eChapters

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).

Being polytocous animal, sow give birth several (8-14) piglets in each farrowing, mother pig find several deficiencies and disorders during gestation, farrowing and postnatal conditions. Prior to birth intrauterine competition amongst the feotus piglets for nutrition and gas exchange is a big stress for abortion. Several infectious and non-infectious causes may hamper proper growth and pathological conditions resulting mortality (15-20%) in piglets than weaner and adults (Sarma, et al, 2012). Several risk factors are also responsible for piglet mortality.

Prenatal factors: Insults during pregnancy can impact a piglet’s ability to survive after birth and regulate their body temperature. Some maternal aspects that affect survability and mortality amongst piglets are prolong farrowing duration more than 4 hours, number of parity of sow, usually first and second parity show high mortality than subsequent parities and also older sow lose more piglets.

Introduction

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

Punjab, known as the “breadbasket of India,” relies heavily on livestock, particularly cattle and buffalo, for its agricultural prosperity. These animals play a crucial role in the region’s dairy industry, contributing significantly to milk production and rural livelihoods. However, like all livestock, cattle and buffalo in Punjab are susceptible to a range of diseases that can impact their health, productivity, and overall well-being. Timely and accurate diagnosis is essential for effective treatment and prevention. This article explores some of the most common diseases affecting cattle and buffalo in Punjab, along with the diagnostic procedures used to detect and manage these health issues, ensuring the sustainability and growth of the dairy sector.

Hydatidosis

Hydatidosis is a zoonotic disease caused by metacestode stages of parasite Echinococcus granulosus and is recognized as a major public health problem worldwide. This tapeworm is found in small intestine of the canid (definitive host) where it produce proglottids containing eggs. Upon shedding through faeces the later are ingested by the cattle, buffaloes and other intermediate hosts including man. These hatch in their intestine and the oncosphere invade circulatory system and lodge in various organs (liver, lung, brain etc.) where they develop into large thick walled unilocular cavity called hydatid cyst, which expands and can reach very large size and buds hundreds and thousands of protoscoleces endogenously. In a survey on hydatid cysts, a prevalence rate of around 5% has been recorded in more than 4000 meat samples of different animals in Punjab.

AMR is undoubtedly the most serious challenge ever faced by the world health community because of its multifaceted complex nature and its overwhelming impact over the human and veterinary communities. Livestock sector is no exception to have the serious and devastating impact from the emergence and spread of AMR. Because of a steady shift towards intensive farming and increased stocking density there has been increase occurrence of infectious diseases. Growing world population, rapid urbanization, and deforestation, shrinkage of grazing land all aided to this process. This leads to increased usage of antimicrobials in food animals – cause of concern for the scientific community as AMU is known the single most important driver of AMR.

The Central Nervous System not only consists of the Brain but also involves the Spinal cord.

The spinal cord is responsible for conveying information from the Brain to the rest of the body via the peripheral nervous system. The spinal cord can be divided into 4 functional segments.

1. C1-C5 (Upper Cervical Segments)

2. C6-T2 (Brachial Plexus)

3. T3-L3 (Thoraco - Lumbar)

4. L4-S3 (Lumbo-Sacral Plexus )

It is important to note that the motor and sensory function of the pelvic limbs are not only controlled by the Lumbo-Sacral plexus but also involve the Thoraco Lumbar segments (T3-L3) of the spinal cord. It is important to understand not only the neuroanatomy, but also the functional anatomy before approaching a neurological patient.

Cytauxzoonosis is an emerging tick-borne disease of domestic cats and wild felids caused by the protozoa of the genus Cytauxzoon. The common clinical findings in affected cats are similar to other hemoparasitic infections namely anorexia, depression, anemia, vomiting, icterus, and high fever. The first reporting of feline cytauxzoonosis with four fatal cases was done by Wagner in Missouri, USA in the year 1976.

