as a consumable description of the metaverse great role(Dwivedi et al., 2022). The key to any Metaverse technology is their user experience. Analysis of Metaverse applications shows that most current applications are stand-alone or centralized. These applications provide different user experiences, based on the cardinal technology and interface devices used to communicate within virtual environments.2

Introduction

Global economies have witnessed very deepened financial integration in past decade and have more room for leverage. However, these integrations have also witnessed various turmoil in the economy in the form of Russian- Brazil debt crisis, Asian financial crisis, Dot com bubble, Argentina debt crisis, Global financial crisis and many more since 1990s. Due to the existence of globalization, these crises have impacted not only a particular economy but has extended its scope.

The previous studies have found the credit growth and asset prices as a leading indicator. Some studies also showed that the foreign exchange reserve, broad money supply and trade as variables were also statistically significant. These studies have used either the Signal Extraction Approach used by Kaminsky and Reinhart (1999) which used a threshold value as a limit for multiple variables beyond which a crisis signal is given. Bucharest Stock Exchange (BSE)in Romania (Brezinski and Stephen, 2011) lost its attractiveness among foreign investors. However, there are not enough resources which focuses on Asian economies or India. The emergence of novel technologies, including artificial intelligence (AI), blockchain, cloud computing, big data analytics, machine learning (ML), internet of things (IoT), and 3-D printing, commonly known as Industrial Revolution 4.0 (IR4.0), offers developing nations like India both significant opportunities and challenges. IR4.0 technologies, sometimes referred to as the “digital revolution,” have numerous uses in the industries of services, industry, and agriculture.

Introduction

AI plays a key role in management effectiveness and adaptation, especially in small and medium enterprises (SMEs) facing evolving markets (Lemos S. I. C., et al., 2022). Designing AI-based management frameworks enhances companies’ economic activities through data analysis and informed decision making (Emine, Kalender., 2023). Academic research shows a growing interest in AI in management, with a focus on effective adoption practices and potential areas for development (Jbid, Arsenyan., Anke, Piepenbrink. 2023). Artificial intelligence (AI) significantly influences decision-making processes in management by enhancing rationality, efficiency, and fairness. AI, through machine learning and big data analysis, reduces managerial bounded rationality, enabling a shift from satisficing to optimizing decision making modes (Shtepa, S., et. al., 2023). In human resource management AI-enabled interview analysis improves candidate selection by uncovering hidden patterns and mitigating biases, leading to more objective decisions (Rajneesh, Panwar. 2023). Additionally, by increasing productivity, efficacy and creativity, the incorporation of AI into business strategy transforms decision-making; nonetheless, ethical considerations and human oversight are essential for responsible implementation (Gonesh, C., et. al., 2023).

Introduction

The finance sector is undergoing a profound transformation driven by technological advancements, with artificial intelligence (AI) and blockchain technology at the lead of innovation. AI, characterized by its capacity to analyze vast datasets, recognize patterns, and take data-driven decisions has revolutionized various industries, including finance. On the other hand blockchain technology, initially popularized by cryptocurrencies like Bitcoin has arose as a disruptive force in finance, offering decentralized, transparent and secure transactional systems.

The integration of AI and blockchain technology holds immense promise for the finance sector, offering new avenues for efficiency, transparency, and risk management. By combining the analytical power of AI with the security and transparency of blockchain, financial institutions can streamline operations improve customer experiences, and mitigate risks more effectively. However realizing the full potential of this integration requires a nuanced understanding of its complexities, challenges, and opportunities.

Introduction

The phrase “sustainable” has been used so much in recent years-and frequently so imprecisely—that for many people, it has become meaningless or confused, more of a rhetorical chant than a source of useful advice. Lester Brown, the founder of the World Watch Institute, defined a sustainable society as one that can meet its requirements without compromising the opportunities for future generations. This definition is the one that we come across most frequently. Brown wrote this description in the early 1980s. The Brundtland Report, a 1987 United Nations report, addressed this issue and defined sustainable development as development that satisfies present demands without jeopardising the ability of future generations to satiate their own. Despite their obvious limitations speaking simply of human wants, for example, without reference to the natural world-these are significant moral exhortations. They do serve as a helpful reminder that it is our duty to provide our kids and grandkids with an equally abundant planet as we did, but they offer no guidance on how to truly create a sustainable society. When air, water, and minerals interact with living things to fill a specific region, it is referred to as an ecosystem (Tansley, 1935). This word was first used in 1935. Before then, the main goals of land management had been to provide a single item, like food or lumber, or to conserve a single, iconic species.

