Study on evaluation of International Science and Technology Cooperation Project (ISTCP) in China

This paper presents an overview of evaluation of ISTCP in China. We discuss briefly the history of evaluation and the strengths and weaknesses of different assessment systems. On this basis, with Analytical Hierarchy Process (AHP), we establish evaluation indicator system for ISTCP that includes research project establishment evaluation, mid-period evaluation system, effect evaluation system, and confirm the value of each indicator. At the same time, we established expert database, project database, research organization database, researcher database etc. We therefore establish an evaluation platform for international science and technology cooperation project. We use it to realize full process supervision from evaluation expert selection to project management

Experimental investigation of water droplet impact and icing on hydrophobic surfaces with varying wettabilities

Ice formation and accumulation can lead to operational failure and risks for structures such as power transmission lines, aircrafts, offshore platforms, marine vessels and wind turbines. Liquid repellent surfaces could reduce ice accretion and improve asset integrity and safety in harsh environments. there are significant needs to probe how surface wettability affects the droplet impact, ice formation and ice accretion processes. This study presents experimental results of water droplet impact, droplet dynamics, and icing delay time on flat and inclined stainless-steel surfaces with varying wettabilities. Several different designs of the micro-structure of the hydrophobic surfaces are considered. The commercial hydrophobic coating from Aculon is also used to improve liquid repellency and reduce ice accumulation. It was found that the impact speed and surface wettability are significant factors to the droplet oscillation and the total icing time. The droplet oscillation time is significantly longer on a hydrophobic surface than on a hydrophilic one. Lower surface wettability also significantly increases the droplet total icing time. The droplet total icing time decreases with lower droplet temperature, larger droplet impact velocity, and smaller droplet diameter. The droplet shows a gliding phase on an inclined surface. The total icing time decreases on the inclined surface since the contact area increases due to the gliding process. for typical droplet icing process, the ice formation initiates at the solid-liquid interface and then propagates from bottom to top through the liquid-gas interface. The droplet bounces off from the angled superhydrophobic surface made by electrodeposition at room temperature

Droplet Impact, Spreading and Freezing on Metallic Surfaces of varying Wettability

Paper presented at 2018 Canadian Society of Mechanical Engineers International Congress, 27-30 May 2018.Ice formation and accumulation can lead to operational failure and risks for structures, including power transmission lines, aircraft, offshore platforms, marine vessels, and wind turbines. Liquid repellent and icephobic surfaces can reduce ice accretion and improve asset integrity and safety in harsh environments. There are significant needs to probe how wettability affects the droplet impact, ice nucleation and ice accretion processes on different kinds of micro-structured surfaces. This paper presents experimental results of droplet impact, icing delay time and ice accumulation on metallic surfaces with varying wettability. Several different designs of the hydrophobic surfaces are considered. A commercial hydrophobic coating is also used to enhance liquid repellent features and reduce ice accumulation. The results demonstrated that when the static contact angle increases, the total icing time increases, suggesting desirable icing delays. The total icing time decreases with lower surface temperature, higher impact velocity or smaller droplet diameter

Study on evaluation of International Science and Technology Cooperation Project (ISTCP) in China

This paper presents an overview of evaluation of ISTCP in China. We discuss briefly the history of evaluation and the strengths and weaknesses of different assessment systems. On this basis, with Analytical Hierarchy Process (AHP), we establish evaluation indicator system for ISTCP that includes research project establishment evaluation, mid-period evaluation system, effect evaluation system, and confirm the value of each indicator. At the same time, we established expert database, project database, research organization database, researcher database etc. We therefore establish an evaluation platform for international science and technology cooperation project. We use it to realize full process supervision from evaluation expert selection to project management

Optical Study of Liquid Crystal Lens Doped with Multiwalled Carbon Nanotubes

In this paper, a new kind of electrically controlled liquid crystal lens, which respond in a relatively fast time, is presented. The multiwalled carbon nanotubes are doped into liquid crystal to fabricate the liquid crystal lens. As 0.02 % concentration of multiwalled carbon nanotubes is uniformly distributed in the liquid crystal, the optical features of the liquid crystal lens are obviously improved. The liquid crystal lens with a diameter of 2.0 mm was fabricated with about 0.2 s response time and less than 5 Vrms applied voltage. The focal length can vary from 16 to 510 mm, and the operation voltage changes from 1.0 to 5.5 Vrms. This liquid crystal lens has the very attractive feature of submillisecond response time, which is a much faster response time in comparison with conventional liquid crystal lens. Thus, this kind of liquid crystal lens has high potential for implementation in many practical imaging applications and imaging commercialisation

Fast-Response Liquid Crystal Lens Doped with Multi-Walled Carbon Nanotubes

In this paper, a relatively fast-response liquid crystal (LC) lens was proposed, which was fabricated by a simple method. Multi-walled carbon nanotubes (MWCNTs) were utilized in fabricating the LC lens. As MWCNTs were doped into the LCs, the dielectric anisotropy of the mixture changed, which was the key factor in solving the technical barrier of slow response time. In experiments, the effects of doping with MWCNTs were demonstrated. The concentration of doped MWCNTs was discussed in detail, and the best concentration and doping method were analyzed. The relationship between the concentration and response time was also obtained. This LC lens had a sub-millisecond response time, which was a relatively fast response time in comparison to conventional LC lenses of pristine LCs. Thus, this proposed method could be considered as a new method to realize fast-response LC lens