Bobcats (Lynx rufus) are recognized as the natural wild reservoir of Cytauxzoon felis with prevalence reaching up to 100% in some enzootic areas (Zieman et al., 2017). This infection is highly fatal in domestic cats with 97% mortality without treatment (Meinkoth and Kocan, 2005). However, subclinical infection in some cats has also been evidenced i.e., they were infected with C? felis but did not display the acute clinical signs of the disease before diagnosis (Brown et al., 2010 and Rizzi et al., 2015).

Several other wild felids apart from bobcats and domestic cats have been found to be affected by fatal or nonfatal Cytauxzoon sp. infection. These include lions, jaguar, cougars, pumas, ocelots, Iberian lynx, meerkats, European wild cats and Asiatic wild cat, panthers, and tigers (Jakob and Wesemeier, 1996; Rotstein et al., 1999; Yabsley et al., 2006; Andre et al., 2009; Filoni et al., 2012 and Furtado et al., 2017).

Cardiomyopathies in acts can be classified broadly as Hypertrophic cardiomyopathy and non-hypertrophic cardiomyopathies. Usually, no alarming clinical signs are detected during routine examination in the preclinical stage except cardiac murmurs in some cats with HCM. This is because, that the cats are sedentary and excellent in hiding the signs of illness from the pet parents. Hence the onset is often acute with the sudden appearance of the clinical signs linked to congestive heart failure like severe dyspnoea, cardiogenic shock, aortic thromboembolism, arrhythmias and even syncope and death

Cats with CHF will be presented with severe dyspnoea, due to fulminant pulmonary oedema and or pleural effusion. The pleural effusion associated with CHF is usually a modified transudate, some cases it may be chylous .In echocardiography ,the size of the left atrium should be determined .If the LA size is normal ,then the dyspnoea may not be of cardiac origin .The presence of B-lines in the lung area in association with LA enlargement confirms cardiogenic pulmonary oedema. Stress, anaesthesia, I/V fluids and steroids leads to decompensation and fulminant pulmonary oedema. Thoracocentesis is will be helpful if there is dyspnoea due to significant pleural effusion. At least 300ml of effusion can be usually removed. The dyspnoeic cat is very fragile and should be handled carefully. Butorphanol may be given for anxiolytic effect. Oxygen administration is beneficial. Furosemide when given intravenously has an initial venodilator effect and should be repeated as required monitoring the sleeping respiratory rate (SRR). Addition of nitro-glycerine (vasodilator) reduces preload, decreases left atrial pressure and help in reducing pulmonary oedema. If the clinical signs are suggestive of cardiogenic shock or myocardial failure, Pimobendan I/V or Dobutamine CRI should be considered.

Tissue Doppler Imaging

Doppler echocardiography relies on detection of the shift in frequency of ultrasound signals reflected from moving objects. With this principle, conventional Doppler techniques assess the velocity of blood flow by measuring high-frequency, low amplitude signals from small, fast-moving blood cells. In TDI, the same Doppler principles are used to quantify the higher-amplitude, lower-velocity signals of myocardial tissue motion.

There are important limitations to TD interrogation. As with all Doppler techniques, TDI measures only the vector of motion that is parallel to the direction of the ultrasound beam. In addition, TDI measures absolute tissue velocity and is unable to discriminate passive motion (related to translation or tethering) from active motion (fiber shortening or lengthening). The emerging technology of Doppler strain imaging provides a means to differentiate true contractility from passive myocardial motion by looking at relative changes in tissue velocity.

