Rings are algebraic structures that originated from the theory of algebraic integers. The concept was introduced by Richard Dedekind, taking inspiration from the algebraic structure of integers over complex numbers. Rings were first formalized as a generalization of Dedekind domains that occur in number theory, and of polynomial rings and rings of invariants that occur in algebraic geometry and invariant theory. The concept of ring is an extension of groups and has a wide range of applications in mathematics and computer science. Rings are applied in the study of geometric objects, topology, cryptography and various other branches of algebra.

In the vast arena of mathematics, the emergence of ring theory as a pivotal branch of algebra owes much to the visionary insights of two more extraordinary minds: David Hilbert and Emmy Noether. At the beginning of the 20th century, Hilbert provided a unifying framework for understanding sets of numbers endowed with specific algebraic properties. Concurrently, Noether's seminal contributions, notably expounded in her 1921 paper “Ideal Theory in Rings”, brought forth profound advancements in the understanding of commutative rings, laying a robust theoretical foundation for subsequent explorations. Despite encountering formidable obstacles, including entrenched gender biases and political upheavals, Hilbert and Noether remained steadfast in their pursuit of mathematical truth.

Introduction

Humans have had a lengthy history of understanding electricity and magnetism. The tangible characteristics of light have also been studied. But in contrast to optics, electricity and magnetism—now known as electromagnetics—have been believed to be governed by different physical laws. This makes sense because optical physics as it was previously understood by humans differs significantly from the physics of electricity and magnetism. For instance, the ancient Greeks and Asians were aware of lode stone between 600 and 400 BC. Since 200 BC, China has been using the compass. The Greeks described static energy as early as 400 BC. But these oddities had no real effect until the invention of telegraphy. The voltaic cell or galvanic cell was created by Luigi Galvani and Alesandro Volta in the late 1700s, which led to the development of telegraphy. It quickly became clear that information could be transmitted using just two wires attached to a voltaic cell. The development of telegraphy was therefore prompted by this potential by the early 1800s. To learn more about the characteristics of electricity and magnetism, Andre-Marie Ampere (1823) and Michael Faraday (1838) conducted tests. Ampere's law and Faraday's law are consequently called after them. In order to comprehend telegraphy better, Kirchhoff voltage and current rules were also established in 1845. The data transmission mechanism was not well comprehended despite these laws. The cause of the data transmission signal's distortion was unknown. The ideal signal would alternate between ones and zeros, but the digital signal quickly lost its shape along a data transmission line.

A1.1 Basic matrix operations

A1.1.1 Definition (matrix)

A matrix is an ordered rectangular array of numbers (or functions). The numbers (or functions) are called the elements or the entries of the matrix.

A1.1.2 Dimension/size of a matrix

A matrix having m rows and a columns is a matrix of dimension m x n.

The political failure of community is the background against which a range of post-Marxist European philosophers have sought to rethink what community could be. This chapter focuses in particular on Jean-Luc Nancy, Roberto Esposito, and Giorgio Agamben, who have made substantial contributions to what we might call a new philosophy of community. Nancy, Esposito, and Agamben ask how community, not least because of its promise of solidarity, can continue to serve a political purpose, despite the violence of the Nazi Volksgemeinschaft, which appears to be the logical endpoint of any conception of community. But Nancy, Esposito, and Agamben also respond to the failure of community’s presumed revolutionary potential to become a site of resistance against both capitalism and the modern state. Should community be conceived in the plural (Nancy), as a gift economy (Esposito), or as a coming community of stateless refugees (Agamben)? Such attempts to save community come at a considerable cost, both philosophically and politically, since they make community irrelevant for a normative theory of democracy.

This chapter addresses the second main challenge to Kant’s conception of autonomy: the sense that the opposition between nature and freedom renders the actuality of freedom unintelligible. Turning to the Inaugural Dissertation and the three critiques, the second chapter shows that tackling the problem requires us to first overcome a widespread misunderstanding of Kant’s notion of the intelligible world. The intelligible world is not a given world available to theoretical cognition but initially accessible only through practical cognition as a world that ought to be. The chapter develops a new interpretation of Kant’s use of the principle “ought implies can” to show how the moral self-consciousness of the ought provides the realm of freedom with a first degree of actuality that, however, remains insufficient on its own. For freedom to be truly realized, we have to realize our freedom in the natural world by endowing it with a different purposive form. The third Critique offers unrecognized resources to explain how such a realization may be possible. By means of its account of natural purposiveness and the feeling of life, it redescribes external and subjective nature in such a manner that we can see how freedom may take root in them. By means of its account of fine, Kant specifies the general form of processes through which we can transform given nature and produce a second nature expressive of ideas. The chapter closes by considering why Kant did not fully develop these resources and why freedom remains ultimately unreal in his own account, something especially obvious in his discussion of the highest good.

