Bücher von Yang Liu

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 is devoted to investigating the policy design and effectiveness of financial and market-based instruments to promote energy efficiency financing. The concept of this monograph is to present the latest results related to energy efficiency funding schemes, energy efficiency obligations, voluntary agreements, auction mechanisms, and Super Energy Services Companies (Super ESCOs) in major jurisdictions across the world.  The book focuses on financial and market-based instruments as they deliver a price signal, which provides an incentive for firms to invest in innovation or implement more energy-efficient technologies and deliver energy savings while minimizing costs. Such instruments can have significant advantages for the government, supporting the fiscal sustainability of the government's energy efficiency efforts, requiring less enforcement than regulation and according the market flexibility to select the most cost-efficient technologies. This book is highly recommended to researchers, policy experts, and business specialists who seek an in-depth and up-to-date integrated overview of energy efficiency financing. 

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.

Seminar paper from the year 2018 in the subject Business economics - Banking, Stock Exchanges, Insurance, Accounting, , language: English, abstract:

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.

Technical Report from the year 2017 in the subject Economics - Statistics and Methods, , language: English, abstract: The estimation and grading of customer Probability of Default (PD) using a firm-wide master scale for internal portfolios is a well established procedure in many financial institutions. However, there are often discussions within the institutions about the comparison between the grade PDs, the portfolio average PD calculated across grades and the observed Default Rate (DR). The Bayesian estimator for the grade PD based on different prior distribution assumption is often referred to as an alternative over the simple default rate. In this paper, we apply and compare the approaches to derive the posterior distribution of grade PDs that is closely linked to the portfolio performance assumptions made according to the Master Scale. We then detail the numerical analysis of both informed and uninformed Bayesian estimators for the Grade PDs with simulated single and multi-period data sets. We compare the results in term of prior and posterior Density-in-Range (DiR) and Direction-of-Movement (DoM) for each Grade. With data analysis and preference of criteria, the approach and metrics can be used for both model development and validation, or other risk-awareness reporting and information update exercises to help understand the difference between expectation and observed data. More importantly, this approach provides a methodology to quantify such differences according to knowledge and understanding of the observer, which was not possible for simple approaches such as direct numerical comparison of Master Scale defined PD and the simple rate.

This book is a collection of original works by authors from all over the world on aspects of unemployment and job issues, seen from various angles and based on their recent research. It sheds light on fresh ideas on unemployment, such as the intergenerational approach and unemployment normalization, and offers solutions from diverse areas such as social economy development and policy-making. Practical issues regarding job creation and labor mobility are also covered. The book aims to provide not only a better understanding of the nature and extent of unemployment in various parts of the world but also solutions in diverse contexts.

This book illustrates the configuration of Western migrants' Other-identity during their reversed migration from the West to China

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 SpringerBrief presents a recent advancement in modeling and measurement of the effect of surface wettability on the defrost process. Carefully controlled laboratory measurements of the defrosting of cooled surfaces are used to reveal the effect of surface wetting properties on the extent and speed of frost removal by melting or slumping.