eChapters

2.1 Introduction
As time passes, the customers are used to the practice of having short life for the products, which they buy and use. This is because of the globalization of business and growing global competition all over the world. The companies engaged in manufacturing and selling the products which are used at large volume by households and partly by industries, introduce new models to attract customers and to have more market share. This in turn has impacts on customer buying and retention decisions of those products. Though the companies introduce new models, they are expected to keep the prices of the products at affordable ranges so that there is always a larger customer segment to buy those products. This is like egg or chicken situation, because companies introduce many new models to out beat competition and in turn customers expect improved versions of those models in shorter time intervals. This forces companies to continuously engage themselves to come out with new models or improved models which have convergence of technologies to give maximal customer satisfaction. So, creativity in product design becomes an essential requirement of the product development teams of the companies.
2.2 Product Design
Product design aims to include the following tasks to solve the problems of the users or address expected needs of customers in a given market. These tasks are human intensive.
i. Understanding end-users’ requirements
ii. Imagining alternate product ideas to address their needs

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

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.

2.1. Open source satellite data

The satellite images have been invaluable information sources for Natural Resource Management. They provide synoptic view of the Earth periodically helping in inventory, surveying, and monitoring in agriculture, hydrology, geology, mineralogy, land cover, land use and environment. Free global satellite image data of spatial resolutions of 1m to 1 km and spectral resolution of single band to hyperspectral are available from an increasing number of sources (Table 2.1).

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.

2.1. INTRODUCTION

Many biological variables do not meet the assumptions of parametric statistical tests as they are not normally distributed, the variances are not homogeneous, or both. Objects in nature commonly exhibit one of three distributions:

Introduction

Work-dwelling or workplace well-being describes all aspects of the working lifespan of workers from the point of view of quality, safety and physical environment. It pronounces the workers’ feelings about their work, their working environment, the climate at work and work organization. According to WHO (2010), a healthy workplace environment not only merely finds the absence of illness but also a state of comprehensive physical, mental and social comfort. Work and health hence have a reciprocal relationship that concerns occupational health wellbeing and confers decent work environments i.e. (i) payment of reasonable income; (ii) safety-security in employment and working environment; (iii) guarantee of equal opportunities and treatment for all; (iv) enclosure of social protection for the workers and their families; (v) offering prospects for personal development and inspiring social integration and (vi) allowance of workers to express their apprehensions and to organize (ILO, 1999). In the entrepreneurial sphere, globalization along with fast industrial progress is supposed to be accountable for occupational health hazards and related workplace ill-being, work insecurity, the lack of protection for workers’ health and well-being in the form of work-related accidents, illness and stress, lack of workers’ compensation and paid sick leave, lack of maternity protection, excessive hours of work, etc.(https://www.ilo.org/). This was evidenced in the recent report of ILO (2023) where it was found that 2.3 million people around the world surrender to work-related accidents or diseases every year which links to over 6000 deaths every solitary day (www.ilo.org). Subsequently, there are about 340 million occupational accidents where 160 million are fatalities of work-related illnesses annually in the World.

7.1  Write True/False

Q.1    All the problem of statistical inference may be considered as the problem of “Decision making in the face of uncertainty.”
Q.2    The problem of point estimation is the problem if deciding which value in the “parameter space” Wq is the true value of the parameter.

Deep learning enables automated analysis of agricultural images for crop classification, disease detection, weed identification, yield prediction and soil monitoring. This chapter examines Convolutional Neural Networks, Recurrent Neural Networks and Vision Transformers applied to imagery collected by satellites, drones and ground-based sensors. These models automatically identify patterns and features that conventional methods may overlook. Integration with precision agriculture can improve decision-making, increase crop productivity and reduce resource waste. Challenges include shortages of annotated datasets, limited interpretability and high computational requirements. Transfer learning, data augmentation, edge computing and real-time processing are discussed as approaches for enabling practical deployment in remote and resource-constrained agricultural environments.

13.1. Introduction

The Delphi technique is a systematic, interactive forecasting method that involves gathering expert opinions to make informed decisions, predict future trends, or address complex problems. The core idea behind this technique is to use the knowledge and insights of experts in a structured manner to produce well-informed and reliable outcomes. By combining expert judgment with iterative feedback, the Delphi method allows for deeper analysis, greater accuracy, and a better understanding of complex issues that cannot be easily solved through traditional data analysis or quantitative methods (Ismail and Taliep, 2023).

Originally designed for defence-related forecasting, the Delphi technique has since evolved into a versatile tool used across a wide range of fields, such as business, healthcare, education, public policy, environmental science, and social research. Its versatility stems from its ability to gather expert opinions in a manner that allows for rigorous analysis of complex issues, without the constraints of traditional group meetings or face-to-face discussions.

True/False

Q.1.    Demographic data and vital statistics are important tools for researchers, epidemiologists, health planners and health professionals.
Q.2.    The distribution of people across different geographic regions is known as population density.

5.1. INTRODUCTION

Mean and standard deviation summarizes the salient features of a data set. But they do not describe adequately the full characteristics of the data. For an adequate description of the characteristics of a data set, a convenient method is to plot a frequency distribution curve. Such curve is constructed by first drawing a frequency polygon, which may then be smoothened to a frequency curve, indicating the distribution. The frequency curve or distribution of specific biological variables generally has a specific form and plays a very important role in statistical theory and practice. It is customary to describe a curve in terms of its symmetry or assymmetry and its flatness or peakedness.

