eChapters

Human communication rarely depends on a single information modality. People simultaneously interpret language, images, sounds, gestures and contextual signals to understand their surroundings. Multimodal Large Language Models (MLLMs) seek to reproduce this capability by integrating text, vision and speech within unified computational frameworks. This chapter introduces the evolution and technological foundations of multimodal intelligence and explains how different data modalities can be aligned through shared representations. It examines the architectural components, training paradigms and evaluation methods used to develop multimodal systems. Applications are discussed across healthcare, autonomous systems, education, accessibility, conversational interfaces and intelligent assistants. For example, healthcare applications can combine electronic health records, spoken clinical interactions and medical images to provide richer contextual understanding. Similarly, autonomous vehicles can integrate visual road information, spoken instructions and textual navigation data. The chapter also considers challenges involving multimodal alignment, computational requirements, data quality, hallucination, interpretability and safety. It concludes by highlighting future research toward more natural, context-aware and human-centered AI systems capable of seeing, listening, reasoning and communicating

Large Language Models have moved beyond experimental research environments and now support conversational agents, coding assistants, recommendation systems and intelligent decision-making applications. However, delivering these services to millions of users requires extremely low latency, high throughput, scalability, security and cost efficiency. This chapter examines the engineering principles required for real-time LLM inference and large-scale deployment. It decomposes end-to-end latency into model execution, network and queuing components and discusses techniques for optimizing each stage. Model-level approaches such as quantization, knowledge distillation, LoRA, QLoRA and speculative decoding are examined for reducing computational requirements while maintaining model quality. At the infrastructure level, tensor and sequence parallelism, GPU and TPU acceleration, efficient batching, autoscaling and cloud-native deployment strategies are considered. Key-value caching and speculative decoding are discussed as important methods for reducing response time in interactive applications. The chapter also addresses privacy, confidential computing, real-time PII protection, energy consumption and monitoring of model performance. Practical deployment examples demonstrate how these techniques can support responsive, secure and efficient AI services at production scale.

The rapid growth of Large Language Models has demonstrated that increasing model size, training data and computational resources can significantly improve performance. This chapter examines the theoretical and empirical foundations of scaling laws and their relationship with optimization strategies used to train and deploy increasingly capable LLMs. It discusses the major scaling dimensions of model parameters, dataset size and computational budget and explains how predictable relationships between these factors influence model development. The chapter considers the foundational work on scaling laws and the subsequent movement toward compute-optimal training approaches. It also examines distributed training, advanced optimization algorithms, memory optimization, data curation, quantization, pruning and efficient inference. Particular attention is given to Mixture-of-Experts architectures, efficient attention mechanisms and inference-aware scaling. The interaction between scaling laws and optimization is presented as a synergistic process in which scaling principles guide resource allocation while optimization techniques improve computational efficiency. The chapter concludes by discussing emerging capabilities, sustainability, hardware requirements and responsible scaling of future foundation models.

There are many types of intellectual property and its type varies from country to country. The most common types are patents, copyrights, trademarks, design and trade secrets. Intellectual property is an intangible creation of the human intellect and this intangible nature of intellectual property faces problems in comparison of traditional property like land or goods. Unlike traditional property, intellectual property is not dividable therefore a number of people can consume an intellectual good without its consumption. In addition, investments in intellectual goods suffer from appropriation problems, for instance Landowners can surround their land with a robust fence and hire armed guards to protect it, but creators of intellectual property cannot do that to stop their first buyer from replicating it and selling it at a lower price.

Biological Diversity Act, 2002 was passed by Indian government in pursuance of Convention on Biological Diversity, 1993 The Convention on Biological Diversity (CBD) is a legally binding multilateral environmental agreement that has 194 contracting Parties (Countries) as its members with three objectives mentioned below:

1. Conservation of biological diversity
2. Sustainable use of the diversity and
3. Ensuring fair and equitable sharing of benefits of such use.