Introduction

A payment gateway is a server that is located between the web server of the merchant and the web server of the acquiring bank. When a merchant receives an order, it is transferred to the payment gateway by the merchant server.

The payments sector in India is set to reach USD 100 trillion in volume and USD 50 trillion in transactional value by the end of 2030. The payment gateways, which are also a part of the payments sector are set to reach USD 1.21 billion in India, growing at a CAGR of 17.6% between 2024-2029. The global payment gateway landscape as of 2024 stands at USD 16.27 billion growing at a CAGR of 16.43%. Payment gateways across the world are credit card-based gateways, with the introduction of card readers in mobile phones. The Fintech and payments sector boom in India cannot be justified without mentioning UPI (Unified Payments Interface), which has encompassed payment solutions for small street vendors to large conglomerates with its user-friendly interface and QR-code-based payments. UPI is one of the major payment gateways in India, in addition to IMPS (Immediate Payment Service), RTGS (Real Time Gross Settlement), and NEFT (National Electronic Funds Transfer) and many more.

Introduction

A revolution is coming to the more than a century-old automobile industry. Electric vehicles (EVs) are viewed as a solution to a variety of problems, including reducing greenhouse gas emissions, fostering sustainable economic growth, improving air quality, and reducing the importation of fossil fuels. The demand for electric automobiles in India is rising. Incentives and programs have been launched by the federal and state governments to promote the usage of electric vehicles, and there are also regulations and standards in place. A consumer is a person or group that plans to purchase something, actually does so, or utilizes it for primarily social, family, domestic, or other non business or entrepreneurial activity-related objectives.

An asset that is an important criterion for evaluating the status of the consumer economy can be used to estimate consumer savings.

The concept of electric cars has been around for a while, but in the past ten years, it has gained a lot of attention due to the expanding carbon footprint and other environmental implications of fuel-powered cars. In contrast to an internal combustion engine, which generates power by burning a mixture of fuel and gases, an electric vehicle (EV) is propelled by an electric motor. When electricity was one of the favored sources of motor vehicle power in the late 19th century, electric vehicles (EVs) first made their appearance, providing a degree of comfort and usability that gasoline cars of the day were unable to equal.

Previous Year Questions

1. What is food security? Why is it essential? Describe the current food security system in India. (10M, CSE 2022)

2. What is food security? Indicate the major constraints in food procurement and distribution. (10M, CSE 2017)

Difference Between Production and Productivity

• Production is total quantity produced in an area.

• Productivity is the quantity produced in one unit area.

• For example, total area is 10 hectares and the quantity produced is 15000 kg.

Previous Year Questions

1. Discuss the iron and protein malnutrition in India. Describe the dietary pattern for mitigating the above-said malnutrition. (15M, IFoS-2022)

2. What is protein energy malnutrition? Give micronutrient deficiency in women and children. (20M, CSE 2017)

3. How are vitamins classified? Name the blood clotting vitamin. Give its various functions and sources. (10M, 150W, CSE 2019)

4. What do you mean by nutrient deficiency? Explain the micro-nutrient deficiency in context of work capacity of women and children. (12.5M, CSE 2015)

Previous Year Questions

1. Differentiate between protective foods and energy foods. (10M, IFoS 2015)

2. What do you mean by ‘hunger’ and ‘hidden hunger’? Comment on various strategies in combating hunger. (15M, CSE 2021)

3. Classify foods on the basis of their function (5M, 2023)

Previous Year Questions

1. What are the different ‘National Dietary Guidelines’ of India? What is your opinion on balanced diet of Indians? (20M, CSE 2021)

2. Classify foods on the basis of their function. Write down a balanced diet for a sedentary man & woman in India (10M, CS 2023)

National Dietary Guidelines

Recommended dietary allowance (RDA) is defined as “the nutrient present in the diet which satisfies the daily requirement of nearly all individuals in a population”.