We are familiar with the concept of connectedness of a graph and how it decides whether we can traverse the graph, starting from any vertex and reaching another vertex. It is possible for a connected graph to lose its connectedness by the removal of a single vertex. There are also other connected graphs in which the removal of several vertices does not disconnect the graph. Hence we have stumbled upon a parameter that can give a “sense” of the connectedness of a graph. It may answer important questions about the nature of its connectedness. In this chapter, we will deal with connectivity, which measures the connectedness of a graph using some specific parameters.

CHANCE PERMEATES OUR physical and mental universe. While the role of chance in human lives has had a longer history, starting with the more authoritative influence of the nobility, the more rationally sound theory of probability and statistics has come into practice in diverse areas of science and engineering starting from the early to mid-twentieth century. Practical applications of statistical theories proliferated to such an extent in the previous century that the American government-sponsored RAND corporation published a 600-page book that wholly consisted of a random number table and a table of standard normal deviates. One of the primary objectives of this book was to enable a computer-simulated approximate solution of an exact but unsolvable problem by a procedure known as the Monte Carlo method devised by Fermi, von Neumann, and Ulam in the 1930s–40s.

Statistical methods are the mainstay of conducting modern scientific experiments. One such experimental paradigm is known as a randomized control trial, which is widely used in a variety of fields such as psychology, drug verification, testing the efficacy of vaccines, agricultural sciences, and demography. These statistical experiments require sophisticated sampling techniques in order to nullify experimental biases. With the explosion of information in the modern era, the need to develop advanced and accurate predictive capabilities has grown manifold. This has led to the emergence of modern artificial intelligence (AI) technologies. Further, climate change has become a reality of modern civilization. Accurate prediction of weather and climatic patterns relies on sophisticated AI and statistical techniques. It is impossible to think of a modern economy and social life without the influence and role of chance, and hence without the influence of technological interventions based on statistical principles. We must begin this journey by learning the foundational tenets of probability and statistics.

In addition to different dynamics in different bundles of trajectories (competition and prestige or wealth differentiation are strongly developed in some, cooperation in others), a few commonalities crosscut multiple bundles. Complexification almost always occurs in conditions of demographic growth, although the population levels vary enormously. Residential density regularly plays an important role in shaping interaction patterns related to productive differentiation, integration of local economies, and wealth accumulation on the one hand or attenuated interaction, and ritual and prestige differentiation, on the other. Such forces operate in the same way in all parts of the world, overriding supposedly typical cultural patterns.

EMPIRICAL TECHNIQUES rely on abstracting meaning from observable phenomena by constructing relationships between different observations. This process of abstraction is facilitated by appropriate measurements (experiments), suitable organization of data generated by measurements, and, finally, rigorous analysis of the data. The latter is a functional exercise that synthesizes information (data) and theory (model) and enables prediction of hitherto unobserved phenomena.1 It is important to underscore that a good theory (model) that explains a certain phenomenon well by appealing to a set of laws and conditions is expected to be a good candidate for predicting the same using reliable data. For example, a good model for the weight of a normal human being is w = m * h, where w and h refer to weight and height of the person, and m can be set to unity if appropriate units are chosen. A rational explanation of such a formula for weight based on anatomical considerations is perhaps very reasonable. From an empirical standpoint, if we collect height and weight data of normal humans, we will notice that a linear model of the form w = m * h represents the data reasonably well and may be used to predict the weight of the person based on the height of the person. This fact ascertains a functional symmetry between explanation and prediction. Therefore, a good predictive model must automatically be able to explain the data (and related events) well.

Take a map of your country where the different regions, provinces, or states are clearly shown. How many colors are required to color each region on a map of the country so that the neighboring regions are colored differently? As a fun exercise, let us create a graph by placing a vertex in the middle of each region, and two vertices are adjacent if the states they represent share a border. Now that we have modeled the regions of a map as vertices of a graph, the graph’s vertices can be colored in the same way that the regions are colored on the map. The graph’s vertices cannot be colored the same when they are adjacent as the neighboring regions cannot have the same color. This interesting problem of coloring has been the source of a great amount of research in graph theory. The applications of graph coloring are numerous, since it is a powerful tool that enables the systematic assignment of resources in situations where certain elements must be distinct or non-overlapping. We will also discuss chromatic number of a graph, which is the minimum number of colors required to have a conflict-free coloring. The parallel concept of edge coloring is essential to frequency assignment in networks, where edges need distinct colors to prevent interference. In this chapter, we will also explore the connection between planarity and coloring, through the five color theorem and four color theorem.