The recent concept of intellectual property emerged in England in the 17^th & 18^th centuries. The term Intellectual Property came in use in the 19^th century and in the late 20^th century. As of now the Intellectual Property has occupied common position in the global legal systems. There are different forms of Intellectual property rights which include patents, copyright, industrial design, trademarks, plant variety rights, trade dress, geographical indications and in some jurisdictions trade secrets too

The ITPGRFA is a comprehensive international agreement which is poised at guaranteeing food security through the conservation, exchange and sustainable use of the world's plant genetic resources for food and agriculture, instead of fair and equitable benefit sharing arising from its use. It also advocates Farmers' Rights.

13.1. Genesis of the Treaty

The conservation and sustainable use of plant genetic resources for food and agriculture are keys to feed global growing population in the coming future. The Commission on Genetic Resources for Food and Agriculture was established in 1983, and the voluntary International Undertaking on Plant Genetic Resources was adopted here in. Another major step was taken in 1996 with the adoption of the Global Plan of Action at the Leipzig International Technical Conference on Plant Genetic Resources. 

15.1 Sample Objective Question

1. Which one is related to deposition of Micro-organism?
   1. Madrid Agreement
   2. Berne Convention
   3. Budapest Treaty
   4. Patent Cooperation Treaty.
2. WTO was officially commenced in the year...........
   1. 1970
   2. 2003
   3. 2005
   4. 1995
3. Which one is related to protection of Trademarks
   1. Madrid Agreement
   2. Berne Convention
   3. Budapest treaty
   4. None of these
4. One of the four patent office is...........
   1. Patna
   2. Banglore
   3. Varanasi
   4. Mumbai

The Patent Cooperation Treaty provides an overview of an International Treaty which is administered by the World Intellectual Property Organization. The Patent Cooperation Treaty is an international treaty with more than 157 Contracting States by 2022. India deposited its instrument of accession on 7 September 1998 to both international treaties of WIPO in Geneva i.e. Paris convention for the protection of Industrial Property and Patent Cooperation treaty. PCT provides a system for filing a patent application and allow us to obtain patents in multiple countries around the world on the basis of a single patent application.

Quality assurance is fundamental to producing safe, consistent, legally compliant, and commercially successful ice cream. This chapter presents a comprehensive QA framework covering supplier approval, incoming-material inspection, process monitoring, laboratory testing, microbiological control, sanitation, finished-product release, traceability, market quality, and corrective and preventive action. It explains the relationship between quality assurance, quality control, food safety, documentation, and management systems such as HACCP and ISO-based frameworks. The chapter also examines QA department structures, monthly reviews, audits, digitalization, quality culture, and performance indicators. By integrating prevention with measurement and accountability, the chapter demonstrates how effective QA can protect consumers, reduce losses, improve consistency, and strengthen profitability.

This is an international convention governing the recognition of deposits in officially approved culture collections for the purpose of patent applications in any country that is a party to it. Because of the difficulties and on occasion of virtual impossibility of reproducing a microorganism from a description of it in a patent specification, it is essential to deposit a strain in a culture collection centre for testing and examination by others.

This was the year, 1974, the Director General of WIPO convened a committee of Experts to discuss the possibilities of international cooperation over the deposit of microorganisms for patent purposes

Basic knowledge of statistics is important to understand animal breeding analysis. In this chapter, the hypothesis testing, principles of experimental design, types of data and probability distribution are described with examples using appropriate software.

It is known that the phenotype of an individual is a combination of genotype and environment. Animal breeding analysis is mainly done to partition the genetic part from the environmental part so as to assess the actual genetic worth of the animal. In this process correction for the known environmental part is integral to any animal breeding analysis. In this chapter, a list of common factors affecting important economic traits are described. The analytical part for these effects will be explained in later chapters.

Correction or standardization of data is very important in any animal breeding analysis. Unless the candidates under selection are placed on a common platform, various factors affecting the trait will lead to bias in assessing the performance of the individual with respect to a particular trait. For eg. a lamb born during a favorable season, a lactating cow in its third parity, first lactation yield of a cow with longer age at first calving.

Correlations and regression are important components of animal breeding analysis. The genetic correlation is an important genetic parameter that is useful in the planning of breeding programs involving multiple traits. In this chapter, the basic concepts of correlation and regression including method of estimation, assumptions and test of significance are presented. An example using SPSS software is also given at the end for better understanding.

