Imran Ahmad - Böcker

Nanocellulose: Harnessing Sustainability from Biomass to Biocomposites presents a detailed review of the latest advances in nanocellulose-reinforced biocomposites, with a particular emphasis on their processing, properties, and performance. Through detailed case studies, the book's chapters cover the conceptual design process, including materials selection, fabrication processes, characterization techniques, and innovations in applications and performance. Sections also emphasize the need for meticulous design and tailored process development, which are crucial for selected applications. In addition, the book includes a dedicated chapter on the market value of the global nanocellulose industry and assesses its contributions to sustainability. The mechanical, thermal, and electrical performance properties of these materials are also discussed in detail, as is the structure-property relationship. With its focus on idea generation and informed decision-making, this book will serve as a valuable reference resource for all individuals engaged in this area of research.Includes detailed information on the fundamentals of nanocellulose, including its anatomy, morphological characterization, isolation, extraction, modification, and functionalizationCovers applications in wastewater treatment as propellants, in biomedical, structural, electronic, and food packaging sectorsDiscusses economic, social, environmental aspects, lifecycle analysis, and assessmentCovers seaweed-based nanocellulose, microalgae, both carbon and graphene reinforced hybrid epoxy composites, and palm oil biomassAddresses key challenges and future outlooks

Currently, most of the physical and chemical technologies generally employed for the treatment of wastewater are costly and inefficient. Researchers have been working to find green technologies for the sustainable and economically viable treatment methods for wastewater, and Green Technologies for Wastewater Treatment and Bioenergy Production focuses on these latest advancements. These include bioremediation, bioaugmentation, wetlands, and algal treatment technologies, which are considered to contribute toward a circular bioeconomy and to be crucial in attaining various sustainable development goals.Examines the production of biofuels and other forms of bioenergy derived from wastewater.Explains how these emerging green technologies contribute toward a circular bioeconomy and aid in achieving various sustainable development goals.

Functional foods are foods which contain bioactive components, either from plant or animal sources, which can have health benefits for the consumer over and above their nutritional value. Foods which have antioxidant or cancer-combating components are in high demand from health conscious consumers: much has been made of the health-giving qualities of fruits and vegetables in particular. Conversely, foods which have been processed are suffering an image crisis, with many consumers indiscriminately assuming that any kind of processing robs food of its “natural goodness”. To date, there has been little examination of the actual effects – whether positive or negative – of various types of food processing upon functional foods.This book highlights the effects of food processing on the active ingredients of a wide range of functional food materials, with a particular focus on foods of Asian origin. Asian foods, particularly herbs, are becoming increasingly accepted and demanded globally, with many Western consumers starting to recognize and seek out their health-giving properties. This book focuses on the extraction of ingredients which from materials which in the West are seen as “alternative” - such as flour from soybeans instead of wheat, or bran and starch from rice – but which have long histories in Asian cultures. It also highlight the incorporation of those bioactive compounds in foods and the enhancement of their bioavailability.Functional Foods and Dietary Supplements: Processing Effects and Health Benefits will be required reading for those working in companies, research institutions and universities that are active in the areas of food processing and agri-food environment. Food scientists and engineers will value the new data and research findings contained in the book, while environmentalists, food regulatory agencies and other food industry personnel involved in functional food production or development will find it a very useful source of information.

Learn algorithms for solving classic computer science problems with this concise guide covering everything from fundamental algorithms, such as sorting and searching, to modern algorithms used in machine learning and cryptographyKey FeaturesLearn the techniques you need to know to design algorithms for solving complex problemsBecome familiar with neural networks and deep learning techniquesExplore different types of algorithms and choose the right data structures for their optimal implementationBook DescriptionAlgorithms have always played an important role in both the science and practice of computing. Beyond traditional computing, the ability to use algorithms to solve real-world problems is an important skill that any developer or programmer must have. This book will help you not only to develop the skills to select and use an algorithm to solve real-world problems but also to understand how it works.You’ll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, such as searching and sorting, with the help of practical examples. As you advance to a more complex set of algorithms, you'll learn about linear programming, page ranking, and graphs, and even work with machine learning algorithms, understanding the math and logic behind them. Further on, case studies such as weather prediction, tweet clustering, and movie recommendation engines will show you how to apply these algorithms optimally. Finally, you’ll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks.By the end of this book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.What you will learnExplore existing data structures and algorithms found in Python librariesImplement graph algorithms for fraud detection using network analysisWork with machine learning algorithms to cluster similar tweets and process Twitter data in real timePredict the weather using supervised learning algorithmsUse neural networks for object detectionCreate a recommendation engine that suggests relevant movies to subscribersImplement foolproof security using symmetric and asymmetric encryption on Google Cloud Platform (GCP)Who this book is forThis book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code. Whether you are a beginner looking to learn the most commonly used algorithms in a clear and concise way or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful. Although Python programming experience is a must, knowledge of data science will be helpful but not necessary.