The following types of distribution are commonly seen in the biological world:

3.1. INTRODUCTION

In this chapter, we will study the measures of central tendency which is a numerical method to explain the data in brief. We can see examples of summarizing a large set of data in day to day life like average marks obtained by students of a class in a test, average rainfall in an area, average production in a factory, average income of persons living in a locality or working in a firm etc.

One of the most important objectives of statistical analysis is to get one single value that describes the characteristic of the entire mass of data. Such a value is called the central value or an ‘average’ or the expected value of the variable. This single value is the point of location around which individual values cluster and, therefore, called the measure of location. Since this single value has a tendency to be somewhere at the center and within the range of all values it is also known as the measure of central tendency. The purpose of computing an average value for a set of observations is to obtain a single value which is representative of all the items.

The branch of statistics which describes the data is called descriptive statistics. After collection, the data are presented in form of tables, diagrams or graphs. This facilitates the further analysis of data. The most basic form of data analysis is descriptive analysis of data collected and presented for the study. The descriptive statistics summarises the data and helps to find the representative value of the entire data set and the variation of the other values in the data set around the representative value. The descriptive statistics includes the measures of central tendency or averages, measures of dispersion or scatteredness, measures of skewness and the measures of kurtosis. Before going to discuss the R codes for various measures of descriptive analysis, we would first discuss in brief about these measure. This will help in better understanding of the R codes.

Measures of central tendency or averages: They are the representative values of the data series. They give an idea about the concentration of the values in the central part of the distribution of the data. There are five types of averages. They are 1. Arithmetic Mean (A.M.) 2. Geometric mean (G.M.) 3. Harmonic Mean (H.M.) 4. Median 5. Mode.

4.1. INTRODUCTION

Dispersion is the extent to which values in a distribution differ from the average of the distribution. To quantify the extent of the variation, there are certain measures namely:

1. Range
2. Quartile Deviation
3. Mean Deviation
4. Standard Deviation

Range and Quartile deviation measure the dispersion by calculating the spread within which the values lie. Mean Deviation and Standard Deviation calculate the extent to which the values differ from the average.

Design of Experiment was first elaborated by R. A. Fisher (1935).

• Design of Experiment: Proper allotment of treatments to the available experimental units.

• Treatment: It is the factor whose effect is to be studied. Example: Application of drugs, feeds, conditions, methods, etc.

• Experimental units: The subjects or individuals upon which an experiment is conducted.

Example: Laboratory animals like mice, rat, monkeys, primates; livestock like cattle, buffalo, sheep, goat, poultry; plants, a plot of land, etc.

Necessity of designing an experiment

1. To get maximum information from the available resources.

2. To know the effect of treatment on experimental units and to make comparison between treatments.

3. To know whether the difference between the effect of two treatments is significant or not.

4. A well designed experiment has a well defined method of statistical analysis of data.

5. To minimize error in the whole experimental procedure.

6. An ill designed experiment is not likely to give proper estimates of parameters and comparisons between treatments with desired precision even if the method of data analysis is very good.

7. The design of experiment is essential to get all the important treatment effects to be compared independently.

In 1935, Prof. Ronald A. Fisher laid the foundation for the subject in his monumental work entitled “The Designs of Experiments”. Experimental designs concern the arranging of treatments in such a manner that the inferences and conclusions regarding the effects of these treatments can be easily done and their reliability measured. Experiments are made with a view to find the validity of a particular hypothesis and to have an idea about the extent of the reliability that can be placed on a particular conclusion arrived. The selection of the design will have a very great bearing on the accuracy of the ultimate results. By a random selection of experimental units it is possible to remove the ambiguity about the casual interpretation of the observed associations. Random sampling is the most essential ingredient of all experimental designs. Besides, there are many devices for increasing the precision of the inference and the calculations.

To understand design of experiments, let us first get acquainted with some important terms and concepts associated with design of experiments:

• Experiment: Experiment is a scientifically planned method. The experiment is conducted draw a valid conclusion about a particular problem. The conclusion is based on statistically observation.

• Treatment: The object or the procedures under comparison in an experiment are known as treatment. For Example: Fertilizers, varieties, cultivation practices, irrigation level in case of agricultural experiments and drugs, breeds, farms, etc. in case of animal experiments.

• Yield: The response of the treatment is measured by some indicator such as crop production, milk production, body temperature, etc. Such an indicator is called yield. The treatments are applied to some units such as field plots, sample of cows, sample of patients, etc. and the effect on the yield is observed.

• Experimental material: The experimental material is nothing but a set of experimental units. In other words, the material which is used in experiment is known as experimental material. For Example: A piece of land i.e. Agricultural field in agricultural experiments and a group of cattle i.e. animals in animal experiments.

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

An experiment is a systematic process or activity which leads to collection of information on certain objects to give an answer to the objectives of the researcher.

The experiment in which certain absolute values like average, correlation coefficient, median, mode, etc are worked out to describe the population is known as absolute experiment.

The experiment in which the effects of different objects under consideration are compared is known as comparative experiment.

The variable whose change we wish to study is called as Response variable. For example- yield, mortality rate of insects etc.

The variable whose effect on the response variable we wish to study is termed as Factor. For example - variety, fertilizer, insecticide etc.

Here factor is the independent variable and response variable is the dependent variable.

The various levels of a factor whose effect on the response variable we wish to study are known as Treatment.

True /False

Q.1.    A diagnostic test enables the doctors to reach at a decision whether the patient is suffering from a particular disease or not.
Q.2.    The recent developments in the field by biotechnology have led to the development of many new medical tests.
Q.3.    The general principles in designing and conduct diagnostic test studies are not known well.