eChapters

Practical case studies provide a direct connection between technical knowledge and the real-world challenges faced by ice cream manufacturers and managers. This chapter brings together applied situations involving formulation, overrun, texture, freezing, storage, cold-chain management, production losses, changeovers, product launches, market returns, profitability, and operational decision-making. Each case encourages readers to identify symptoms, determine probable causes, evaluate technical and commercial consequences, and develop corrective actions. The cases demonstrate that ice cream performance depends on the interaction of formulation, processing, quality, equipment, distribution, consumer expectations, and business strategy. The chapter is particularly valuable for students, technologists, entrepreneurs, managers, consultants, and industry professionals seeking practical problem-solving skills.

Pre-training and fine-tuning constitute two fundamental stages in the development of modern Large Language Models. This chapter provides a comprehensive examination of how LLMs acquire general language capabilities through large-scale pre-training and subsequently adapt those capabilities to specific tasks through fine-tuning. Pre-training uses extensive unlabeled datasets and self-supervised learning objectives to develop representations of language, context and knowledge. The chapter discusses Transformer-based architectures, data preparation, cleaning, tokenization and the importance of high-quality training corpora. It then examines fine-tuning approaches including Supervised Fine-Tuning, Reinforcement Learning from Human Feedback, Parameter-Efficient Fine-Tuning, LoRA, QLoRA, adapter tuning and soft prompting. Major challenges such as data quality, privacy, computational cost, catastrophic forgetting, overfitting, hallucination, bias and security vulnerabilities are addressed. The chapter also considers emerging approaches that allow models to adapt during inference and improve reasoning efficiency. Overall, it highlights the continuing need for training strategies that balance accuracy, adaptability, efficiency, safety

Precipitation is a vital component of the Earth’s water cycle, playing a crucial role in shaping our climate, weather, and ecosystems. It occurs when water vapor in the atmosphere condenses and falls to the ground as rain, snow, sleet, or hail. Precipitation is a key driver of weather patterns, influencing everything from local weather events to global climate trends.
In this chapter, we will explore the various forms of precipitation, including rain, snow, sleet, and hail, and examine the factors that influence their formation and distribution 
We will also discuss the importance of precipitation in sustaining life on Earth, from supporting plant growth and agriculture to shaping landscapes and ecosystems. Key topics to be discussed are:
• Forms of precipitation (rain, snow, sleet, hail)
• Precipitation processes (formation, distribution, intensity)
• Factors influencing precipitation (temperature, humidity, wind patterns)
• Importance of precipitation (ecosystems, agriculture, water resources)
• Precipitation measurement and prediction (methods, challenges, applications)

Precision agriculture includes information driven administration choices that further develop asset use productivity, bringing about decreased Agriculture expenses while bringing down the natural effects from agriculture. Consequently, information and information assortment frameworks, choice help devices, and information driven hardware and info changes are significant parts of Precision agriculture, participating in three vital farming advances: determination, direction, and performing separately. Before the coordination of shrewd innovations, ICT (data and correspondence innovation) was incorporated into farming devices and mechanical assembly to get certifiable data. Here, remote detecting, mechanized hardware and programming, telemetric, drones, autonomous vehicles, GPSs, and computerized developments were incorporated into cultivating rehearses. For example, the agro-tech association John Deere introduced GPSs for ranch farm haulers, anticipating extended yield and reduced input wastage. The previous status of accuracy agribusiness before brilliant cultivating can be summarized as follows.
Data Collection and Acquisition
Data, data collection, and decision support devices are critical for the distinctive verification and finish of alternate points of view in cultivating. In Accuracy agribusiness, data on individual fields and gathers are collected by seeing, assessing, and distinguishing with different sorts of sensors, yield and soil checking, and remote-detecting mechanical assemblies, such as imaging from drones, groups, airplane, or satellites. As such, “detecting” is a fundamental organization gadget of accuracy agribusiness, which is seeing distinct information and giving data on climatic circumstances, soil conditions, fertilizer necessities, water openness, nuisance and infection stresses, and other field limits. An extent of sensors is used in Accuracy farming. Biomass limits are huge in making decisions to screen the readiness and truly zeroing in on crops. Sensors for mass stream and dampness content are portions of yield screens, along with a differential global positioning system (DGPS) recipient. Appropriately aligned yield screens can produce precise constant

In this Chapter the precision farming/ Agriculture is discussed along with its steps of implementation. It also includes how artificial Intelligence plays role to perform precision farming/ agriculture in various way along with its scope, limitation and challenges.

