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After being hit by the bullet, Huai Mo Lian opened his eyes again. He was dressed in a bridal dress and had been taken in by the aphrodisiac. The self-control that Baili Ye was proud of was completely destroyed by her. After the festivities, this woman had actually used the hairpin to force him into a corner. Interesting woman. You will be my wife from now on. Life is my man, death is my ghost!

This book introduces readers to an evolutionary learning approach, specifically genetic programming (GP), for production scheduling. The book is divided into six parts. In Part I, it provides an introduction to production scheduling, existing solution methods, and the GP approach to production scheduling. Characteristics of production environments, problem formulations, an abstract GP framework for production scheduling, and evaluation criteria are also presented. Part II shows various ways that GP can be employed to solve static production scheduling problems and their connections with conventional operation research methods. In turn, Part III shows how to design GP algorithms for dynamic production scheduling problems and describes advanced techniques for enhancing GP’s performance, including feature selection, surrogate modeling, and specialized genetic operators. In Part IV, the book addresses how to use heuristics to deal with multiple, potentially conflicting objectives in production scheduling problems, and presents an advanced multi-objective approach with cooperative coevolution techniques or multi-tree representations. Part V demonstrates how to use multitask learning techniques in the hyper-heuristics space for production scheduling. It also shows how surrogate techniques and assisted task selection strategies can benefit multitask learning with GP for learning heuristics in the context of production scheduling. Part VI rounds out the text with an outlook on the future. Given its scope, the book benefits scientists, engineers, researchers, practitioners, postgraduates, and undergraduates in the areas of machine learning, artificial intelligence, evolutionary computation, operations research, and industrial engineering.

Antennas From Theory to Practice Comprehensive coverage of the fundamentals and latest developments in antennas and antenna design In the newly revised Second Edition of Antennas: From Theory to Practice, renowned researcher, engineer, and author Professor Yi Huang delivers comprehensive and timely coverage of issues in modern antenna design and theory. Practical and accessible, the book is written for engineers, researchers, and students who work with radio frequency/microwave engineering, radar, and radio communications. The book details the basics of transmission lines, radiowaves and propagation, antenna theory, antenna analysis and design using industrial standard design software tools and the theory of characteristic modes, antenna measurement equipment, facilities, and techniques. It also covers the latest developments in special topics, like small and mobile antennas, wide- and multi-band antennas, automotive antennas, RFID, UWB, metamaterials, reconfigurable and MIMO antennas, and more. The new edition includes up to date information on a wide variety of newly relevant topics and trends, like adaptive impedance matching, the theory of characteristic modes, antenna materials and fabrication processes, and over-the-air (OTA) antenna system measurements. Many questions and examples are provided which enhances the learning experience. The book covers: An introduction to circuit concepts and transmission lines, including lumped and distributed element systems, transmission line theory, and the Smith Chart An exploration of field concepts and radiowaves, including wave equations and solutions and radiowave propagation mechanisms, characteristics, and models Discussions of antenna basics and popular antennas, including wire-type antennas, aperture-type antennas, and antenna arrays Information about antenna manufacturing and measurements, including antenna measurement facilities and methods The use of industrial standard simulation tools for antenna design and analysis Perfect for engineers and researchers who work in RF engineering or radar and radio communications, Antennas: From Theory to Practice, Second Edition will also earn a place on the bookshelves of university students seeking a concise and practical introduction to the basics of antennas and antenna design.

Side Reactions in Peptide Synthesis, based on the author's academic and industrial experience, and backed by a thorough review of the current literature, provides analysis of, and proposes solutions to, the most frequently encountered side reactions during peptide and peptidomimetic synthesis. This valuable handbook is ideal for research and process chemists working with peptide synthesis in diverse settings across academic, biotech, and pharmaceutical research and development. While peptide chemistry is increasingly prevalent, common side reactions and their causes are often poorly understood or anticipated, causing unnecessary waste of materials and delay. Each chapter discusses common side reactions through detailed chemical equations, proposed mechanisms (if any), theoretical background, and finally, a variety of possible solutions to avoid or alleviate the specified side reaction. - Provides a systematic examination on how to troubleshoot and minimize the most frequent side reactions in peptide synthesis - Gives chemists the background information and the practical tools they need to successfully troubleshoot and improve results - Includes optimization-oriented analysis of side reactions in peptide synthesis for improved industrial process development in peptidyl API (active pharmaceutical ingredient) production - Answers the growing, global need for improved, replicable processes to avoid impurities and maintain the integrity of the end product. - Presents a thorough discussion of critical factors in peptide synthesis which are often neglected or underestimated by chemists - Covers solid phase and solution phase methodologies, and provides abundant references for further exploration

For Yu Xin, who aimed to become a singer, she naturally had to put in a lot of effort in terms of her charisma. Although she said that, it didn't mean that she was just an empty vase. On the contrary, she, Yu Xin, was a famous and talented student in the Music Academy.

