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.

Nitrogen is an essential plant nutrient and plays a particularly important role in intensively cultivated irrigated systems. This chapter examines nitrogen management in irrigated soils, where assured water availability and intensive cropping can increase nitrogen demand and create significant nutrient losses. The chapter begins with the functions of nitrogen and the forms in which nitrogen occurs in soil, including inorganic and organic forms. Mineralization and immobilization processes are discussed, along with factors affecting nitrogen mineralization and nitrogen transformations under waterlogged conditions. Major pathways of nitrogen loss, particularly volatilization, denitrification and leaching, are examined because they directly influence nitrogen-use efficiency and environmental quality. The chapter discusses different nitrogen fertilizers, including ammonium sulphate, ammonium chloride, ammonium nitrate and slow-release fertilizers. Nitrification inhibitors and strategies for efficient fertilizer use are also considered. Special attention is given to leaching of urea-N, nitrogen-use efficiency and nitrogen balance in the plant–soil system. The chapter therefore emphasizes balanced nitrogen management for sustaining productivity while reducing nutrient losses

Crop improvement is essential for developing horticultural varieties with superior yield, quality, resistance, and adaptation. This chapter introduces conventional breeding methods such as selection, hybridization, backcrossing, and mutation breeding, along with modern approaches involving molecular markers and biotechnology. Important breeding objectives include disease and pest resistance, abiotic stress tolerance, improved nutritional quality, shelf life, fruit characteristics, and market preference. Modern techniques such as marker-assisted selection, genomic approaches, and genome editing can accelerate the development of improved cultivars. The chapter connects traditional genetic principles with contemporary breeding technologies and explains how integrated breeding strategies can address changing production environments, consumer requirements, and emerging horticultural challenges.

Horticultural crops comprise a diverse group of plants differing in botanical characteristics, growth habits, climatic requirements, economic uses, and production systems. This chapter presents systematic approaches for classifying horticultural crops into fruits, vegetables, flowers, plantation crops, spices, medicinal and aromatic plants, and other specialized groups. Classification based on botanical relationships, life cycle, climate, growth habit, edible parts, and commercial utilization is discussed to provide a practical understanding of crop diversity. Knowledge of classification helps students identify crops, understand their biological relationships, select appropriate production practices, and organize horticultural resources. The chapter establishes an essential foundation for crop-specific management and improvement

Sustainability

Sustainability is a complicated concept. The most often cited definition comes from the U.N. World Commission on Environment and Development (WCED): “Sustainable development is the development that fulfills the needs of the present without compromising the ability of future generations to meet their requirements.” Hence, sustainability has been defined as meeting our own needs without compromising the ability of future generations to meet their own needs.

Aim: To verify the relationship between the input and output voltage for 3-phase transformer connection in various ways:

A 3-phase transformer can be considered as a set of 3 single-phase transformer bank or as single unit of 3 phase windings. Since three winding are available in both primary and secondary side then there can be different types of connections and so far, different characteristics.

Commercial Building

Commercial buildings consist of government buildings, banks, hospitals, schools, malls, retail, restaurants, and office spaces. Among these, office and retail occupy the major percentage of energy consumption. 

More than 40% of the total final energy consumption and about 30% of greenhouse gas (GHG) emissions are in connection with the civil and residential sector.

Maximum energy consumption in commercial buildings by HVAC system (51% followed by appliances and food service equipment (20%), lighting(12%), electronics and information technology(9%) and water (8%).

Introduction

India has vast livestock resources. Livestock sector includes animal husbandry, dairy and fisheries sector. Livestock provides livelihood to two-third of rural community and employment to about 8.8 % of the population in India. Livestock sector contributes 4.11% GDP and 25.6% of total Agriculture GDP. It provides milk and milk products, meat and meat products, and also constitutes a major supplier of food and food articles, raw materials, and finished products.

Introduction

To address the escalating food demands of the global population, a transformation in the agricultural sector is imperative (Velten et al., 2015; Lampridi et al., 2019). The current overdependence on finite resources is leading to their overexploitation, largely due to irresponsible usage (Velten et al., 2015; Lampridi et al., 2019). By adopting a site-specific approach, farmers can apply inputs only where they are necessary in the field (Velten et al., 2015; Lampridi et al., 2019). This method can enhance output, decrease labor costs, and mitigate the environmental damage caused by excessive input use (Velten et al., 2015; Lampridi et al., 2019).

