Yang Liu

This book systematically introduces the powder bed laser melting technology and its application and summarizes the author's team's experience in scientific research, engineering development, and data accumulation in recent 15 years. It includes in-depth theoretical analysis and a lot of engineering experience in equipment debugging, process development, and material testing. The book takes the powder bed laser melting technology as the object and divides the content into 15 chapters. It is used as technical learning materials for researchers and engineering development personnel engaged in metal 3D printing.

This book presents the latest research on switching control, adaptive switching control, and their applications in the transient stability control and analysis of large-scale complex power systems. In large-scale complex power systems, renewable power generators, flexible power electronics converters, and distributed controllers are widely employed. Due to the poor overcurrent tolerance capability of power electronics converters and lacking of coordination mechanism, stability control in events, such as natural disasters, cascaded faults, and severe disturbances, is viewed as the key challenge in the operation of these systems. High-performance self-coordinated controllers are needed for the control of important power sources and power electronics converters. Adaptive switching controllers are a group of controllers designed by the authors for the control of various renewable power generators, synchronous generators, and modular multilevel converters. These controllers operate in a self-coordinated manner and aim to employ the largest transient control energy of converters and power sources. Imbalance between power generation and consumption is largely filled by the application of these controllers, and transient stability of power systems can be significantly improved. This book covers both the preliminary knowledge and key proofs in the design and stability analysis of adaptive switching control systems, and considerable simulation and experimental results are presented to illustrate the application and performance of the controllers. This book is used as a reference book for researchers and engineers in fields of electrical engineering and control engineering.

This book mainly focuses on the sampled-data control of logical networks. We believe that the methods (semi-tensor product of matrices), results (recent results on Boolean control networks under periodic sampled-data control, Boolean control networks under aperiodic sampled-data control, and logical control networks under event-triggered control) and topics (logical networks) in this book have become of particular interest to readers recently. Firstly, logical networks are of interest due to their rich range of applications in biology, game theory, coding, finite automata, graph theory, and other fields. Secondly, semi-tensor product of matrices offers a useful tool for formulating, analyzing and designing controllers for logical networks. Moreover, this book is the first to introduce sampled-data control into the study of logical control networks. All research results in this book are novel and worthy of further study. The book’s content is divided into three parts (Boolean control networks under periodic sampled-data control, Boolean control networks under aperiodic sampled-data control, and logical control networks under event-triggered control), which essentially progress from easier to more difficult. In addition, corresponding examples and diagrams are included in each section to facilitate understanding.

This open access book focuses on the practical application of Adaptive Dynamic Programming (ADP) in chemotherapy drug delivery, taking into account clinical variables and real-time data. ADP's ability to adapt to changing conditions and make optimal decisions in complex and uncertain situations makes it a valuable tool in addressing pressing challenges in healthcare and other fields. As optimization technology evolves, we can expect to see even more sophisticated and powerful solutions emerge.

This open access book focuses on the practical application of Adaptive Dynamic Programming (ADP) in chemotherapy drug delivery, taking into account clinical variables and real-time data. ADP's ability to adapt to changing conditions and make optimal decisions in complex and uncertain situations makes it a valuable tool in addressing pressing challenges in healthcare and other fields. As optimization technology evolves, we can expect to see even more sophisticated and powerful solutions emerge.

This book systematically introduces the powder bed laser melting technology and its application and summarizes the author's team's experience in scientific research, engineering development, and data accumulation in recent 15 years. It includes in-depth theoretical analysis and a lot of engineering experience in equipment debugging, process development, and material testing. The book takes the powder bed laser melting technology as the object and divides the content into 15 chapters. It is used as technical learning materials for researchers and engineering development personnel engaged in metal 3D printing.

This book presents the latest research on switching control, adaptive switching control, and their applications in the transient stability control and analysis of large-scale complex power systems. In large-scale complex power systems, renewable power generators, flexible power electronics converters, and distributed controllers are widely employed. Due to the poor overcurrent tolerance capability of power electronics converters and lacking of coordination mechanism, stability control in events, such as natural disasters, cascaded faults, and severe disturbances, is viewed as the key challenge in the operation of these systems. High-performance self-coordinated controllers are needed for the control of important power sources and power electronics converters. Adaptive switching controllers are a group of controllers designed by the authors for the control of various renewable power generators, synchronous generators, and modular multilevel converters. These controllers operate in a self-coordinated manner and aim to employ the largest transient control energy of converters and power sources. Imbalance between power generation and consumption is largely filled by the application of these controllers, and transient stability of power systems can be significantly improved. This book covers both the preliminary knowledge and key proofs in the design and stability analysis of adaptive switching control systems, and considerable simulation and experimental results are presented to illustrate the application and performance of the controllers. This book is used as a reference book for researchers and engineers in fields of electrical engineering and control engineering.

