Smart and Functional Polymers

This book is based on the Special Issue of the journal Molecules on “Smart and Functional Polymers”. The collected research and review articles focus on the synthesis and characterization of advanced functional polymers, polymers with specific structures and performances, current improvements in advanced polymer-based materials for various applications, and the opportunities and challenges in the future. The topics cover the emerging synthesis and characterization technology of smart polymers, core?shell structure polymers, stimuli-responsive polymers, anhydrous electrorheological materials fabricated from conducting polymers, reversible polymerization systems, and biomedical polymers for drug delivery and disease theranostics. In summary, this book provides a comprehensive overview of the latest synthesis approaches, representative structures and performances, and various applications of smart and functional polymers. It will serve as a useful reference for all researchers and readers interested in polymer sciences and technologies

Universal health coverage: The case of China

In less than a decade, China transformed its inadequate, unjust health care system in order to provide basic universal health coverage (UHC) for its people. What forces made it possible for China to achieve this? What kind of transformation took place? What are the impacts of these policy changes? What can we learn from China? Moreover, while China has achieved UHC in basic health services, this does not mean that everyone has equal access to the same quality of affordable health care. This paper, which uses a theory of political economy to analyse China´s policy changes and accomplishments, consists of four main sections. Section I reviews the historical development of the Chinese health care system from the 1950s through the 1990s, tracing the serious consequences of the policy shift in the 1980s when the health care system and health care delivery became privately financed and commercialized. Section II analyses the political economy factors that drove and shaped the reform of the Chinese health system, focusing on the politics, institutions and actors that synergistically led to the establishment of UHC in 2009. In this section, we modified slightly John Kingdon´s theory and used it to examine four main streams of forces to explain how China´s reform came about. (1) The problem stream shows how Chinese political leaders recognized a serious, widespread public discontent regarding health and then diagnosed the root causes of these health problems. (2) The policy stream examines how major stakeholders in the health sector proposed, and heatedly debated, different policy options based on their vested interests and ideologies. (3) The financial stream highlights how China´s health policy was driven by fiscal constraints. (4) The politics stream analyses the political factors that influenced the agenda setting and policy formulation of UHC in authoritarian China, albeit with limited political transparency. The paper tracks these streams with historical evidence to conclude that the policy changes for UHC in China were established by the convergence of these four streams. Section III presents the policy outcomes - the current financing structure of the UHC (i.e., the three different insurance schemes, their benefit packages, and key companion programmes to assure the supply of basic services). Based on quantitative evidence, we summarize the impacts of China´s UHC in terms of equitable access to health care, quality and affordability of health care, health outcomes, and financial risk protection from high and/or catastrophic medical expenses. Although China´s UHC was a great achievement, stark disparities remain between urban and rural residents in China, along with high health expenditure inflation rates arising from inefficiency and waste in the health care system. In section IV, we discuss the remaining challenges for China´s health care system and comment on the potential lessons of the Chinese experience for other nations

Analysis of Alphalactalbumin and Betalactoglobulin from the Rehydration of Bovine Colostrum Powder Using Cloud Point Extraction and Mass Spectrometry

Alphalactalbumin (α-La) and betalactoglobulin (β-Lg) in the rehydration of bovine colostrum powder were successfully separated by cloud point extraction using a nonionic surfactant Triton X-114. The effects of different factors, including the surfactant concentration, sample volume, electrolyte, and pH were discussed. The optimized conditions for cloud point extraction of alphalactalbumin (α-La) and betalactoglobulin (β-Lg) can be concluded that the best surfactant is 1% (w/v) Triton X-114, 200 μL of sample volume, 150 mmol/L NaCl, and 6% (w/v) sucrose. After cloud point extraction, the capillary electrophoresis is used to check the efficiency of the extraction procedure. The results had been effectively confirmed by the characterization with matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS)

Performance and Emission Optimisation of an Ammonia/Hydrogen Fuelled Linear Joule Engine Generator