Estimation of variance is primary for any breeding program as all the genetic parameters are dependent on these estimates. The properties of an estimate, their assumptions, type of data, availability of data, and computational requirement make it important to understand the method of estimation of variance components so as to make a wise choice that would suit the breeder’s requirement. This chapter explains the methods of estimation of variance with merits and demerits of each and the reader could use these as a guide to choose the right method of estimation.

The genetic parameters are the basis for any animal breeding program and the important parameters such as heritability, repeatability and genetic correlation are ratios of (co)variances and their reliability depends on the accurate estimation of variance components. Estimates of variance components are used extensively in animal breeding.

The key genetic parameters available with the animal breeder to attain improvement are heritability, repeatability and genetic correlation. This chapter will mainly deal with the theoretical considerations of these parameters.

The goal of an animal breeder is to bring about permanent improvement in different economic traits, either individually (univariate) or simultaneously in multiple traits (multitrait). The phenotypic value available from the animal is a combination of genotypic value and environmental deviation. By permanent improvement in a trait, we mean the improvement in genotypic value, which could be carried over to future generations. The environmental deviations are spatially and temporally specific, and thus will not be the same over generations. The genotypic value, itself is made up of additive effect, dominance deviation and interaction deviation. The additive effect is a value of genes (the sum of the average effect of genes) and hence is more important for the breeder as only the genes are passed on to the next generation.

Domestic livestock plays a major role in meeting the food requirement of human beings. Especially with dwindling resources, improvement in production efficiency is primary to meet the ever-increasing demand for animal protein. The role of an animal breeder is to bring about a sustained improvement in the quality and quantity of such products. Over the years, there has been an enormous improvement in the production ability of livestock and statistical analysis has played a predominant role in the application of principles of animal breeding to improve the productivity of animals. The combination of genetics and statistics developed into a separate branch of science called biometry.

Milk yield is one of the most important economic traits and modelling the lactation curve is important with respect to the unique shape with ascending, persistent and descending phases. The non-linear relationship of daily milk yield with days in milk has led to extensive research in modelling the lactation curve. The nature of the lactation curve, different models, identifying the best model of fit, factors affecting the curve parameters and orthogonal polynomials to model the curve are discussed in this chapter.

Linear models have been extensively used in animal breeding. This chapter explains the basics of linear models including description; fixed, random and mixed models; model diagnostics and covariates. The concept of linear models in animal breeding is explained with representation and actual examples of use in different types of livestock.

Introduction to Linear Models

A model can be defined as a physical, mathematical or otherwise logical representation of a system, entity, phenomenon, or process. Linear models are a way of describing response variables in terms of a linear combination of the predictor variable. They provide a framework for a large set of models whose common goal is to explain or predict a quantitative dependent variable by a set of independent variables that can be categorical or quantitative.

Healthcare represents one of the most promising yet sensitive application domains for Large Language Models. LLMs can process extensive medical literature, electronic health records, clinical narratives and other forms of biomedical information to support diagnosis, research and patient care. This chapter explores the growing role of LLMs in healthcare, beginning with their development from general language models toward specialized biomedical and clinical systems. It examines applications in disease diagnosis, clinical decision support, medical documentation, literature analysis, drug discovery and personalized medicine. LLMs can assist with differential diagnosis, symptom extraction, medical question answering, clinical trial matching and interpretation of medical information. In drug discovery, they can analyze scientific publications, patents and clinical evidence to identify potential drug candidates, targets and opportunities for drug repositioning. Personalized medicine applications involve integrating genetic, lifestyle and treatment-response information to support patient-specific therapeutic decisions. At the same time, the chapter emphasizes major concerns involving patient privacy, hallucination, explainability, regulatory compliance and human oversight. The discussion highlights the importance of responsible integration of LLMs with clinical expertise and established healthcare practices

Traits such as growth, lactation test day yield, semen evaluation are longitudinal traits measured repeatedly on the animal and random regression models (RRM) have been found to be suitable for modeling these traits, The methodology used, data requirement, assumptions, validity, software available and application of RRM are explained in this chapter.