This book mainly focuses on the sampled-data control of logical networks. We believe that the methods (semi-tensor product of matrices), results (recent results on Boolean control networks under periodic sampled-data control, Boolean control networks under aperiodic sampled-data control, and logical control networks under event-triggered control) and topics (logical networks) in this book have become of particular interest to readers recently. Firstly, logical networks are of interest due to their rich range of applications in biology, game theory, coding, finite automata, graph theory, and other fields. Secondly, semi-tensor product of matrices offers a useful tool for formulating, analyzing and designing controllers for logical networks. Moreover, this book is the first to introduce sampled-data control into the study of logical control networks. All research results in this book are novel and worthy of further study. The book’s content is divided into three parts (Boolean control networks under periodic sampled-data control, Boolean control networks under aperiodic sampled-data control, and logical control networks under event-triggered control), which essentially progress from easier to more difficult. In addition, corresponding examples and diagrams are included in each section to facilitate understanding.

This book situates migrating individuals’ sense of Otherness in receiving countries front and center and systematically illustrates the configuration of Western migrants’ Other-identity during their reverse migration from the West to China, which has become a new destination of international migration due to its rise to prominence in the global labor market. Consequently, international migrants from Western countries, especially those with skills desired in China, have become this country’s main target in the global race for talent. In this context, this book attends to American migrants on the Chinese mainland, who are perceived as the prototypical waiguoren in this region, as an illuminating case, and illustrates the configuration of their Other-identity, rising from their intercultural adaptation as the privileged but marginalized Other in an asymmetric power structure. This book also attempts to reveal the condition and process of Chinese Othering of American migrants that exists but is far less openly discussed in China.

How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.

This thesis presents the development of theranostic gold nanostars (GNS) for multimodality cancer imaging and therapy. Furthermore, it demonstrates that a novel two-pronged treatment, combining immune-checkpoint inhibition and GNS-mediated photothermal nanotherapy, can not only eradicate primary treated tumors but also trigger immune responses to treat distant untreated tumors in a mouse animal model. Cancer has become a significant threat to human health with more than eight million deaths each year, and novel methods for cancer management to improve patients’ overall survival are urgently needed. The developed multifunctional GNS nanoprobe with tip-enhanced plasmonics in the near-infrared region can be combined with (1) surface-enhanced Raman spectroscopy (SERS), (2) two-photon photoluminescence (TPL), (3) X-ray computed tomography (CT), (4) magnetic resonance imaging (MRI), (5) positron emission tomography (PET), and (6) photothermal therapy (PTT) for cancer imaging and treatment. The ability of the GNS nanoprobe to detect submillimeter intracranial brain tumors was demonstrated using PET scan – a superior non-invasive imaging modality – in a mouse animal model. In addition, delayed rechallenge with repeated cancer cell injection in cured mice did not lead to new tumor formation, indicating generation of a memorized immune response to cancer. The biocompatible gold nanostars with superior capabilities for cancer imaging and treatment have great potential for translational medicine applications.

This thesis presents the development of theranostic gold nanostars (GNS) for multimodality cancer imaging and therapy. Furthermore, it demonstrates that a novel two-pronged treatment, combining immune-checkpoint inhibition and GNS-mediated photothermal nanotherapy, can not only eradicate primary treated tumors but also trigger immune responses to treat distant untreated tumors in a mouse animal model. Cancer has become a significant threat to human health with more than eight million deaths each year, and novel methods for cancer management to improve patients’ overall survival are urgently needed. The developed multifunctional GNS nanoprobe with tip-enhanced plasmonics in the near-infrared region can be combined with (1) surface-enhanced Raman spectroscopy (SERS), (2) two-photon photoluminescence (TPL), (3) X-ray computed tomography (CT), (4) magnetic resonance imaging (MRI), (5) positron emission tomography (PET), and (6) photothermal therapy (PTT) for cancer imaging and treatment. The ability of the GNS nanoprobe to detect submillimeter intracranial brain tumors was demonstrated using PET scan – a superior non-invasive imaging modality – in a mouse animal model. In addition, delayed rechallenge with repeated cancer cell injection in cured mice did not lead to new tumor formation, indicating generation of a memorized immune response to cancer. The biocompatible gold nanostars with superior capabilities for cancer imaging and treatment have great potential for translational medicine applications.