Color is a psycho-physiological property of humanvisual experiences when the eyes look at objects andlight. Color is not a physical property of thoseobjects or light, rather, it is the result of aninteraction between physical light in theenvironment and human visual system (Palmer, 1999).For processing color images, it is required todevelop an understanding on how colors arerepresented following human perception.

3.1 Light sources

A broad range of electromagnetic spectrum, shown inFig. 3.1, consists of electromagnetic waves rangingfrom very long wavelengths at radio waves to veryhigh frequency at gamma waves. A very narrowinterval in this spectrum, toward the higher end ofspectral frequencies, accounts for the visible raysand it is called the visiblespectrum. The light and colors that ahuman eye perceives relate to the frequencies ofwaves that fall under the visible spectrum. Apictorial representation of the correspondence ofwavelengths in the visible range of the spectrum todifferent perceived colors has been shown in Fig.3.1. There are seven distinguishable colors in thefigure, violet, indigo, blue, green, yellow, orange,and red, usually known in order of their increasingwavelengths by the acronym of VIBGYOR. The luminancesensitivity function that is shown as a curve inFig. 3.1 is a function of the wavelength. It isempirically observed that the sensitivity of thehuman visual system is maximum in the green zone ofthe visible spectrum. The luminance sensitivityfunction gradually decays toward violet (higherfrequencies) and red (lower frequencies) from thegreen zone, as shown in the figure by the whitecurve.

Operational Amplifiers: Introduction

An operational amplifier (op-amp) is a very prominent active device used in analog integrated circuit (IC) design. Prominence is due to the widespread and diverse areas of applications of the op-amps as its parameters are very close to ideal in a certain range of operating frequencies. Apart from basic arithmetic operations such as addition, multiplication, and integration, op-amps are also widely employed as amplifiers, wave shaping circuits, active filters, log/anti-logarithmic amplifiers, nonlinear function generators, and in analog-to digital and digital-to-analog conversion, and so on.

Figure 8.1(a) shows a pin connection diagram of the most commonly used type-741 op-amp; it needs a dual power supply, has two terminals for inverting and non-inverting inputs, one terminal for the output, and three terminals without any connections for simple applications. Dual op-amps and quad op-amp ICs with matching characteristics are also available.

Op-amp is essentially a high-gain differential amplifier (DA) that can be shown in its simplest form as represented in Figure 8.1(b). The output voltage of the op-amp is the difference between the two input voltages multiplied by the high-gain factor A, so the output voltage is expressed as:

The differential gain A is frequency dependent in a practical op-amp. Therefore, as a first approximation, it is represented by a single-pole roll-off model given below.

We have discussed the traversal of graphs and now it is time to learn about yet another parameter, that will help to visualize and represent a graph even better. We are familiar with the order and size of a graph which are basic parameters of a graph, and we are aware about the connectedness of a graph. Closely related to the concept of connectedness, is the question of how “connected” a graph is. The answer to that question comes from the degree of a graph which gives an idea as to how densely or sparsely connected a graph can be. We will introduce the reader to algorithms and complexity analysis. The question of whether a degree sequence can be realized into a graph, is settled by the Havel–Hakimi algorithm, which will be covered in detail. The reader is also introduced to the process of identifying whether two graphs are alike, through the concept of isomorphism. An alternate method of representing graphs, apart from drawing them, using the adjacency matrix will be explained in this chapter.

Key Concepts

• • The growing share of electricity in the energy sector

• • The connection of electricity and global warming

• • Important terms related to electricity

• • Conventional sources of electricity generation

• • Green and renewable sources of electricity generation

• • Smart grid

Introduction

Electricity is the fundamental driver for growth of the modern society. The availability of reliable electric supply is a priority for any residential, industrial, or commercial setup. With the rapid proliferation of digital appliances and the critical role they are playing in our daily life, the dependence on high-quality electric power supply has further increased manifold.

Electricity started as a source of energy for lighting, replacing oil and gas-based lamps. But at that time very few people would have realized that slowly this new source of energy will ‘capture’ the whole residential, industrial, and workplace setup. It is difficult to imagine our lives without electricity now – starting from heating our meals, washing and drying of our clothes, heating the water, keeping the house or office cool or hot to running all kinds of entertainment and communication appliances. This source of energy has turned into an omnipresent phenomenon in our lives. Electricity is the main driver behind technologies related to the Internet and communication also. A major part of the railways is already running on electricity, and the transition of road transport is also imminent in the near future.