Introduction

The primary source of livelihood is agriculture in India, where two thirds of India’s population, services and the private sector account for the remainder. Agricultural land accounts for about 43 percent of the geographical area of India. In earlier days, India was largely dependent in terms of food imports, but over the years, the country’s production of grain and seeds was self-sufficient, and this effort led to the formation of the Green Revolution. In fact, there was strong effort to achieve food self-sufficiency. This trend has continued up to now and continuous improvements have occurred. Precision agriculture or precision farming means the right thing to do, in the right way, on the right place and at the right time. Precision agriculture is expected to suit the agro-climate activities to improve application precision. Farming land has declined a bit in the last 40 years but the number of farmers has increased. According to the 2010–11 Agriculture Census, a total of 138.35 million operating holdings were estimated (single farmers) and 159,59 million hectares were covered by operations.

Precision agriculture is a management technique that collects, processes and analyzes temporary, spatial and individual data and integrates this with other information in order to support management decisions based on an estimated variability to enhance efficiency of use of agricultural production resources, productivity, quality, profitability and sustainability. Precision Agriculture manages every input of crop production (fertilizer, specifically found calestone, herbicide, seeds, insecticide, etc. Reduce waste, boost income and maintain efficiency Place. Precision Agriculture adapts soil and crop management carefully to suit the different field conditions.

Precision feeding aims to supply animals with appropriate nutrients according to their changing physiological state, growth, production and environment. Conventional feeding plans are often static and may lead to waste, increased costs and environmental losses. This chapter introduces an AI-enabled framework integrating animal data, feed composition, growth characteristics and external factors. Regression, ensemble learning and other machine-learning models predict feed intake and weight gain, while linear programming and metaheuristic optimization generate efficient allocation plans. A decision-support system provides recommendations in real time. The approach can improve feed conversion, nutrient utilization, profitability and sustainability while reducing overfeeding, resource wastage and the environmental impact of livestock production.

Abstract
 
The definition of what drought is and what drought is not, has profound implications for the environment and all segments of society, yet it may be different for each. Many attempts have been made to develop a comprehensive and meaningful definition. A generic definition provides a starting point:  Drought is a persistent and abnormal moisture deficiency having adverse impacts on vegetation, animals, and people. The public perceives “drought” as a serious departure from normal water conditions, a departure that requires a public response to reduce negative impacts. About two thirds of the geographic area of India receives low rainfall (less than 1000 mm), which is also characterized by uneven and erratic distributions.

Predictive analytics estimates future health risks and outcomes, while prescriptive analytics recommends appropriate interventions and resource-allocation strategies. This chapter explains how statistical models, machine learning and healthcare data can support disease-risk assessment, prognosis and personalized treatment. Applications involving cardiovascular disease, diabetes, cancer, chronic-disease management and early-warning systems are examined. The integration of clinical, genomic, lifestyle and electronic health-record data is discussed alongside survival analysis, disease-progression modelling and treatment-response prediction. The chapter also addresses bias, fairness, transparency, privacy, regulation and interoperability. Future developments involving real-time analytics, IoT-enabled monitoring and precision medicine illustrate the transition toward proactive, preventive and data-driven healthcare.

Predictive modelling of plant growth can help farmers and researchers improve crop management, resource utilization and production planning. This chapter examines machine learning approaches for predicting important plant growth characteristics. It introduces plant growth fundamentals and the environmental and management factors that influence development. The chapter covers the machine learning workflow, including data acquisition, preprocessing, feature engineering, model development and evaluation. Supervised learning, unsupervised learning and deep learning approaches are considered for predicting plant height, biomass, canopy development, leaf area and yield-related characteristics. Data from IoT devices, drones and satellite imagery can strengthen prediction models. The chapter also discusses precision agriculture applications, limitations and emerging technologies such as explainable AI, digital twins and edge AI.