Precision medicine and personalised treatment of systemic or tumour diseases gain their acceptance and application in veterinary care, too. The application of personalised medical protocols and companion diagnostics is easier and ethically well reasoned within the veterinary medical field – and it can offer many advantageous lessons to human clinical transformation, too [1]. In vitro companion diagnostics for testing the applicability and sensitivity levels of certain kinase inhibitor and immunotherapeutic drugs offer a greatly increased efficiency for veterinary cancer patients, too. As the understanding of cancer biology has advanced, the field of veterinary oncology has grown significantly, paralleling developments in human oncology. Precision medicine is an innovative approach to disease treatment and prevention that considers individual variability in genes, environment, and lifestyle. In the context of veterinary medicine, precision medicine aims to provide tailored therapies that are specifically designed for the unique genetic and molecular profile of each animal’s cancer. This approach moves away from the traditional “one-size-fits-all” model, instead focusing on targeted treatments that maximize efficacy while minimizing side effects. Companion diagnostics, such as molecular tests, help determine whether a dog or cat is likely to respond to a particular drug. For dogs and cats, precision medicine holds great promise in improving outcomes for cancers, which are among the leading causes of death in companion animals.

Introduction

One of the remarkable advancements in veterinary medicine recently is the application of interventional cardiology techniques to treat various congenital heart diseases. In dogs and cats, congenital heart diseases can be treated much more easily and with fewer complications using minimally invasive interventional approaches, rather than through traditional open-heart surgery. Among congenital heart diseases, treatment options are available for conditions such as PDA (Patent Ductus Arteriosus), AS (Aortic Stenosis), PS (Pulmonic Stenosis), VSD (Ventricular Septal Defect), ASD (Atrial Septal Defect), extrahepatic shunt, and stem cell applications. These diseases often cannot be significantly improved by medical treatment alone, making the use of the aforementioned interventional techniques essential for effective management.

Renal failure is one of the most commonly presented conditions in veterinary clinics world over. Although the potential for recovery is favorable, most animals die due to multisystemic effects induced by azotemia. Hence, extracorporeal removal of endogenous and exogenous toxins is very essential for expanding the window of recovery. Hemodialysis has been used in treatment of veterinary patients for the last 50 years and is still one of the most expensive and technically demanding treatment modalities in veterinary medicine. It is an advanced extracorporeal renal replacement therapy which has recently transitioned from a clinical curiosity to a more advanced standard for management of acute kidney injury (AKI) and chronic kidney disease (CKD) in companion animals particularly dogs and cats.

In addition to eliminating selective toxins that cause AKI, regardless of renal function, hemodialysis is also effective in the removal of pharmacokinetically active soluble uremic substances in CKD to enhance life expectancy and quality of life (Singh, 2021). Other common indications of hemodialysis in veterinary medicine include fluid overload, dysregulated body fluid composition, drug overdose or intoxications (Foster, 2020; Singh et al?, 2024). Medical management alone is often insufficient to control the clinical signs and metabolic abnormalities associated with uremia when AKI is severe or when there is involvement of acute on chronic kidney disease. Consequently, animals are at risk of death within a short therapeutic window, despite the potential reversibility of AKI and the improvement of life expectancy in CKD.

As the heart diseases in companion animals is becoming common as in humans, the research in canine cardiac diseases is one of the most extensive studies in the present scenario. As per the available literature, the overall occurrence of various cardiac abnormalities ranges from 0.37 percent to 1.67 percent in various parts of the country. Whereas, in western parts of the globe, nearly 8 million dogs suffer from heart disease accounting 10% of all dogs in the United States. Heart diseases are common among older dogs; up to 75% of senior dogs have some type of heart condition, and unfortunately, most go undiagnosed due to varied reasons. The recognition of canine cardiac diseases has been delayed, and ignored on account of lack of awareness and knowledge by the owner and inadequate diagnostic facility in our country. Physical examination, radiography, electrocardiography and echocardiography are most useful diagnostic procedures employed in diagnosing the cardiac diseases in dogs. However, majority of the acquired cardiac diseases are being managed medically with one or a combination of drugs. Improved diet management and health care of companion animals have led to increased life expectancy, as a result, cardiac diseases have become pivotal causes of death for both dogs and cats. For such reason, a major objective has become to increase the quality of life by reducing the expansion of cardiac diseases via early detection of diseases and provision of effective treatment.

