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Pragmatic Internet of Everything (IOE) has emerged as a powerful paradigm for representing and solving complex problems. This reference demonstrates how to coordinate behaviour among a collection of semi-autonomous problem-solving agents: how they can coordinate their knowledge, goals and plans to act together, to solve joint problems, or to make individually or globally rational decisions in the face of uncertainty and multiple, conflicting perspectives. The book presents a collection of articles surveying several major recent developments in Pragmatic Internet of Everything (IOE). The book focuses on issues and challenges that arise in building IOE systems for smart cities in real-world settings. It also presents solutions to the issues faced by system architects. The synthesis of recent thinking, both theoretical and applied, on major IOE problems makes this essential reading for anyone involved in the design and planning of IOT systems for smart cities. Key Features - Summarizes available literature and practical ventures with references - Merges different perspectives on IoT technology thereby giving a 360-degree perspective to the reader - Gives some tips for implementation of practical ventures in this space - Includes an analysis of information gathered from citizens of smart cities.

Computational thinking (CT) is a timeless, transferable skill that enables you to think more clearly and logically, as well as a way to solve specific problems. With this book you'll learn to apply computational thinking in the context of software development to give you a head start on the road to becoming an experienced and effective programmer. Beginning with the core ideas of computational thinking, with this book you'll build up an understanding of the practical problem-solving approach and explore how computational thinking aids good practice in programming, complete with a full guided example.

Find out how Events Processing (EP) works and how it can work for you Business Event Processing: An Introduction and Strategy Guide thoroughly describes what EP is, how to use it, and how it relates to other popular information technology architectures such as Service Oriented Architecture. * Explains how sense and response architectures are being applied with tremendous results to businesses throughout the world and shows businesses how they can get started implementing EP * Shows how to choose business event processing technology to suit your specific business needs and how to keep costs of adopting it down * Provides practical guidance on how EP is best integrated into an overall IT strategy and how its architectural styles differ from more conventional approaches This book reveals how to make the most advantageous use of event processing technology to develop real time actionable management information from the events flowing through your company's networks or resulting from your business activities. It explains to managers and executives what it means for a business enterprise to be event-driven, what business event processing technology is, and how to use it.

Proving mathematical theorems at the olympiad level represents a notable milestone in human-level automated reasoning 1 – 4 , owing to their reputed difficulty among the world’s best talents in pre-university mathematics. Current machine-learning approaches, however, are not applicable to most mathematical domains owing to the high cost of translating human proofs into machine-verifiable format. The problem is even worse for geometry because of its unique translation challenges 1 , 5 , resulting in severe scarcity of training data. We propose AlphaGeometry, a theorem prover for Euclidean plane geometry that sidesteps the need for human demonstrations by synthesizing millions of theorems and proofs across different levels of complexity. AlphaGeometry is a neuro-symbolic system that uses a neural language model, trained from scratch on our large-scale synthetic data, to guide a symbolic deduction engine through infinite branching points in challenging problems. On a test set of 30 latest olympiad-level problems, AlphaGeometry solves 25, outperforming the previous best method that only solves ten problems and approaching the performance of an average International Mathematical Olympiad (IMO) gold medallist. Notably, AlphaGeometry produces human-readable proofs, solves all geometry problems in the IMO 2000 and 2015 under human expert evaluation and discovers a generalized version of a translated IMO theorem in 2004. A new neuro-symbolic theorem prover for Euclidean plane geometry trained from scratch on millions of synthesized theorems and proofs outperforms the previous best method and reaches the performance of an olympiad gold medallist.

The use of artificial intelligence (AI) is becoming more prevalent across industries such as healthcare, finance, and transportation. Artificial intelligence is based on the analysis of large datasets and requires a continuous supply of high-quality data. However, using data for AI is not without challenges. This paper comprehensively reviews and critically examines the challenges of using data for AI, including data quality, data volume, privacy and security, bias and fairness, interpretability and explainability, ethical concerns, and technical expertise and skills. This paper examines these challenges in detail and offers recommendations on how companies and organizations can address them. By understanding and addressing these challenges, organizations can harness the power of AI to make smarter decisions and gain competitive advantage in the digital age. It is expected, since this review article provides and discusses various strategies for data challenges for AI over the last decade, that it will be very helpful to the scientific research community to create new and novel ideas to rethink our approaches to data strategies for AI.