Abstract

Real-time PCR, also known as qPCR, is a highly sensitive technique used to determine the real-time amplification of a target DNA sequence. However, the presence of secondary structures or spurious amplicons can distort the results. When using SYBR Green chemistry, the features of qPCR primers differ from those of standard endpoint PCR primers. As a result, designing primers for SYBR Green qPCR requires extra caution and attention. This chapter outlines the essential features and conditions for designing qPCR primers, including primer length, melting temperature, GC content, specificity, and avoiding primer-dimer formation.

Abstract

PCR primer design is a crucial skill for molecular biologists, as it enables the study of gene expression, gene amplification, and molecular markers. Primer3 is an online tool widely used for designing optimal PCR primers, which are pairs of short oligonucleotides that initiate DNA synthesis in vitro using a thermocycler. This tool uses a range of parameters to generate high- quality primers, including target sequence length, melting temperature, GC content, specificity, and product size. Understanding and effectively utilizing these parameters is essential for achieving accurate and reliable PCR results. In this chapter, the practical aspect of primer designing using the online tool Primer3 has been discussed. The parameters and the purpose of their usage have been given in a tabulated manner.

Preparation of base map being in mind, the forest, agriculture, water, road and boundary are almost same for all but theme may be different as per requirement. Base information is to survey and need terrain information, the preparation includes land forms by separation of water, kart area, coastal area. Meanwhile preparation of focus might be on DEM (Digital Elevation Model) and water movement helps in demarcation of boundaries on map.

4.1 Introduction
Companies that produce tangible items or offer services must drastically increase their productivity in order to maintain their operations in the face of intensifying global competition. A single quantum jump productivity enhancement technique or multiple concurrent incremental improvement productivity strategies can both be used to achieve this goal. If a business chooses the second choice, integrating various tactics for a significant increase in the total productivity would be quite difficult. The first alternative, a single productivity improvement plan, will make it possible for the business to increase productivity with the same or greater success. The only difficulty with this strategy is locating the probable problem area and then using process improvement techniques to achieve the anticipated dramatic increase in productivity. Business process reengineering refers to any significant adjustment of business processes that results in a dramatic increase in an organization’s productivity. Price, quality, after-sales support, adherence to delivery schedules, technological breakthroughs, etc. are some elements that influence market competition. Therefore, the business should focus its efforts on each of these areas in order to achieve notable improvements in terms of decreased operating costs and higher sales revenue from the sale of its goods and services to clients. Improved pricing for a company’s goods or services can provide it an advantage over competitions. Applying various cost-cutting strategies to lower variable costs and overhead costs will lower the price of the good or service. The other elements, viz. quality, after-sales support, delivery schedule adherence, and technological advancements are all directed in this direction

Let‘s dive deeper into the processes of condensation and explore each in more detail:
• Adiabatic Processes: Adiabatic cooling occurs when air rises and expands, reducing its temperature (Fig. 9.1). This process can lead to condensation and cloud formation. There are several ways air can rise and cool adiabatically:
• Orographic Lift: When air is forced to rise over a mountain or hill, it cools, and condensation occurs, resulting in clouds and precipitation. This is why mountainous regions often experience more rainfall than surrounding are as.
• Frontal Lift: When two air masses with different temperatures and humidity levels meet, the warmer air is forced to rise, cool,
and condense, forming clouds and precipitation. This can lead to significant weather changes, such as the formation of fronts and lowpressure systems.
• Convection: When the sun heats the ground, it warms the air closest to the surface, causing it to rise and cool, leading to condensation and cloud formation. This process can lead to the development of cumulus clouds, thunderstorms, and other types of convective weather.

Introduction

GIS (Geographical Information System) based mapping takes cogence of three major geometrical dimensions, point, line and polygons and in GIS these three features correlate their relationship through a concept called Topology. Among the various applications where covariate processing requires specific attention, the landuse/ land cover and Digital Elevation Models/surfaces or terrains are the major area of interest. Use and processing of data for these covariates carries importance as accuracy of both these aspects would influence the derivatives extracted out of them further. For example, if landuse is improperly defined, the site for which planning is proposed may not sustain, similarly, point data/elevation data in case of DEMs would decide the exact topography of any landforms for their further utilization. For their use in GIS, while DEMs depend upon point data, the landuse/ land cover will be taken care by polygons. For the purpose of the study, we are going to discuss about these two covariates in the following paragraphs.

