eChapters

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. 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. 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.

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.

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

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

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

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

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.

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

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.

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

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

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

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

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

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

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

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

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.

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 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.

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

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

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

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 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

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

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

Chemical exposure in the workplace is a prevalent concern across various industries, including manufacturing, agriculture, healthcare, and construction. These exposures can range from acute to chronic and involve many substances, from heavy metals and solvents to pesticides and industrial chemicals. While the physical health effects of chemical exposure are well-documented, the psychological impacts are less understood but equally significant. Psychological stress is a response to perceived threats or challenges characterized by physical, emotional, and cognitive symptoms. Theories such as the Transactional Model of Stress and Coping by Lazarus and Folkman emphasize the role of individual perception and coping mechanisms in stress experiences.

The modern industrial landscape is characterized by ubiquitous chemicals, ranging from benign substances to potentially hazardous compounds. As industries strive for higher productivity and efficiency, the workforce is frequently exposed to various chemicals. This exposure is not without consequences; while the physical health effects of chemical exposure have been extensively studied, the psychological impacts are less understood. Emerging research indicates that chemical exposure can significantly contribute to psychological stress, affecting worker health, productivity, and overall well being.

Introduction

The rapid growth and socio-economic upliftment of a developing country are directly related to the intensity of industrial growth of that country (Bastian et al., 2019; Kniivila, n.d). The emergence of new enterprises is the main source of a healthy and growing industrial ecosystem. They represent 99% of businesses in developing countries and account for two-thirds of employment creation (Secretary-General and UNCTAD, 2022). Enterprises hence create employment, drive inclusive growth, promote innovation, and improve socio cultural conditions along with addressing sustainable development goals- No poverty(SDG-1), Industry, innovation and infrastructure(SDG-9), Reduce inequality(SDG-10)of the UN’s 2030 agenda (Forbes, 2022; Secretary-General and UNCTAD, 2022). Their innovative outputs in the form of goods and services, address local as well as global requirements of the customer market and bring them on the path of sustainability (Szopik-Depczynska et al.,2018; Rivera et al.,2020). The entrepreneurs have a long vision of success, engaging their primary activities with No poverty (SDG-1), Zero hunger(SDG-2), Gender equality(SDGs-5), Decent work and economic growth(SDG-8), Industry, innovation and infrastructure(SDG-9), Reduce inequality(SDG-10), Responsible consumption and production(SDG-12), Climate action(SDG-13), Partnership for goals(SDG-17) and addressing challenges of society and the environment in return for optimal economic profit (Alagpuria,2021; Lammers et al.,2022; Secretary-General and UNCTAD,2022; Vig,2021; Del-Aguila Arcentales et al.,2022; Sadiq et al.,2022).

Human life on the earth has been intertwined with the fabric of crops and livestock for its sustenance, since time immemorial, for food and protection against the vagaries of nature. When agricultural and livestock production systems began to change in the 1950s, from traditional extensive systems to semi-intensive and intensive production systems, diseases of rural farm enterprises became challenging. Especially, the emerging diseases in livestock continued to demand more and more investments for ensuring food safety and security. Despite significant progress made in the recent past in reducing the prevalence of animal diseases, there is still an increasing concern over the losses associated with diseases of economic importance that result in production inefficiency. The scientific foundation for the discipline of animal health economics was laid by Morris (1969) and further steps were taken by Ellis (1972) to economically evaluate the Swine Fever eradication programme in Great Britain using cost-benefit analysis, Martin Hugh-Jones (1975) to specialize in GIS to model diseases and assessed the economic impact on production by infectious diseases, James and Ellis (1978) to use the same tool for FMD control programme, Renkema and Stelwagen (1979) to evaluate the replacement rates in dairy herds, Renkema (1980) to analyze the economic aspects of diseases in animals, Ellis and Putt (1981) to suggest the epidemiological and economic implications of FMD vaccination programme in Kenya, and McInerney (1983) to analyze the economics of animal diseases in dairy cattle, the multidisciplinary field of animal health economics was not amply exploited especially in developing countries to assess the extent of livestock disease losses (Ngategize and Kaneene, 1985).