This paper presents a Linear Joule Engine Generator (LJEG) powered by ammonia and hydrogen co-combustion to tackle decarbonisation in the electrification of transport propulsion systems. A dynamic model of the LJEG, which integrates mechanics, thermodynamics, and electromagnetics sub-models, as well as detailed combustion chemistry analysis for emissions, is presented. The dynamic model is integrated and validated, and the LJEG performance is optimised for improved performance and reduced emissions. At optimal conditions, the engine could generate 1.96 kWe at a thermal efficiency of 34.3% and an electrical efficiency of 91%. It is found that the electromagnetic force of the linear alternator and heat addition from the external combustor and engine valve timing have the most significant influences on performance, whereas the piston stroke has a lesser impact. The impacts of hydrogen ratio, oxygen concentration, inlet pressure, and equivalence ratio of ammonia-air on nitric oxide (NO) formation and reduction are revealed using a detailed chemical kinetic analysis. Results indicated that rich combustion and elevated pressure are beneficial for NO reduction. The rate of production analysis indicates that the equivalence ratio significantly changes the relative contribution among the critical NO formation and reduction reaction pathways

HDTR-Net: A Real-Time High-Definition Teeth Restoration Network for Arbitrary Talking Face Generation Methods

Talking Face Generation (TFG) aims to reconstruct facial movements to achieve high natural lip movements from audio and facial features that are under potential connections. Existing TFG methods have made significant advancements to produce natural and realistic images. However, most work rarely takes visual quality into consideration. It is challenging to ensure lip synchronization while avoiding visual quality degradation in cross-modal generation methods. To address this issue, we propose a universal High-Definition Teeth Restoration Network, dubbed HDTR-Net, for arbitrary TFG methods. HDTR-Net can enhance teeth regions at an extremely fast speed while maintaining synchronization, and temporal consistency. In particular, we propose a Fine-Grained Feature Fusion (FGFF) module to effectively capture fine texture feature information around teeth and surrounding regions, and use these features to fine-grain the feature map to enhance the clarity of teeth. Extensive experiments show that our method can be adapted to arbitrary TFG methods without suffering from lip synchronization and frame coherence. Another advantage of HDTR-Net is its real-time generation ability. Also under the condition of high-definition restoration of talking face video synthesis, its inference speed is $300\%$ faster than the current state-of-the-art face restoration based on super-resolution.Comment: 15pages, 6 figures, PRCV202

Efficient Core-selecting Incentive Mechanism for Data Sharing in Federated Learning

Federated learning is a distributed machine learning system that uses participants' data to train an improved global model. In federated learning, participants cooperatively train a global model, and they will receive the global model and payments. Rational participants try to maximize their individual utility, and they will not input their high-quality data truthfully unless they are provided with satisfactory payments based on their data quality. Furthermore, federated learning benefits from the cooperative contributions of participants. Accordingly, how to establish an incentive mechanism that both incentivizes inputting data truthfully and promotes stable cooperation has become an important issue to consider. In this paper, we introduce a data sharing game model for federated learning and employ game-theoretic approaches to design a core-selecting incentive mechanism by utilizing a popular concept in cooperative games, the core. In federated learning, the core can be empty, resulting in the core-selecting mechanism becoming infeasible. To address this, our core-selecting mechanism employs a relaxation method and simultaneously minimizes the benefits of inputting false data for all participants. However, this mechanism is computationally expensive because it requires aggregating exponential models for all possible coalitions, which is infeasible in federated learning. To address this, we propose an efficient core-selecting mechanism based on sampling approximation that only aggregates models on sampled coalitions to approximate the exact result. Extensive experiments verify that the efficient core-selecting mechanism can incentivize inputting high-quality data and stable cooperation, while it reduces computational overhead compared to the core-selecting mechanism

Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring

We present an effective and efficient method that explores the properties of Transformers in the frequency domain for high-quality image deblurring. Our method is motivated by the convolution theorem that the correlation or convolution of two signals in the spatial domain is equivalent to an element-wise product of them in the frequency domain. This inspires us to develop an efficient frequency domain-based self-attention solver (FSAS) to estimate the scaled dot-product attention by an element-wise product operation instead of the matrix multiplication in the spatial domain. In addition, we note that simply using the naive feed-forward network (FFN) in Transformers does not generate good deblurred results. To overcome this problem, we propose a simple yet effective discriminative frequency domain-based FFN (DFFN), where we introduce a gated mechanism in the FFN based on the Joint Photographic Experts Group (JPEG) compression algorithm to discriminatively determine which low- and high-frequency information of the features should be preserved for latent clear image restoration. We formulate the proposed FSAS and DFFN into an asymmetrical network based on an encoder and decoder architecture, where the FSAS is only used in the decoder module for better image deblurring. Experimental results show that the proposed method performs favorably against the state-of-the-art approaches. Code will be available at \url{https://github.com/kkkls/FFTformer}.Comment: Code will be available at \url{https://github.com/kkkls/FFTformer