Growth, test-day milk yield, and semen traits are examples of longitudinal traits measured repeatedly on the animal and random regression models (RRM) have been found suitable for modeling such traits. RRM is based on a covariance function which could be defined as “a continuous function to give the variance and covariance of traits measured at different points on a trajectory” (van der Werf, 2001). Kirkpatrick et al. (1990) showed that variance components for longitudinal data can be modelled through covariance functions. Random regression models (RRM) and the resulting covariance function have been recognized as ideally suited for the analysis of longitudinal data (Hill, 1998; Meyer, 1998).

Maintaining the required type of information in the form of records is primary for any type of animal breeding analysis and this chapter explains the importance of records and ways to maintain them in the form of registers or computer databases. The formats for different types of registers to be maintained are also given as a template in appendix. Any breeding plan is formulated to improve the economic traits depending on the utility of species reared and the chapter lists important economic traits of livestock and poultry with definitions and formulae, where ever required.

Basic record maintenance is a must for any animal breeding program. Every farm or breeding program is expected to maintain records in the form of registers or computerized databases. The goal of animal breeding is to select mates with the objective to improve economically important traits. In this chapter, the most common economic traits in different species of livestock are described. Even though the list is not exhaustive the basic economic traits that have been worked for improvement are explained. Breeders also explore new composite traits which are a combination of these base traits.

Meta-analyses has been used extensively to combine the results of existing studies and obtain powerful estimates of a parameter. In animal breeding, attempts have been made to obtain meta-analysis estimates for genetic parameters. This tool is more pertinent to animal breeding on account of heterogeneity in terms of location, sample size and other factors in the estimation of genetic parameters. This chapter explains the background, methodology, precautions and implications of meta-analysis with respect to genetic parameters.

A meta-analysis is a powerful tool of statistics that could combine several estimates from different studies for a parameter and the resulting weighted estimate could be used as a representative value for the particular parameter. However, not all the studies are similar with respect to location, sample size, and methodology used and thus various precautions are to be taken right from the selection of studies to incorporating of between-study heterogeneity in the models. In animal breeding, met-analysis is very much useful for obtaining meaningful estimates of genetic parameters and this chapter explains the methodology of meta-analysis in animal breeding.

This chapter describes various multivariate techniques which are useful in animal breeding. The genetic correlation with multiple response variables is already explained in the chapters on Correlation and Genetic Parameters. The other techniques including MANOVA, Principal Component Analysis, Factor Analysis, Cluster Analysis and Discriminant Analysis are explained.

Multivariate analysis is referred to a set of data with multiple response variables in general and such data is encountered often in animal breeding analysis. The simplest form of multivariate analysis is the genetic correlation where two or more traits are analysed together as response variables with factors affecting them on the right-hand side (RHS) and details of these are already explained in earlier chapters.

Animal breeding data is unique in terms of volume, statistical analysis, type of information required etc. The large volume of data extended spatially and temporally warrants additional tools in the form of databases to handle data and enable accuracy, retrieval, efficient utilization and adaptability to software. This chapter explains the nature of animal breeding data and the importance of databases with examples and explanations.

Animals are raised for some utility including meat, milk, hide, wool etc., and efforts are on to continuously improve the quality and quantity of these products. Some of the important economic traits in this regard were already discussed in earlier chapters. Record maintenance is primary for decision-making with respect to the selection of animals. The details of record maintenance including the format of registers are already explained in chapter 2. Apart from the actual information required for economic traits, pedigree information forms an important part of animal breeding data.

The aim of the breeder is to improve on various economic traits through selection. However, due to the lesser number of parents used, over a period of time, there creeps in the effect of inbreeding and loss of genetic variability which in turn causes depression and loss of fitness. This is more prominent in finite endangered populations and evaluation for genetic variability also forms an integral part of the animal breeding analysis.

Genetic variability is an integral part of domestic animal populations as fitness is directly related to the variability.