Delve into the realm of generative AI and large language models (LLMs) while exploring modern deep learning techniques, including LSTMs, GRUs, RNNs with new chapters included in this 50% new edition overhaulPurchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesFamiliarize yourself with advanced deep learning architecturesExplore newer topics, such as handling hidden bias in data and algorithm explainabilityGet to grips with different programming algorithms and choose the right data structures for their optimal implementationBook DescriptionThe ability to use algorithms to solve real-world problems is a must-have skill for any developer or programmer. This book will help you not only to develop the skills to select and use an algorithm to tackle problems in the real world but also to understand how it works.You'll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, with the help of practical examples. As you advance, you'll learn about linear programming, page ranking, and graphs, and will then work with machine learning algorithms to understand the math and logic behind them.Case studies will show you how to apply these algorithms optimally before you focus on deep learning algorithms and learn about different types of deep learning models along with their practical use.You will also learn about modern sequential models and their variants, algorithms, methodologies, and architectures that are used to implement Large Language Models (LLMs) such as ChatGPT.Finally, you'll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks.By the end of this programming book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.What you will learnDesign algorithms for solving complex problemsBecome familiar with neural networks and deep learning techniquesExplore existing data structures and algorithms found in Python librariesImplement graph algorithms for fraud detection using network analysisDelve into state-of-the-art algorithms for proficient Natural Language Processing illustrated with real-world examplesCreate a recommendation engine that suggests relevant movies to subscribersGrasp the concepts of sequential machine learning models and their foundational role in the development of cutting-edge LLMsWho this book is forThis computer science book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code.Whether you are a beginner looking to learn the most used algorithms concisely or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful.Python programming experience is a must, knowledge of data science will be helpful but not necessary.

Discover how to design intelligent software systems by balancing AI and traditional architecture. This guide offers a roadmap for robust, scalable AI-enabled systems, blending principles with practical insights.Key FeaturesLearn to integrate AI with traditional software architectures, enabling architects to design scalable, high-performance systemsExplore key tools and processes to mitigate risks in AI-driven system development, ensuring timely project delivery and budget controlGain hands-on experience through case studies and exercises, applying architectural concepts to real-world AI systemsBook DescriptionArchitecting AI Software Systems provides a definitive guide to building AI-enabled systems, emphasizing the balance between AI’s capabilities and traditional software architecture principles.As AI technologies gain widespread acceptance and are increasingly expected in future applications, this book provides architects and developers with the essential knowledge to stay competitive. It introduces a structured approach to mastering the complexities of AI integration, covering key architectural concepts and processes critical to building scalable and robust AI systems while minimizing development and maintenance risks. The book guides readers on a progressive journey, using real-world examples and hands-on exercises to deepen comprehension. It also includes the architecture of a fictional AI-enabled system as a learning tool. You will engage with exercises designed to reinforce your understanding and apply practical insights, leading to the development of key architectural products that support AI systems. This is an essential resource for architects seeking to mitigate risks and master the complexities of AI-enabled system development.By the end of the book, readers will be equipped with patterns, strategies and concepts necessary to architect AI-enabled systems across various domains.What you will learnUnderstand the challenges of building AI-enabled systems and managing risks like underperformance and cost overrunsLearn architectural tools to design and integrate AI into traditional systemsMaster AI/ML concepts like inference and decision-making and their impact on architectureUse architectural models to ensure system cohesion and functionalitySimulate and optimize AI performance through prototyping and iterationDesign scalable AI systems using patterns and heuristicsIntegrate AI into large systems with a focus on user experience and performanceWho this book is forThis book is designed mainly for software and systems architects responsible for designing and integrating AI capabilities into existing and new systems. It also serves as a valuable resource for CTOs, VPs of Engineering, and aspiring architects seeking a comprehensive understanding of the holistic approach to AI system development. Additionally, AI/ML engineers and software developers will benefit from gaining deeper insights into the architectural principles that underpin AI systems, enabling them to align their work with broader architectural goals