Floriculture and landscape architecture combine plant production with aesthetics, environmental improvement, recreation, and urban development. This chapter introduces the production, management, post-harvest handling, and marketing of flowers and ornamental plants. It also explains principles of landscape design, including balance, rhythm, harmony, colour, texture, form, space, and focal points. Softscape and hardscape components are considered in the planning of gardens, parks, institutional landscapes, roadsides, and urban green spaces. The chapter highlights the increasing importance of floriculture and landscape plants for urban biodiversity, pollution reduction, temperature moderation, recreation, and human well-being. Modern applications include turf management, green infrastructure, indoor plants, and peri-urban floriculture.

Climate change impacts

• Climate change is a crucial factor behind cyclone Tauktae

• Global ocean has absorbed 90% of the excess heat generated by greenhouse gas emissions since 1970

• Led to anomalous ocean warming in Arabian Sea and Bay of Bengal, which in turn makes cyclones intensify rapidly

• India will see more frequent cyclones every year due to global warming

Horticultural crops are vulnerable to numerous insect pests, diseases, nematodes, weeds, and other biological stresses that can reduce yield and quality. This chapter presents integrated pest and disease management as a coordinated approach combining prevention, monitoring, cultural practices, biological control, mechanical methods, resistant varieties, and need-based chemical intervention. Accurate diagnosis and regular scouting are emphasized as foundations of effective management. Biorational products, pheromones, microbial pesticides, and natural enemies can reduce dependence on conventional pesticides. Responsible chemical use requires correct product selection, dose, timing, coverage, safety measures, and resistance management. The chapter promotes economically effective and environmentally responsible crop protection while safeguarding beneficial organisms, workers, consumers, and ecosystems.

Horticultural commodities are highly perishable because they remain physiologically active after harvest and are susceptible to moisture loss, respiration, microbial deterioration, mechanical injury, and physiological disorders. This chapter introduces principles of harvesting maturity, handling, grading, sorting, packaging, storage, transportation, and cold-chain management. Appropriate post-harvest practices help preserve freshness, nutritional quality, appearance, and market value while reducing losses. The chapter also examines value addition through processing, drying, preservation, packaging, and development of new horticultural products. Efficient post-harvest systems connect production with consumers and markets and are particularly important for high-value fruits, vegetables, flowers, spices, and other perishable commodities. Together, improved technology, quality management, and value addition can significantly increase farmer income and strengthen horticultural value chains.

3.1 Overview

3.1.1 Rice straw: What is it?

When harvesting paddy rice, rice straw is produced as a by-product. Approximately 0.7-1.4 kilograms of rice straw can be obtained for each kilogram of milled rice, depending on the variety, moisture content during harvest, and cutting length of the remaining plant stems. The rice straw is separated from the grains after the plants are threshed manually using stationary threshers or with modern combine harvesters, as shown in Figure 3.1.

Rice straw is a type of lignocellulosic biomass comprising cellulose, hemicelluloses, and lignin. Figure 3.2 illustrates that pretreatment can be used to separate these components. Unlike lignin, which is found in the cell wall, cellulose and hemicellulose are organic fibers.

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.

Introduction

Eco-friendly interior design is an important consideration for anyone who wants to create a healthy, sustainable, and beautiful space.

Environmental impact: The materials used in interior design can have a significant impact on the environment. Eco-friendly materials are made from renewable resources, reduce waste, and often have a lower carbon footprint than traditional materials.

What is A Smart Energy Home?

Smart homes use internet - connected devices to enable remote monitoring and management of appliances and systems, such as temperature, lighting, and security cameras. Smart energy homes take a step beyond the gadget-filled smart home to focus on bringing smart functionality to major household energy-using systems like HVAC, water heating, and plug loads (including appliances and electronics).

Curriculum development and lesson planning are complex educational processes requiring alignment among learning objectives, competencies, instructional activities, assessment strategies, learner characteristics, and changing societal requirements. Generative AI offers new opportunities to support educators and curriculum designers in managing these processes more efficiently and intelligently. This chapter examines AI-augmented approaches to curriculum development and lesson planning, with emphasis on personalization, competency-based education, adaptive learning, and data-informed instructional design. Generative AI can assist in developing learning objectives, lesson sequences, teaching activities, question banks, assessment rubrics, case studies, and differentiated learning resources. It can also analyze student performance and recommend modifications to curriculum content, learning pathways, and instructional strategies. The chapter considers applications within Indian educational contexts, including curriculum alignment, digital platforms, low-bandwidth environments, and emerging institutional initiatives. It also discusses challenges such as teacher resistance, inadequate AI literacy, interoperability, infrastructure limitations, data privacy, and ethical concerns. Successful implementation requires human review and pedagogical judgment. The chapter ultimately presents AI as a curriculum co-design and teacher-augmentation tool capable of improving efficiency, personalization, scalability, and educational relevance.