Previous Year Questions

1. Discuss the strengths and weaknesses of Public Food Distribution system in India. (8M, IFoS-2022)

2. Describe the vicious cycle of poverty and enlist the existing policy instruments for combating malnutrition. (20M, CSE 2022)

3. Enlist and explain the efforts of the GOI in streamlining the current levels of poverty (10 marks, CS 2022)

4. Enlist and explain the salient features of Public Distribution System as per the provisions of the National Food Security Act, 2013. (10M, CSE 2022)

5. Discuss the strengths and weaknesses of public food-grain distribution system in India. (8M, IFoS 2020)

Previous Year Questions

1. No questions were asked in past 10 years

Availability of Foodgrains

• Net availability of foodgrains = Gross Production (-) seed, feed & wastage, (-) exports imports (+/-) change in stocks.

Previous Year Questions

1. Write down the constraints of fruit processing industry in India (10M, CSE 2023)

2. Enlist & Explain the issues & challenges in the growth of food processing industry in India (10M, CSE 2022)

3. Give a detailed account of constraints in sustaining the food-grain production in India. (10M, IFoS 2020)

4. What are the major constraints of food production in India (10 mark, CSE 2020)

5. Describe the food production constraints in Indian agricultural system. (15M, IFoS-2018)

6. Indicate the major constraints in food procurement and distribution. (10M, CSE 2017)

7. Discuss the procurement and processing constraints of food grain. (15M, IFoS-2017)

Previous Year Questions

1. Write down the various programmes run by state & central govts for ensuring food security in our country. Briefly discuss the strategies for sustainable agricultural production (20M, CSE 2023)

2. How will national food policy help in sustainable food security? (CSE 2020, 10 marks)

3. Discuss the national initiatives for food and nutrition security. (8M, IFoS-2019)

4. What are the special schemes for providing food grains to underprivileged, destitutes and malnourished populations (CSE 2016, 12.5 marks)

5. National and international food policies. (10M, IFoS-2016)

Previous Year Questions

1. Discuss the reasons for food grain surplus (7.5 marks, CSE 2019)

2. What are the reasons for good grain surplus in India? Write the steps taken by the government to save the food grains. (12.5M, CSE 2015)

Reason for Grain Surplus

Green Revolution

• Introduction of high yielding varieties of Rice and Wheat

• Green Revolution was made possible through the use of

Previous Year Questions

1. CS 2015: Discuss the impact of rapid increase in population on food and agriculture (12.5 marks)

What is Food security?

The Food and Agricultural Organization (FAO) – “when all people at all times have physical and economic access to sufficient, safe and nutritious food to meet their dietary needs and food preferences for an active and healthy life”.

It has Following Dimensions

• Availability: It means food production within the country, food imports and the stock stored in government granaries.

Previous Year Questions
1. How has food production, incuding  horticultural crops, changed in  India during the last one decade? Write down its impact on food and nutritional security (20 marks, CSE 2023)
2. What is the food production and consumption pattern of India in the last fifty years? (15M, CSE 2021) 
3. Describe food grain production and consumption trends in India (7.5M, CSE 2019) 
4. Food production and consumption trends in India (12.5M, CSE 2015)

A database is a collection of data, typically describing the activities of one or more related organisations. Biological database is meant for storing and organising biological data, collecting information from life sciences, scientific experiments, computational analysis and contents from published literatures.

Biological databases play a vital role in bioinformatics, since it helps varieties of researchers to access and analyse data from different parts of the world. The knowledge obtained helps to solve disease oriented and environment related issues and to make vital policy decisions. Biological databases remain as tool to identify insect species as well as in comparing their relationships with related species. The biological knowledge distributed among different general and specialised databases, can be retrieved with efficient queries.

Various information like genomics, proteomics, metabolomics, microarray, genes expression, phylogenetics etc. are also stored and maintained as biological databases in terms of genome databases, protein structure databases, taxonomic database etc. These computerised web databases facilitate effective data management and analysis help in knowledge transmission, make coherent information exchange between the studies and public audience.