Statistics is defined as a discipline that includes procedures and techniques used to collect, process, and analyze numerical data to make inferences about the data. The word statistic is used as a singular, denoting the numerical data. Statistics are the numerical quantities calculated from sample observations. Here, the word statistics hence is plural. Basically, statistics is defined as the statistical data (in the plural sense) and also as statistical methods (in the singular sense). A clearer and more precise definition is that ‘statistics is the study of methods and procedures for collecting, classifying, presenting, analyzing, and interpreting data to make scientific inferences from it’. Bio statistics is the science dealing with the application of statistical methods to the problems of biological sciences.

2.1 FUNCTIONS OF STATISTICS

a) To present the facts in a definite form: If the data or results are given in numbers, rather than in qualitative statements, they will be more useful. The statements like ‘there is a considerable amount of unemployment in a country or the human population in a state is increasing at a faster rate’ are not in definite forms. The statements should be in definite form like there is a 15 percent unemployment in 2023 or the population is increasing by 5 percent every decade.

3.1 MEASURES OF CENTRAL TENDENCY (AVERAGES)

When data are presented in raw form or in the form of frequency distribution, they fail to provide a general idea of the data under study. The general idea of statistical methods is to condense the raw data into a form that can convey some meaningful characteristic(s) of the data. Though classification simplifies the data and improves the understanding of the main characteristics in the data, it is still difficult to remember. In any distribution of data, values of a variable may tend to cluster (or concentrate) around a central value. The data must be condensed into a single value. Such presentation of data is called a Central Tendency and the measures devised to consider this tendency are known as Measures of Central Tendency.

An average condenses a frequency distribution or raw data and presents it in a single representative number. It is that single value which is considered as the most representative or typical value for a set of values. Such a value usually lies somewhere near the centre of the group. That is why, an average is usually referred to as a measure of central tendency. It is located at a point around which most of the other values tend to cluster and therefore it is also termed as a measure of central location.

For any statistical analysis, the required primary data are usually collected by the census or sampling methods. In the census method, the data are collected from all the individuals. E.g., If we want to study the weight of 100 cows, we have to make use of all 100 cows for data collection. That is, we collect data from the entire population or universe. However, in the sampling method, the data are collected from a small group of the population which is termed as the sample. A sample is a portion of the population selected to represent the population. E.g., we collect data only from a sample of cows, say 20 cows drawn from the population.

Before trying to understand Hypothesis Testing and Experimental Designs, we should be conversant with the concept of Probability Distribution.

5.1 PROBABILITY DISTRIBUTIONS

Let us look at an example to grasp the essence of a probability distribution. Let us rotate a pair of dice and observe the sum by totalling the numbers that turn upon in both the dice. Let us call this sum by a random variable X. When the first dice shows up number 1, the second dice could show 1, 2, 3, 4, 5, or 6. Likewise, when the first dice shows up number 2, the second dice could show 1, 2, 3, 4, 5, or 6. If we continue the experiment this way until all the possibilities are exhausted, the sample space will contain 36 elements. The distribution that can be observed will contain a sum = 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12. The starting sum will be = 2, because when both the dice show the number 1, the sum 1 + 1 = 2. Likewise, the maximum sum in the distribution will be 12, when both the dice show up a number 6. If we write the values of the random variable (sum) with associated probabilities, it is called a probability distribution.

A scientific experiment is a planned research to obtain new facts or to confirm or refute the results of previous experiments. Research is usually conducted in a controlled environment to study the effect of one or more variables on observations. The objective of research may be to answer a question, verify a hypothesis, or estimate the effect of an independent variable on a dependent variable. After stating the research problem, we need to outline the methodology (or materials and methods) of the research, which includes developing an experimental design.