With the advances in scientific foundations and technological implementations, optical metrology has become versatile problem-solving backbones in manufacturing, fundamental research, and engineering applications, such as quality control, nondestructive testing, experimental mechanics, and biomedicine. In recent years, deep learning, a subfield of machine learning, is emerging as a powerful tool to address problems by learning from data, largely driven by the availability of massive datasets, enhanced computational power, fast data storage, and novel training algorithms for the deep neural network. It is currently promoting increased interests and gaining extensive attention for its utilization in the field of optical metrology. Unlike the traditional “physics-based” approach, deep-learning-enabled optical metrology is a kind of “data-driven” approach, which has already provided numerous alternative solutions to many challenging problems in this field with better performances. In this review, we present an overview of the current status and the latest progress of deep-learning technologies in the field of optical metrology. We first briefly introduce both traditional image-processing algorithms in optical metrology and the basic concepts of deep learning, followed by a comprehensive review of its applications in various optical metrology tasks, such as fringe denoising, phase retrieval, phase unwrapping, subset correlation, and error compensation. The open challenges faced by the current deep-learning approach in optical metrology are then discussed. Finally, the directions for future research are outlined.

Background/Objectives: This systematic review integrates Cognitive Load Theory (CLT), Educational Neuroscience (EdNeuro), Artificial Intelligence (AI), and Machine Learning (ML) to examine their combined impact on optimizing learning environments. It explores how AI-driven adaptive learning systems, informed by neurophysiological insights, enhance personalized education for K-12 students and adult learners. This study emphasizes the role of Electroencephalography (EEG), Functional Near-Infrared Spectroscopy (fNIRS), and other neurophysiological tools in assessing cognitive states and guiding AI-powered interventions to refine instructional strategies dynamically. Methods: This study reviews n = 103 papers related to the integration of principles of CLT with AI and ML in educational settings. It evaluates the progress made in neuroadaptive learning technologies, especially the real-time management of cognitive load, personalized feedback systems, and the multimodal applications of AI. Besides that, this research examines key hurdles such as data privacy, ethical concerns, algorithmic bias, and scalability issues while pinpointing best practices for robust and effective implementation. Results: The results show that AI and ML significantly improve Learning Efficacy due to managing cognitive load automatically, providing personalized instruction, and adapting learning pathways dynamically based on real-time neurophysiological data. Deep Learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Support Vector Machines (SVMs) improve classification accuracy, making AI-powered adaptive learning systems more efficient and scalable. Multimodal approaches enhance system robustness by mitigating signal variability and noise-related limitations by combining EEG with fMRI, Electrocardiography (ECG), and Galvanic Skin Response (GSR). Despite these advances, practical implementation challenges remain, including ethical considerations, data security risks, and accessibility disparities across learner demographics. Conclusions: AI and ML are epitomes of redefinition potentials that solid ethical frameworks, inclusive design, and scalable methodologies must inform. Future studies will be necessary for refining pre-processing techniques, expanding the variety of datasets, and advancing multimodal neuroadaptive learning for developing high-accuracy, affordable, and ethically responsible AI-driven educational systems. The future of AI-enhanced education should be inclusive, equitable, and effective across various learning populations that would surmount technological limitations and ethical dilemmas.

The growing demand for innovative and user-centric product design has led to a growing need for effective idea generation methods. In recent years, natural language processing (NLP) tools such as ChatGPT have emerged as a promising solution for supporting idea generation in various domains. This paper investigates a framework for studying the role of ChatGPT in facilitating the ideation process in product design. This investigation measures the impact of ChatGPT on the generation of innovative concepts compared to the use of “classic” design methods. An overview of the state-of-the-art idea generation methods in product design opens the paper. Then, the paper highlights some hypotheses about the impact of ChatGPT on innovative product design, aiming for product augmentation by adding features. The paper then describes the design experience in which ChatGPT is used as a tool for concept generation. Finally, the paper analyzes the dataset, using precise metrics to characterize the participants’ performance and compare them. This analysis allows the paper to argue about the validation/rejection of the hypotheses. The paper concludes with a discussion of the implications of the findings and some suggestions for future research. Along with the paper, the Microsoft Excel workbook used to perform the data analysis is available to the readers to perform their own data collection and analysis. The workbook UX has been carefully studied and developed to make it usable by anyone. At the same time, it should be flexible enough to manage several situations characterized by different numbers of participants, product functions to implement, and generated concepts.