Learn to transform LLM capabilities into production-ready agent systems using practical patterns and domain-driven approaches, guided by Imran Ahmad, author of 50 Algorithms Every Programmer Should KnowFree with your book: DRM-free PDF version + access to Packt's next-gen Reader*Key FeaturesDesign and implement 30 proven agent architectures used in real-world production environmentsBuild scalable, secure, and resilient agent workflows that move beyond simple chat interfacesMaster core agentic principles—perception, memory, reasoning, and planning—to create truly autonomous systemsBook DescriptionAs AI evolves from passive tools into proactive collaborators, intelligent agents are leading a fundamental shift in computing. This guide provides the critical knowledge of agent architectures, practical tools, and industry approaches needed to build robust, autonomous AI systems that do more than just generate text—they act.You will begin by mastering foundational capabilities: perception, memory, reasoning, planning, and learning. You’ll gain deep insight into the cognitive loops that drive autonomous behavior and build sophisticated architectures using frameworks such as LangChain and LangGraph.The book explores high-impact applications across diverse sectors, including software development, finance, manufacturing, legal and education, to show how agents optimize workflows, automate quality control, and enhance advisory systems. Through real-world case studies, you will create agents capable of contextual reasoning, effective tool use, and seamless human collaboration. Finally, you’ll learn essential strategies for deployment, management, and ethical alignment, ensuring your AI solutions are both scalable and responsible in production environments.Whether you're building your first intelligent agent or improving business systems, this book provides clear, actionable guidance for creating scalable and responsible AI solutions.*Email sign-up and proof of purchase requiredWhat you will learnDeploy production-ready agent systems that scale securely and reliablyUse LangChain and LangGraph to build autonomous agents with modular architecturesImplement agents with sophisticated memory, planning, and reasoning capabilitiesSeamlessly integrate tools, APIs, and external data into agent workflowsEstablish robust evaluation frameworks to measure and optimize agent performanceImplement guardrails and explainability features to ensure ethical and safe deploymentBuild multi-agent systems for complex, collaborative task orchestrationApply specific agent architectures across healthcare, finance, and legal domainsWho this book is forThis book is designed for AI engineers, software developers, machine learning researchers, and technical leaders who are building intelligent systems or deploying LLM-powered applications. It is particularly beneficial for professionals transitioning from traditional machine learning to agent-based architectures or those solving complex automation challenges. Python experience and basic machine learning knowledge are recommended to get the most out of the code implementations.

Final FRCA: 300 SBAs offers a wealth of exam questions focussing on the new SBA format. With so few resources available for candidate wishing to practice the new exam format, Final FRCA: 300 SBAs will ensure that trainees have sufficient experience to ensure success in the exam. Presented as 10 complete SBA mock paper sections, and featuring the most up-to-date and comprehensive coverage of practice questions, the content is carefully designed to test knowledge and improve exam technique. Covering the full breadth of the syllabus with particular focus on clinical scenarios, Final FRCA: 300 SBAs provides the definitive SBA revision guide to maximise chances of exam success. 300 SBAs providing large number of questions in the new exam formatAnswers feature detailed explanations to consolidate knowledgeTables, diagrams and graphs to assist in understanding answers and help clarify complex conceptsA further reading reference is provided with each SBA question to aid understanding of answersUp to date and relevant SBA questions reflect RCA style questioning, along with accepted current practice and guidelines

Primary FRCA: 450 MTFs and SBAs offers a comprehensive selection of practice questions for candidates preparing for the Primary FRCA exam.Chapters cover the full breadth of the syllabus with detailed answers and explanations to reinforce understanding, while accompanying references facilitate in-depth reading around the topic in question.Thoroughly updated and including 45 new questions, Primary FRCA: 450 MTFs and SBAs is the ‘go to’ revision tool to maximise chances of exam success.Key points450 MTFs and SBAs cover the full breadth of the Primary FRCA exam syllabusIn depth answers explain why each option is correct or incorrectFive mock papers, each including 60 MTFs and 30 SBAs, allow effective exam practiceNumerous illustrations and tables assist understanding of key conceptsPrevious edition (9781907816598) published in 2014

This book provides a methodical compilation of deriving composites from the hidden treasure of the aquatic world.Continuous and rapid progress in the composite industries have increased the demand for resilient, economically viable, and sustainable composite materials having enhanced mechanical , thermal and electrical properties which better suits there respective applications.If the materials/organisms used for the production or conversion of composites are renewable, degradable and easily and abundantly available then it gives great opportunity to the researchers to work on different options or processes to make them a viable technology. This work describes the organisms and materials present in the aquatic environment for the production of composite materials. Elaborating the versatile green expedients and their potential applications in the field of composites.  Since, growing ecological and environmental consciousness has driven efforts for development of new innovative materials for various end-use applications.Therefore the LCA an circular bio-economy will be discussed to be efficient and sustainable. This book is ideal for the students, academicians, researchers and industry players. It also cover the present scenario, applications and future perspectives of composites derived from aquatic organisms. This compiled book features chapters that discuss the conversion of different materials  and organisms present in aquatic environment to composite materials like micro-algae, seaweeds, chitosan, collagen, agar cyanobacteria etc.in a viable manner.