Insects are present everywhere. In the world where life is possible, insects can be found. To begin with, it is not necessary to go to distant places. Insects can be collected from garden, cultivated fields or even in our house. For the collection of insects, we must have some idea of the following:

1. How they live.

2. What they require at different stages of life.

A. Complete Metamorphosis

There are four stages

Hybridisation

Hybridisation occurs when two species cross to form a new species. If they are named before their hybrid nature has become apparent, the names would become invalid as soon as the hybridity of the specimen has been established.

Hybridism can be of three types:

1. Sympatric Hybridisation

When individuals in two regions overlap in an otherwise well-defined species, they are called sympatric hybridisation. This is common in bird of paradise and humming birds. This hybridisation is listed as cross of two parental species.

The study of science is incomplete, if it is not made available to others. The most popular way to make the work wide circulated is through publication. It is most important for taxonomists that what is most needed in the world about their work, is to be published in time. In taxonomy, the publications may be in the form of book, pamphlets, journals, articles, scientific papers, symposium chapters, catalogues and check-lists etc. A taxonomist should publish his work in appropriate journals like population studies or biological studies etc. It should be mentioned in the title if the new research is published. Sometimes, new taxa of one country are included in the faunal work of another country without mention in the title about the country. This is bound to remain out of knowledge for future works. The major types of taxonomic publications are as follows:

Publication of New Taxa

These are ordinary descriptive papers of new taxa such as subspecies and genera. Such descriptions do not involve careful comparison of all related species. The published descriptions may be justified only when the names of the taxa are required for biological or economic works. These are also helpful in revision of any work where the new species can be easily fitted in the classification. In addition to this, short communications concerning with new records of various taxa are also quite useful.

Palaeontology is the science that deals with the life past geological periods. It is just the opposite of neontology which deals with the systematics of recent organisms. Fossils are the remains or parts of animal or plants that lived thousands of years ago which have turned into rock. They are preserved traces of living organisms in the earth crust.

Fossil History of Insects

Occurrence of insects in fossilised state provides a clear idea of the main order of insects. The oldest known insects’ fossil appears to be of Apterygota and blattids seem to be the oldest living insects, which have almost lived without undergoing large scale evolutionary changes from the Carboniferous to the present day.

Some of the earliest apterygotes might have developed from the Permian or lower Carboniferous. The richest collection of fossil insects is from the vegetable deposits like coal, lignite peats and in amber entangled in the raisin. Most of the insects are known only through their wings, resulting in the study of venation. However, the amber fossil of Miocene presents a clearer picture. The best known epoch is undoubtedly the Permian while Triassic provides richest discoveries of the insect fossils.

Classification involves grouping organisms into a series of hierarchical categories. Actual method of establishing a classification consists in defining groups or categories on a hierarchical scale. The animals can be classified in a taxonomic hierarchy consisting of a series of categories of ascending ranks from species to kingdom. Each of these categories includes one or more groups from the next lower level. Therefore, all animals can be classified in a taxonomic hierarchy consisting a series of categories in ascending order from species to kingdom such as species, genus, family, order, class, phylum and kingdom.

According to the history of biological classification, Aristotle, a Greek philosopher classified different animals based on the habitat, characteristics etc. Later Swedish botanist Carolus Linnaeus introduced taxonomic hierarchy categories during 18th century and this system is followed globally.

Taxonomic hierarchy refers to the sequence of categories in increasing or decreasing order. Kingdom is the highest rank and species is the lowest rank in hierarchy. Taxonomic hierarchy is the process of arranging various organisms into successive levels of biological classification. Each of the level of the hierarchy is called taxonomic category or rank.

Code of Ethics was observed by International Commission on Zoological Nomenclature for renaming of homonyms. The commission had issues declarations or opinions from time to time, which together constitute the start of a code of ethics in the field of nomenclature. The first Declaration of the international Commission was as follows:

• When it is noticed by any zoologist that the generic name or specific name published by any living author as new is in reality a homonym, and therefore unavailable under articles 34 and 36 of the Rules of Nomenclature, the action is for said person to notify the said author of the facts, and give said author an opportunity to propose a substitute name.