Introduction

Canine atopic dermatitis (CAD) is a chronic inflammatory skin disease marked by itching (pruritus), redness (erythema), and recurrent infections. While traditionally considered an immune-mediated condition, recent studies highlight the significant role of skin barrier dysfunction in its development. A key aspect of CAD is the compromised skin barrier, which increases permeability to allergens, irritants, and microorganisms, thereby worsening the disease. The skin barrier is a complex, dynamic structure essential for protecting dogs from environmental challenges. The stratum corneum—the outermost layer of the epidermis—serves as a crucial defense against environmental insults, allergens, and pathogens. In dogs with atopic dermatitis, this barrier often becomes impaired, leading to increased transepidermal water loss (TEWL), greater allergen penetration, and secondary infections. This dysfunction both results from and contributes to the condition, creating a self-perpetuating cycle of barrier disruption, inflammation, and immune dysregulation.

Learning objectives

1. Recognise inflammatory cell types

2. Recognise common skin pathogens

3. Diagnostic test are best for each case

4. Gain experience in interpretation

5. Recognise important artefacts

″The most crucial step towards healing is having the right diagnosis″. While approaching a case, a clinician should follow appropriate steps in a systematic way for accurate diagnosis of dermatological disorders. A careful history is a first step to choose appropriate diagnostic tests.

Asiatic Elephant (Elephas maximus) is the national heritage animal of India. This particular species now exists in 13 Asian countries including India. As per 2017 census, India is home to an estimated figure of 29,964 elephants that is >60% wild population of the globe. Once distributed across the country, now they are squeezed to 14 states grouped in four regions ie., southern (14,612), north-eastern (10,139), east-central (3,128) and northern regions (2,085). There are 2675 captive elephants across different states under both private and government custody. This largest terrestrial mammal is protected under Seclude I species of Indian Wild Life Protection Act, 1972.

Both central and state Government are implementing short- and long-term programmes for health and well-being of captive as well as free-range elephants. The Central Government has established an ‘Elephant Cell’ at the Wildlife Institute of India (WII), Dehradun to provide technical inputs to the ministry for strengthening the efforts towards conservation of elephants. Add-on, the ‘Project Elephant’ was launched in 1991-92 in the Ministry of Environment, Forests and Climate Change to restore the elephant habitats and reduce their sufferings. Elephant habitats are notified as Elephant Reserves that are basic management units for elephant conservation and managements. A total of 33 Elephant Reserves has been notified in the country. A book titled, ‘Caring for elephants: Managing health and welfare in captivity’ is edited for field officers, veterinary professionals and elephant handlers.

Introduction

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

Introduction

Mithun (Bos frontalis), or ‘Gayal’ or ‘Eso’ or ‘Sial’ or ‘Sandong’ or ‘Wei’, is a rare bovine species, exclusively found in the northeastern hill region (NEHR) of India at an altitude of 300-3000 m. It plays an important role in the socio-economic and cultural life of indigenous tribes of Arunachal Pradesh, Nagaland, Mizoram and Manipur and is aptly referred to as the ‘Pride of Northeast India’ (Mukherjee et al. 2019). Besides India, mithun population is distributed to China, Bangladesh, Thailand, Bhutan, Myanmar, and Malaysia. The Indian mithun is a medium- to large-sized majestic ruminant with a characteristic jet-black body, an ash-colored forehead, and ‘white stockings’ on all the limbs. This unique animal has a special preference for salt feeding, is a skilled browser even in steep, hilly terrain, and does not need pasture land like cattle. Mithun is primarily reared under a free range system as an important source of lean meat, rich in amino acids, proteins, PUFA, etc. On September 1, 2023, the Food Safety and Standards Authority of India (FSSAI) officially certified the mithun as a food animal (Joshi et al., 2021; Lalchamliani et al., 2024; Ramesh et al., 2024). Compared to milk from other species, mithun milk has much larger quantities of fat, lactose, solids-not fat (SNF), calcium, important vitamins A, D, and E, essential amino acids, and polyunsaturated fatty acids (Devi et al., 2023).