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"This book systematically outlines the complex relationship between modern new poetry and classical poetry traditions of China for the first time. Since its first publication in 1994, this book has been an important reference for the study of Chinese modern new poetry. This book consists of four chapters. The first three chapters analyzes the influence of Chinese classical poetry traditions on modern Chinese new poetry from a macro perspective while the last chapter uses case studies to illustrate influence of traditions on individual writers from a more micro perspective. Together, these four chapters provide a multi-layered elaboration on the complex relationship between traditional Chinese poetry and modern Chinese poetry. The author argues that Chinese modern new poetry does not completely deviate from Chinese classical poetry traditions, but draws nutrition from Chinese and Western poetry traditions and continues to form new traditions"--

This book investigates compressive sensing techniques to provide a robust and general framework for network data analytics. The goal is to introduce a compressive sensing framework for missing data interpolation, anomaly detection, data segmentation and activity recognition, and to demonstrate its benefits. Chapter 1 introduces compressive sensing, including its definition, limitation, and how it supports different network analysis applications. Chapter 2 demonstrates the feasibility of compressive sensing in network analytics, the authors we apply it to detect anomalies in the customer care call dataset from a Tier 1 ISP in the United States. A regression-based model is applied to find the relationship between calls and events. The authors illustrate that compressive sensing is effective in identifying important factors and can leverage the low-rank structure and temporal stability to improve the detection accuracy. Chapter 3 discusses that there are several challenges in applying compressive sensing to real-world data. Understanding the reasons behind the challenges is important for designing methods and mitigating their impact. The authors analyze a wide range of real-world traces. The analysis demonstrates that there are different factors that contribute to the violation of the low-rank property in real data. In particular, the authors find that (1) noise, errors, and anomalies, and (2) asynchrony in the time and frequency domains lead to network-induced ambiguity and can easily cause low-rank matrices to become higher-ranked. To address the problem of noise, errors and anomalies in Chap. 4, the authors propose a robust compressive sensing technique. It explicitly accounts for anomalies by decomposing real-world data represented in matrix form into a low-rank matrix, a sparse anomaly matrix, an error term and a small noise matrix. Chapter 5 addresses the problem of lack of synchronization, and the authors propose a data-driven synchronization algorithm. It can eliminate misalignment while taking into account the heterogeneity of real-world data in both time and frequency domains. The data-driven synchronization can be applied to any compressive sensing technique and is general to any real-world data. The authors illustrates that the combination of the two techniques can reduce the ranks of real-world data, improve the effectiveness of compressive sensing and have a wide range of applications. The networks are constantly generating a wealth of rich and diverse information. This information creates exciting opportunities for network analysis and provides insight into the complex interactions between network entities. However, network analysis often faces the problems of (1) under-constrained, where there is too little data due to feasibility and cost issues in collecting data, or (2) over-constrained, where there is too much data, so the analysis becomes unscalable. Compressive sensing is an effective technique to solve both problems. It utilizes the underlying data structure for analysis. Specifically, to solve the under-constrained problem, compressive sensing technologies can be applied to reconstruct the missing elements or predict the future data. Also, to solve the over-constraint problem, compressive sensing technologies can be applied to identify significant elements To support compressive sensing in network data analysis, a robust and general framework is needed to support diverse applications. Yet this can be challenging for real-world data where noise, anomalies and lack of synchronization are common. First, the number of unknowns for network analysis can be much larger than the number of measurements. For example, traffic engineering requires knowing the complete traffic matrix between all source and destination pairs, in order to properly configure traffic and avoid congestion. However, measuring the flow between all source and destination pairs is very expensive or even infeasible. Reconstructing data from a small number of measurements is an underconstrained problem. In addition, real-world data is complex and heterogeneous, and often violate the low-level assumptions required by existing compressive sensing techniques. These violations significantly reduce the applicability and effectiveness of existing compressive sensing methods. Third, synchronization of network data reduces the data ranks and increases spatial locality. However, periodic time series exhibit not only misalignment but also different frequencies, which makes it difficult to synchronize data in the time and frequency domains. The primary audience for this book is data engineers, analysts and researchers, who need to deal with big data with missing anomalous and synchronization problems. Advanced level students focused on compressive sensing techniques will also benefit from this book as a reference.

He had waited for her for a thousand years, but she had waited for him for a thousand years.The inexplicable transmigration allowed Mu Yu to live with that peerless man for eight years.He said, "You must help Xuanyuan Wuhen become the emperor.Mu Yu replied in disbelief, "Can I?"Eight years later he lost his heart, but still had to send her to his side.And when they met eight years later, he would never be able to extricate himself from her.When love idiots meet all the Handsome Man, is it her fortune or their misfortune?The Mu Rong Ling who didn't eat humans, the Feng Tian Lou who was as passionate as fire, the cold Xuanyuan Wuhen who was as cold as ice, the Xuanyuan Jing who stuck to her all the time, who was the man she fell in love with a thousand years ago? "Who is the man who has been waiting for her for a thousand years?"

Optimization techniques have been widely adopted to implement various data mining algorithms. In addition to well-known Support Vector Machines (SVMs) (which are based on quadratic programming), different versions of Multiple Criteria Programming (MCP) have been extensively used in data separations. Since optimization based data mining methods differ from statistics, decision tree induction, and neural networks, their theoretical inspiration has attracted many researchers who are interested in algorithm development of data mining. Optimization based Data Mining: Theory and Applications, mainly focuses on MCP and SVM especially their recent theoretical progress and real-life applications in various fields. These include finance, web services, bio-informatics and petroleum engineering, which has triggered the interest of practitioners who look for new methods to improve the results of data mining for knowledge discovery. Most of the material in this book is directly from the research and application activities that the authors’ research group has conducted over the last ten years. Aimed at practitioners and graduates who have a fundamental knowledge in data mining, it demonstrates the basic concepts and foundations on how to use optimization techniques to deal with data mining problems.