A Survey on ChatGPT: AI-Generated Contents, Challenges, and Solutions

With the widespread use of large artificial intelligence (AI) models such as ChatGPT, AI-generated content (AIGC) has garnered increasing attention and is leading a paradigm shift in content creation and knowledge representation. AIGC uses generative large AI algorithms to assist or replace humans in creating massive, high-quality, and human-like content at a faster pace and lower cost, based on user-provided prompts. Despite the recent significant progress in AIGC, security, privacy, ethical, and legal challenges still need to be addressed. This paper presents an in-depth survey of working principles, security and privacy threats, state-of-the-art solutions, and future challenges of the AIGC paradigm. Specifically, we first explore the enabling technologies, general architecture of AIGC, and discuss its working modes and key characteristics. Then, we investigate the taxonomy of security and privacy threats to AIGC and highlight the ethical and societal implications of GPT and AIGC technologies. Furthermore, we review the state-of-the-art AIGC watermarking approaches for regulatable AIGC paradigms regarding the AIGC model and its produced content. Finally, we identify future challenges and open research directions related to AIGC.Comment: 20 pages, 6 figures, 4 table

Social-Aware Clustered Federated Learning with Customized Privacy Preservation

A key feature of federated learning (FL) is to preserve the data privacy of end users. However, there still exist potential privacy leakage in exchanging gradients under FL. As a result, recent research often explores the differential privacy (DP) approaches to add noises to the computing results to address privacy concerns with low overheads, which however degrade the model performance. In this paper, we strike the balance of data privacy and efficiency by utilizing the pervasive social connections between users. Specifically, we propose SCFL, a novel Social-aware Clustered Federated Learning scheme, where mutually trusted individuals can freely form a social cluster and aggregate their raw model updates (e.g., gradients) inside each cluster before uploading to the cloud for global aggregation. By mixing model updates in a social group, adversaries can only eavesdrop the social-layer combined results, but not the privacy of individuals. We unfold the design of SCFL in three steps. \emph{i) Stable social cluster formation. Considering users' heterogeneous training samples and data distributions, we formulate the optimal social cluster formation problem as a federation game and devise a fair revenue allocation mechanism to resist free-riders. ii) Differentiated trust-privacy mapping}. For the clusters with low mutual trust, we design a customizable privacy preservation mechanism to adaptively sanitize participants' model updates depending on social trust degrees. iii) Distributed convergence}. A distributed two-sided matching algorithm is devised to attain an optimized disjoint partition with Nash-stable convergence. Experiments on Facebook network and MNIST/CIFAR-10 datasets validate that our SCFL can effectively enhance learning utility, improve user payoff, and enforce customizable privacy protection

Trade Privacy for Utility: A Learning-Based Privacy Pricing Game in Federated Learning

To prevent implicit privacy disclosure in sharing gradients among data owners (DOs) under federated learning (FL), differential privacy (DP) and its variants have become a common practice to offer formal privacy guarantees with low overheads. However, individual DOs generally tend to inject larger DP noises for stronger privacy provisions (which entails severe degradation of model utility), while the curator (i.e., aggregation server) aims to minimize the overall effect of added random noises for satisfactory model performance. To address this conflicting goal, we propose a novel dynamic privacy pricing (DyPP) game which allows DOs to sell individual privacy (by lowering the scale of locally added DP noise) for differentiated economic compensations (offered by the curator), thereby enhancing FL model utility. Considering multi-dimensional information asymmetry among players (e.g., DO's data distribution and privacy preference, and curator's maximum affordable payment) as well as their varying private information in distinct FL tasks, it is hard to directly attain the Nash equilibrium of the mixed-strategy DyPP game. Alternatively, we devise a fast reinforcement learning algorithm with two layers to quickly learn the optimal mixed noise-saving strategy of DOs and the optimal mixed pricing strategy of the curator without prior knowledge of players' private information. Experiments on real datasets validate the feasibility and effectiveness of the proposed scheme in terms of faster convergence speed and enhanced FL model utility with lower payment costs.Comment: Accepted by IEEE ICC202

Emerging Theranostic Nanomaterials in Diabetes and Its Complications

Diabetes mellitus (DM) refers to a group of metabolic disorders that are characterized by hyperglycemia. Oral subcutaneously administered antidiabetic drugs such as insulin, glipalamide, and metformin can temporarily balance blood sugar levels, however, long-term administration of these therapies is associated with undesirable side effects on the kidney and liver. In addition, due to overproduction of reactive oxygen species and hyperglycemia-induced macrovascular system damage, diabetics have an increased risk of complications. Fortunately, recent advances in nanomaterials have provided new opportunities for diabetes therapy and diagnosis. This review provides a panoramic overview of the current nanomaterials for the detection of diabetic biomarkers and diabetes treatment. Apart from diabetic sensing mechanisms and antidiabetic activities, the applications of these bioengineered nanoparticles for preventing several diabetic complications are elucidated. This review provides an overall perspective in this field, including current challenges and future trends, which may be helpful in informing the development of novel nanomaterials with new functions and properties for diabetes diagnosis and therapy.Peer reviewe