The large volume, type and nature of animal breeding data and the complex nature of analytical requirement warrants additional tools for handling, storage, retrieval and solving complex equations. Accuracy, speed, computational requirement, applicability, etc., has resulted in the development of numerous software exclusive for animal breeding data analysis. The database management system is already explained in the chapter on ‘Nature of animal breeding data and database management’. This chapter gives a note on the commonly used animal breeding software.

Introduction

Resources must be managed effectively to achieve maximum sustainable agricultural production from limited natural resources. Many of the resources that agriculture depends on (such as water and land) are finite and need to be well managed for long term sustainability. In most cases, this means avoiding adverse effects on the society and environment. With the development of agricultural sectors, the utilization patterns of natural resources in agriculture are important concern for policy makers in present climate change scenario. Unlike other production activities, agricultural activities also utilize factor resources to produce several marketable products or services. The way the natural resources are utilized in agriculture and its concern for society and environment, plays a vital role in economic viability and ecological sustainability of agriculture.

There are several conventional methodologies to estimate the level of profit and efficiency of production system at enterprise level. Only marketable factors are considered in conventional methods keeping the non-marketable component of production system away. Non-marketable component of a production system includes various social and environmental dimensions of it. Nammalwar (1997) has explained the concept of environmentally sound and sustainable enterprises, which include the physical resources, ecological resources, human use value, quality of life values with either way impact of agriculture and environment. In recent years, non-marketable dimensions of agricultural activities have been given importance because of its concern for the environment and society. This chapter provides a methodology that incorporates non-marketable component of agricultural enterprise besides its marketable component. The Resource Cost Ratio (RCR) approach is a modified methodology developed by Debnath et al. (2010) from Domestic Resource Cost Ratio (DRCR) that Morris (1990) has applied at macro-economic level to determine the comparative advantage between countries.

Appendix A: Diagrammatic Presentation of Processes Involved in Obtaining an Indian Patent

1.1    General Introduction, History, Ownership of Tangible and Intellectual Property
    
1.2    Patentable Subject Matter
    
1.3    Basic Requirements of Patentability
    
1.4    Novelty, Public Domain, Obviousness and Non Obviousness
    
1.5    Inventive Step (Non-Obviousness) and Industrial Application

3.1    Competitive and Collaborative Research
    
3.2    The Strategy to Protecting Plants : Plant Variety Protection Act
    
3.3    IPR Issues in Biotechnological Patents Haldi, Neem and Basmati Rice
    
3.4    Patent Litigation

The Madrid System for the International Registration of Marks is governed by two treaties. The first one is he Madrid Agreement, which was concluded in 1891 and revised at Brussels (1900), Washington (1911), The Hague (1925), London (1934), Nice (1957) and Stockholm (1967), and amended in 1979. The other one is the Protocol related to this agreement which was concluded in 1989. The aims of this protocol are to make the Madrid system more flexible and more compatible with the domestic legislation of certain countries or intergovernmental organizations that had not been able to accede to the Agreement. 

6.1    Copyrights
    
6.2    Trademarks
    
6.3    Geographical Indications
    
6.4    Trade Secrets

4.1    Provisional and Complete Specifications
    
4.2    Publication
    
4.3    Request of Examination
    
4.4    PCT Application: Gateway to Foreign Patents

5.1    TRIPS
    
5.2    WTO
    
5.3    WIPO
    
5.4    Paris Convention
    
5.5    Berne Convention
    
5.6    Budapest Treaty
    
5.7    Madrid Agreement Marks (MAM)
    
5.8    The Patent Law Treaty (PLT)

2.1    Specification Drafting
    
2.2    Methods and Precautions in Drafting Patent Specification
    
2.3    Disclosure Requirements
    
2.4    Patent Claims
    
2.5    Filing of A Patent Application

Accurate crop-yield forecasting supports food-security planning, resource allocation and farm decision-making. Yield is influenced by interacting factors such as weather, soil, irrigation, fertilizer and pesticide use, making reliable prediction challenging. This chapter presents an algorithmic framework using historical agricultural and environmental data. It covers cleaning, missing-value treatment, categorical encoding, normalization and feature extraction. Decision Tree, Gradient Boosting, AdaBoost and XGBoost regression models are evaluated individually and combined through soft voting. Performance is measured using standard prediction-error metrics. The case study demonstrates that ensemble modelling can improve accuracy and robustness, helping farmers and planners optimize inputs, anticipate production and strengthen agricultural productivity.