Scientific experiments are conducted to study the causal relationships between independent variable(s) and dependent variable(s). The researcher manipulates one or more independent variables and measures their effect on one or more dependent variables. Designing an experiment is planning an experiment. It is a complete sequence of steps taken before experimenting to ensure that appropriate data are obtained to perform a data analysis to get valid inferences about the problem. It must be seen that the data collected are relevant to the problem investigated. E.g., studying the effect of breed, age, feed, etc. on milk output. Here, the objects like breed, age, feed, etc. are called treatments. That is, any specific experimental conditions (like the addition of feed additives, new feed formula, etc.) applied to the experimental units are called treatments. The basic objects on which the experiment is done are called experimental units (E.g., a bird, an insect, an animal). However, the responses in all the experimental units receiving the same treatments may not be similar, due to the inherent differences in the experimental units, called experimental error, caused by extraneous factors.

Veterinary epidemiology is the study of disease within a livestock population, dealing with the investigation (including identification, quantification, and examination), of the direct and indirect determinants of disease distribution in animal populations. Any epidemiological investigation starts with whether there is an animal health problem in a population, which requires data collection on animal, management, economic, and environmental factors. These data are then analyzed descriptively to assess the frequency of disease occurrence. The results are then used to decide whether investigations of causal relationships between disease occurrence and potential risk factors are necessary for the development of control or prevention policies. Once the relationships are understood, necessary interventions for the disease are then planned and their impacts assessed. Hence, the goal of epidemiological investigations is the control and prevention of animal disease problems by integrating the available pieces of evidence into the decision-making process for disease control policy development.

8.1 NEED FOR ANIMAL DISEASE FREQUENCY MEASURES

The very essence of veterinary epidemiology is to measure animal disease occurrence and to make comparisons between the animal population groups. The animal disease frequency measures help us to understand the distribution of diseases in animal populations. Hence, the principal role of veterinary epidemiology is to describe and explain differences in the distribution of animal diseases between the populations.

Measures of the disease frequencies are used to describe how common an illness is concerning the size of the population (the population at risk) at a particular point in time. The major reason for determining the morbidity is to assess the extent of a disease problem in a population. This assessment is possible only when the number of diseased animals is calculated and compared with the total number of animals in the population. A report of 10 cases of mastitis in a dairy herd does not indicate the true extent of the disease problem, unless the report also indicates the total number of dairy animals in the herd or population. Thus, it is important to relate the number of cases of a disease to the population at risk of developing that disease.

9.1 ECONOMIC LOSSES DUE TO ANIMAL DISEASES

Economic considerations are increasingly forcing their way forward in the decision-making processes of animal health management. With the continuing intensification of livestock production systems, the economic implications of livestock diseases have become increasingly important at both farm and national levels, as they represent avoidable waste of scarce resources (McInerney, 1988). From an economic point of view, an animal disease either destroys the basic resources, reduces the attainable output, dwindles the efficiency with which the resources are converted into products, or decreases the economic value of products (McInerney, 1995).

The effects of a disease present in an animal or herd may be manifested by stunted growth, lowered production, delayed maturity, delayed conception, mortality, reduced resource (especially feed) use efficiency, etc. These negative impacts of a disease demand more resource(s) than usual, resulting in higher expenditures for the same output and reduced farm income. Apart from these negative effects at the farm or herd level, animal diseases lead to economic losses at the regional, national, and global levels too by increasing animal mortality, reducing productivity, increasing disease control costs, swelling losses in animal trade, decreasing the market values of livestock and their products, and ultimately ending at food insecurity. These economic impacts of livestock diseases, besides their social impacts, have now been recognized globally, in both developed and developing countries, necessitating quantification of the economic impact of animal diseases as an urgent and important step to support the prevention and control decisions for improved animal health to ensure better rural and global economy.

After assessing the prevalence or incidence of the disease concerned, the next step is to quantify the economic losses due to that disease by classifying and presenting all the information available on the losses, expressing these in monetary values, and identifying and quantifying the indirect losses attributable to a disease, to the extent possible. Usually, most of the negative effects of diseases can be conveniently estimated in terms of reduced output, mortality, reduced weight, delayed maturity, and treatment cost, directly. However, quantifying the indirect effects of the disease will be complex, but it is not impossible.