Generally, Covariate (covariable) (koh-vair-iãt) in statistics is defined as a continuous variable that is not part of the main experimental manipulation but has an effect on the dependent variable. In other words, as per ANCOVA (Analysis of covariance) definition: a covariate is a control variable.

Profitability in the ice cream business depends on controlling costs and protecting value across the complete product journey. This chapter examines both backend and frontend profitability levers, including yield improvement, giveaway control, manufacturing efficiency, OEE, utilities, refrigeration, quality costs, procurement, channel economics, distributor performance, route profitability, freezer productivity, and return on investment. It emphasizes that high sales volume does not necessarily guarantee sustainable profitability. SKU economics, pricing discipline, working capital, cold-chain losses, stock ageing, and trade schemes must also be managed. The chapter presents a structured profitability-improvement approach that connects manufacturing discipline, commercial strategy, operational efficiency, and financial governance to stronger contribution, EBITDA, cash flow, and sustainable business growth.

Components of Project Finance
1. Capital Structure Determination: Deciding on the mix of debt, equity, and other sources of financing that will be used to fund the project. The goal is to structure the capital in a way that minimizes costs, maximizes returns, and balances risk. Here’s an elaborate breakdown of the steps involved in capital structure determination:
Understanding Financing Options
• Debt Financing: Borrowing funds that must be repaid with interest. Common sources include bank loans, bonds, and credit lines. Debt financing is often preferred for its tax benefits (interest payments are tax-deductible) and lower cost compared to equity. • Equity Financing: Raising capital by selling shares of the project to investors. This does not require repayment but dilutes ownership. Equity financing is useful for sharing risk and avoiding the burden of debt repayments.
• Hybrid Instruments: Combining features of both debt and equity, such as convertible bonds or preferred shares. These can provide flexibility and attract a broader range of investors.
• Other Sources: Grants, subsidies, and public-private partnerships (PPPs) can also contribute to project financing without adding to debt or diluting equity.

ABSTRACT

This paper is an attempt to draw the attention of readers of this volume to understand the importance of documentation and preservation of earth’s resources.  

Introduction

Earth has innumerable treasures that were and are being exploited by mankind. Geo-resources in the form of minerals, non-renewable hydrocarbons, paleontologic relics and related evidences indicate earth’s evolutionary history. Continuum of biological activity albeit varying rates of proliferation had resulted in accumulation of natural resources embedded in Earth and distributed at specific geographical niches. Biological perspective of earth history is significant in unraveling bio-geo-sphere interactions through geological time. Fossil records of invertebrates, vertebrates and plants are signals of major changes in earth’s evolutionary history. These constitute unparalleled windows to understand continental separation. Intercontinental and intra continental migration of living forms are also traced through fossil study.

Abstract

Proteins are complex molecules composed of one or more polypeptide chains made up of amino acid residues. These molecules perform a variety of essential functions in living organisms, including DNA replication, molecule transport, catalyzing metabolic processes, and providing structural support to cells. In this chapter, we explore the four levels of protein structure: primary, secondary, tertiary, and quaternary. Primary structure refers to the specific sequence of amino acids in a polypeptide chain, while secondary structure describes the folding of the chain into alpha-helices, beta-sheets, or other patterns. Tertiary structure is the three-dimensional shape of a single polypeptide chain, and quaternary structure refers to the interactions between multiple polypeptide chains to form a larger protein complex. Additionally, this chapter covers various methods used to predict protein structure, including homology modeling, ab initio prediction, and threading algorithms.

18.1 Library Introduction

This library contains modules for various types of functionalities. Some commonly use module of python standard library are:

(i) math module: which provides mathematical function to support difference types of calaculations.

(ii) cmath modules: which provides mathematical function for complex number.

(iii) random module: which provides function generation random number.

(iv) staticstics module: which provides mathematical satical function.

(v) urllib module: which provides URL handling function so that you can access websites from within your program.

1. NmPy library, provides some advance math functionalities along with tools to create and manipulate numeric arrays.

2. Scipy library, provides algorithmic and mathematical tools for scientific calculations.