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.

Introduction

Canine epilepsy, a prevalent neurological disorder, significantly impacts the quality of life for both dogs and their owners. Traditional anticonvulsant therapies often yield inconsistent outcomes, necessitating the exploration of alternative treatments such as homeopathy [1][2]. Homeopathy’s individualized approach aligns with the principles of unicism, focusing on holistic care tailored to each patient’s unique symptom profile [3]. However, the subjective nature of remedy selection and response assessment poses challenges for practitioners.

The integration of Artificial Intelligence (AI) and Machine Learning (ML) offers a promising avenue to address these challenges [4]. Predictive models can analyze complex datasets to uncover patterns that guide remedy selection and treatment strategies. This study evaluates the comparative performance of Logistic Regression, Random Forest, and XGBoost in predicting treatment outcomes for canine epilepsy managed with homeopathic remedies.

8.1 Introduction

The objective of this chapter is to develop a smart control for site-specific management of fixed irrigation system with Blynk app. Mobile app is designed to control the water management in field. The complete system comprises of two sections field device and mobile app. Field device comprises of Arduino, Node MCU, Power supply, LCD, Relay board, soil moisture sensor, temperature & humidity sensor, water level sensor, motor1, motor2. The system is designed to establish control and communication with specific agricultural field to take sensory data from the sensors and control the PUMP IN motor and PUMP OUT motor with the help of mobile app. Fig.8.1 shows a block diagram for a system.

The increasing capabilities of Large Language Models have created strong demand for real-time AI services, but their computational complexity makes low-latency deployment difficult on conventional systems. Transformer models require substantial memory, high computational throughput and efficient data movement, creating challenges for mobile devices, embedded systems, edge platforms and latency-sensitive applications. This chapter introduces hardware–software co-design as an integrated strategy for addressing these challenges. Rather than optimizing hardware and software independently, co-design aligns model algorithms with the characteristics of processors, memory hierarchies, accelerators and communication systems. The chapter examines hardware platforms including GPUs, TPUs, FPGAs and ASICs, together with software techniques such as quantization, pruning, knowledge distillation, operator fusion and memory scheduling. Hybrid architectures are discussed for reducing latency, improving throughput, lowering energy consumption and increasing scalability. Applications in healthcare, finance, autonomous driving, robotics, industrial IoT and real-time translation illustrate the importance of efficient inference. The chapter demonstrates that coordinated optimization across the full computing stack can bridge the gap between LLM capability and practical real-time deployment while maintaining acceptable accuracy and reliability.

Introduction

Accuracy and uncertainty analysis is an important step in preparation of surface map through geostatistical techniques or digital soil mapping approach. It is because that we rely on a map based on how much accurate it is and the degree of uncertainty associated with the estimates while the map was prepared. Any stakeholder for a soil map especially policy makers is interested in accuracy and uncertainty of the map before taking suitable land management decisions involving soil map as an input. Again the level of accuracy and uncertainty depends on the properties for which the map has been developed. If the property of interest is sensitive to environment or human consumption, then the accuracy level should be very high while the uncertainty level should be very low e.g. heavy metal concentration in soil, quality parameters of water etc.