As we saw earlier, the economic impacts of animal diseases can be measured using statistical / epidemiological methods and economic methods. Economic methods of disease loss estimation include the Gross Margin Analysis, Enterprise Budgeting, Equi-Marginal Principle, Partial Budgeting, Cost Benefit Analysis, and Decision Analysis.

An animal disease is an economic process in the farming system, as it consumes a considerable amount of scarce economic resources that would have been otherwise productively utilized by the animals and results in negative benefits to society. Hence, while measuring the losses attributable to animal diseases, the manifestation of such losses and the factors influencing such losses may also be identified, which require modelling the ways by which the negative effects of the diseases are exhibited. A description of such models and their procedures is essential to understand the comprehensive effects of diseases. We will list and describe such models and tools in this chapter.

11.1 CORRELATION ANALYSIS

While simple descriptive statistical tools are adequate to estimate the economic effects of animal diseases, these tools cannot measure the nature and strength of the relationships between two or more variables. However, the statistical knowledge can effectively be used to understand Various statistical measures studied so far, like measures of central tendency and dispersion and skewness, relate to one variable only. However, there are many situations where there are several problems involving the use of two or more variables. Although correlation analysis is only a bivariate technique like t-test, ANOVA, and Chi square test, we will briefly see about correlation in this chapter, as it is also a part of multivariate analysis. The first step is to determine which bivariate analytical tool we need to adopt to test the study hypothesis.

Programming techniques are used the mathematical tools by the scientists, economists, and planners to specify the optimum combination of resources and enterprises in farms and firms, to identify the profit maximizing mixes of commodities to be produced, to specify the cost minimizing methods of production and transportation of goods, to identify the least cost locations from which raw materials can be purchased, to determine the locations of production at which a particular commodity should be produced and the consumption centres to which it should be shipped, and to specify the least cost ingredients of a feed or ration which must meet the specified nutritional requirements. Programming techniques can be either linear or non-linear. Although several non-linear programming techniques (like quadratic, integer, separable, dynamic programming, etc., which are very complex to work with) are available, we will largely deal with linear programming technique and to a certain extent will brief the theory of dynamic programming.

There are three types of disease occurrence namely, endemic, epidemic and outbreaks. Martin et al. (1993) described the term endemic as the disease occurrence in a predictable pattern. There can be low, moderate or high levels of endemicity. Although many times the terms epidemic and outbreak are used synonymously, the term epidemic is used when there is an unexpected incidence of a disease. Thrusfield (1995) defined an outbreak as the occurrence of a disease involving one or more animals.

Animal disease prevention and control activities offer valuable benefits for food production, food security and safety, animal and human welfare, and alleviation of poverty. The effectiveness of disease prevention and control policies depends on the good governance and quality of the veterinary services, in compliance with OIE standards and guidelines on animal disease control. The apex body in each country is the core system that takes care of the prevention and control of animal diseases. This body is responsible for early detection of diseases and rapid response to outbreaks. The veterinary services in developing countries require adequate human and financial resources and capacity building to carry out the tasks of disease prevention and control and to protect animal health and thus public health, including food security and food safety.

14.1 NEED FOR ANIMAL DISEASE INFORMATION SYSTEMS

Once the animal disease data are gathered, a few things need to be done with the data so collected. First, they must be managed and quality checked; Secondly, they must be analyzed to be understandable; and thirdly, they must be acted upon. For data to be analyzable and useful for decision-making, they need cautious management and quality control. An information system is the collection of data, people, procedures, hardware, software, files, and data required to accomplish an organized set of functions.