• When the author of a newly discovered generic or specific homonym is dead, the discoverer is free to rename the genus or species as he likes. It is common practice to rename the category after the author of the homonym. However, this practice was never universal and it is not a matter of ethics.

Taxonomic keys are devices that make identification of taxa easy and enjoyable. A key is presentation of appropriate diagnostic characters in a series of attractive choices to facilitate identification of a specimen.

Key characters are the characters that are easily perceived of very low variability, usually present in preserved material. They are useful as convenient labels for distinguishing taxa by the process of classification. Many taxonomic characters such as chemical, chromosomal, physiological and behavioural may have high value of identification because they are inaccessible in preserved material. Ideal key characters apply equally to all individuals of the population. They are absolute, relatively constant and external so that they can be observed directly without special equipment.

Identification of the insects is the integral part of systematics. It utilises all taxonomic procedures. The collections in the world are the accumulation of past taxonomists and are stored for the future identifications. Every species name is based on a published description or figure and usually on the type specimen. Identification is the association of other specimens with the appropriate description or with type specimen.

The steps in identification are as follows:

1. Preliminary key to orders and families

2. Key to genera and species if recent monographs or faunal works are available

3. Reference to the most recent catalogues

4. Reference to current bibliographies for literature published subsequent to the most recent catalogues

5. Reference to original descriptions

6. Comparison with the authentic described specimens or with types.

Super Species

According to Mayr (1931, 1942) Linsleg and Usinger (1953),“A super Species is a monophyletic group of very closely related and largely or entirely allopatric Species”.

Super species are, in other words, groups of populations that seem on other grounds (morphology, ecology etc.) to have passed beyond the point of potential interbreeding and to have acquired separate evolutionary roles. They are so by the more conclusive evidence of remaining separate when sympatric. It is to be assumed that they are still near the critical point of specification, near a definitive isolation. They are nascent species that will, if they survive, collectively form a subgenus or eventually a genus but have hardly get reached that degree of divergence.

They are not given special names; the Rules of Nomenclature make no provision for that usual designation and they are mostly known by the name of an included species and are in fact super species.

The Species Concept in Systematics

The modern biological or, more strictly, genetical concept of a species is a group of interbreeding organisms, genetically isolated from other such groups. The definition of the category species has long been one of the major problems of taxonomy. It is because of two reasons:

1. Because of the congruity of lingering typological concepts with the nature of evolving species.

2. Because evolutionary species may have changing characters and are only gradually differentiated in phylogeny.

For evolutionary study and descriptive purposes, many kinds of variation within species come in to consideration, but the only regular intra specific taxon is the subspecies. Subspecies are widely but not universally useful and their definitions yield to the same sort of criteria as for dividing other phylogentic continuance.

Man has tendency to classify everything in a hierarchical manner for example, ships may be classified on size, weight, what they carry or naval versus merchant or passenger. Any of these classification is as valid as any other.

But classification of biological species is unusual in that a predefined system exists i.e. the evolutionary relationships among organisms. But evolutionary relationships can only be estimated; and contrary to popular belief, even a good fossil record does not conclusively show the phylogeny or evolutionary tree of a group species.

Numerical taxonomy can be defined as “Numerical evaluation of the affinity or similarity between taxonomic units and ordering of these units into taxa on the basis of their affinities”. The term includes drawing of phylogenetic inferences from the data by statistical or other mathematical methods to the extent to which this should prove possible. The practice involves a number of fundamental assumptions and philosophical attitudes toward taxonomic work.

The science of taxonomy started with Linnaeus, who used two Latin names for an organism by a universally agreed name and identity, and further provided means for their realistic classification. These identifications and classifications got strengthened further with accumulated biological knowledge. In the recent times taxonomy has become more demanding subject for areas in entomology, which are dependent on access to accumulated experience and knowledge for their survival and development. By providing name and classifying an insect, taxonomy provides information on the nature of population, life cycle, habitat and behaviour and thereby developing hypotheses, concepts and theories, which are basic to biological control. In addition, it enables comparison and synthesis, all of which lead to refinement and fine tuning of the concepts and theories for identification.