9.1 Introduction
Building upon our foundational understanding of statistical methods in meteorology, Chapter 9, “Advanced Analytic Techniques,” propels us into the forefront of data science applications within the atmospheric sciences. The imperative for these advanced methodologies stems directly from the evolving nature of meteorological data itself. Modern observations, numerical weather prediction (NWP) model outputs, and climate simulations generate an unprecedented volume of information, often arriving with incredible velocity and exhibiting immense variety in format and structure. Traditional statistical approaches, while robust for many tasks, often struggle to efficiently process such vast, high-dimensional datasets, or to fully capture the intricate, often non-linear, relationships inherent in atmospheric processes.
This chapter is dedicated to exploring why and how these sophisticated techniques are not merely supplementary, but increasingly essential. We will delve into how machine learning and deep learning algorithms excel at identifying subtle, complex patterns within massive datasets, enabling breakthroughs in areas like severe weather detection, nowcasting, and the bias correction of model outputs. We will also examine advanced spatial statistics, which provide powerful frameworks for explicitly modeling the intricate geographical dependencies of meteorological variables, crucial for accurate interpolation, downscaling, and understanding regional climate impacts. Furthermore, the chapter will introduce functional data analysis, a method particularly adept at treating continuous meteorological fields (like temperature profiles or pressure maps) as functions, allowing for a more holistic analysis of their evolution and variability. By embracing these advanced analytical techniques, meteorologists and climate scientists can unlock deeper insights, enhance predictive skill, more rigorously quantify uncertainties, and ultimately contribute to a more resilient society in the face of a changing climate. Prepare to expand your analytical toolkit and discover new frontiers in meteorological understanding.

Biomedical signals provide valuable information about physiological conditions and support continuous health monitoring. This chapter introduces ECG, EEG, EMG, PPG, respiratory and blood-pressure signals and examines the challenges associated with their acquisition and interpretation. It discusses noise removal, filtering, normalization, resampling and artifact correction before presenting time-domain, frequency-domain, time–frequency, wavelet and nonlinear feature-extraction methods. Feature selection and dimensionality reduction are considered in relation to model accuracy and clinical interpretability. The chapter also explores machine learning, deep learning and hybrid approaches through applications involving arrhythmia detection, seizure prediction and wearable cardiovascular monitoring, highlighting their importance in intelligent and personalized healthcare.

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.

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.

Age old practice of photography was taken with still camera in parallel direction, not aerially scan or snapping which really present wider prospective of any object or places in rearranging or planning skilfully in two dimensional working. Earlier aeroplane played a important role in over head condition to capture snaps but again accuracy and angle of camera limited the works because these photographs were diagonally or trigonally not matched the reality of ground.

Precision agriculture depends on information collected through ground sensors, satellites, IoT systems and other connected devices. However, these data sources differ in spatial resolution, timing, quality and technical compatibility. This chapter introduces a computational framework for acquiring, harmonizing and analysing heterogeneous agricultural data. Preprocessing methods address noise, missing values and inconsistencies, while machine learning, deep learning and ensemble models identify useful environmental, spectral and temporal patterns. Applications include crop-health assessment, soil-moisture estimation, yield prediction and climate-resilience planning. Edge and cloud computing support scalability and real-time operation. Integrating multiple data sources can improve model accuracy, robustness and the practical usefulness of intelligent farming systems.

Agriculture addresses the essential food wellspring of the world, and it has been confronting serious difficulties because of the rising interest for food items, sanitation, and security worries as well as calls for ecological insurance, water conservation, and maintainability. This improvement is anticipated to go on since the total populace is assessed to reach 9.7 billion by 2050. Since farming comprises the most noticeable illustration of water utilization around the world, it is normal that food interest and water utilization will decisively increment within a reasonable timeframe. Moreover, the rising utilization of composts and pesticides combined with the increase of cultivating exercises could prompt future ecological difficulties. Essentially, arable land is restricted, and the quantity of farmers is diminishing around the world. These difficulties highlight the requirement for imaginative and practical cultivating arrangements.
Consolidating novel innovations has been distinguished as a promising answer for address these difficulties. Brilliant cultivating and Precision farming have arisen because of such discussions. The previous is an overall idea for information communication technologies (ICT) and other state of the art developments in cultivating exercises to build productivity and viability. The last option centers around site-explicit administration in which the land is separated into homogeneous parts, and each part gets the specific measure of agricultural contribution for crop yield enhancement through original advances. Unmistakable advances that certainly stand out in this field incorporate Wireless Sensor Networks (WSNs), the Internet of Things (IoT), artificial intelligence (AI) techniques, including machine learning and deep learning.
Notwithstanding the previously mentioned innovations, remote detecting has been viewed as a mechanical device with high potential to work on savvy and Precision farming. Satellites, human-maintained airplane, and robots are well known remote-detecting advances. Drones, popularly known as Unmanned