The constituents of an information system are:

• People – those who gather data, those responsible for data input and those responsible for data analysis

• A storage / retrieval / analysis system – which consists of computer hardware and appropriate software

• A feedback delivery system – mechanisms to ensure that processed data are fed back to data gatherers

Introduction

In the last decade, digitalization has transformed every aspect of life and society, changing almost every area of our day-to day lives. Not just that; the rate of change that is happening is astonishing and only going to accelerate further. From the way, we work and communicate to how we organize our lives, digital tools and technologies are everywhere including the veterinary sector. Veterinarians are recognizing the need to depart “the analog island” and increasingly willing to embrace digital solutions. Undoubtedly, digitalization is crucial when it comes to boosting efficiency and quality in veterinary medicine and delivery of animal healthcare to masses across various geographies and resource poor setups. Uniquely these digital tools helps vets also to focus on the needs of patients and animal owners.

Veterinary oncology is an upcoming field in recent years due to the integration of the importance of spontaneous canine cancers in understanding and developing novel strategies for human cancer management. We are stepping towards a very exciting era in veterinary oncology, where comparative oncology has played a significant role for many decades in understanding the molecular basis of cancer in companion animals. The comparative animal models have generated remarkable interest in both academia and industry. Studies have demonstrated the utility of comparative oncology in understanding drug resistance and tumor biology, contributing to the translational research[1]. This has yielded breakthrough discoveries, from advanced diagnostics to novel therapies, providing opportunities to improve the longevity and quality of life of the pets. These inputs have led to advancements such as the first conditionally approved chemotherapeutic agent for lymphoma, novel BRAF testing for canine urothelial carcinoma, tyrosine kinase inhibitors for mast cell tumors, updates in stereotactic/FLASH radiation, and novel immunotherapeutics. As the understanding of cancer biology has advanced, the field of veterinary oncology has grown significantly, paralleling developments in human oncology. Veterinary oncologists treat a variety of cancers in domestic animals, particularly dogs and cats, but also in exotic animals, horses, and even zoo animals. While the types of cancers in animals share similarities with human cancers, treatment strategies can differ significantly due to species-specific differences in anatomy, physiology, and response to therapies [2, 3].

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.

Rabies is an ancient, dreadful disease known to mankind from the vedic ages. It is a zoonotic disease caused by Rabies virus, genus Lyssavirus family Rhabdoviridae, affecting all mammals including man. The WHO categorizes rabies as a neglected zoonotic disease that causes around 59,000 human deaths globally annually after onset of symptoms. It is a vaccine preventable disease reported from over 150 countries and territories barring the island nations like Australia, Brunei, Japan, Maldives, New Zealand, Papua New Guinea, Singapore, Timor Leste and the Pacific Island Countries. The Indian islands of Andaman Nicobar and Lakshadweep are rabies free so far.

On the other hand, Africa and Asia has the highest burden of human rabies including South-East Asia: Bangladesh, Bhutan, DPRK, India, Indonesia, Myanmar, Nepal, Sri Lanka, and Thailand. The Asia Pacific region having 60% of the world’s population includes the world’s most populous countries – India and China. Asia alone shares almost half of the global rabies deaths annually.

Veterinary Nuclear Medicine (VNM)

Veterinary Nuclear Medicine (VNM) is a specialized field that uses radioisotopes to detect, diagnose, and treat diseases. Gamma scintigraphy involves injecting radioactive tracer pharmaceuticals into the animal’s body and imaging them as they move through the body using a gamma radiation-sensitive camera. This technique provides a 2D or 3D image of the whole body or a specific organ, allowing for the estimation of the quantitative distribution within the region of interest (ROI). Advancements in gamma scintigraphy have allowed for the acquisition of information about both static and dynamic high-resolution images, such as heart wall motion, blood flow, and myocardial perfusion. Unlike other diagnostic modalities viz X-rays, ultrasonography, and CT; gamma scintigraphy offers functional information of specific organs. The most commonly used gamma radiation-emitting isotope is 99m-Technetium (99mTc), which emits a gamma energy of 98% 140-keV photon with only 10% internal conversion. Other radionuclides used in veterinary nuclear medicine facilities include iodine (123I and 131I), Gallium (67Ga), and Thallium (201Ti).