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Generative adversarial networks (GANs) have shown remarkable success in image synthesis, making GAN models themselves commercially valuable to legitimate model owners. Therefore, it is critical to technically protect the intellectual property of GANs. Prior works need to tamper with the training set or training process, and they are not robust to emerging model extraction attacks. In this paper, we propose a new ownership protection method based on the common characteristics of a target model and its stolen models. Our method can be directly applicable to all well-trained GANs as it does not require retraining target models. Extensive experimental results show that our new method can achieve the best protection performance, compared to the state-of-the-art methods. Finally, we demonstrate the effectiveness of our method with respect to the number of generations of model extraction attacks, the number of generated samples, different datasets, as well as adaptive attacks.

The outbreak of the COVID-19 pandemic triggers infodemic over online social media, which significantly impacts public health around the world, both physically and psychologically. In this paper, we study the impact of the pandemic on the mental health of influential social media users, whose sharing behaviours significantly promote the diffusion of COVID-19 related information. Specifically, we focus on subjective well-being (SWB), and analyse whether SWB changes have a relationship with their bridging performance in information diffusion, which measures the speed and wideness gain of information transmission due to their sharing. We accurately capture users' bridging performance by proposing a new measurement. Benefiting from deep-learning natural language processing models, we quantify social media users' SWB from their textual posts. With the data collected from Twitter for almost two years, we reveal the greater mental suffering of influential users during the COVID-19 pandemic. Through comprehensive hierarchical multiple regression analysis, we are the first to discover the strong {relationship} between social users' SWB and their bridging performance.

Additional file 1. A. Characteristics of the 39 studies included in the meta-analysis. B. Characteristics of the 5 studies not included in the meta-analysis due to lack of information.

The development of clustering heuristics has demonstrated that Bitcoin is not completely anonymous. Currently, existing clustering heuristics only consider confirmed transactions recorded in the Bitcoin blockchain. However, unconfirmed transactions in the mempool have yet to be utilized to improve the performance of the clustering heuristics. In this paper, we bridge this gap by combining unconfirmed and confirmed transactions for clustering Bitcoin addresses effectively. First, we present a data collection system for capturing unconfirmed transactions. Two case studies are performed to show the presence of user behaviors in unconfirmed transactions not present in confirmed transactions. Next, we apply the state-of-the-art clustering heuristics to unconfirmed transactions, and the clustering results can reduce the number of entities after applying, for example, the co-spend heuristics in confirmed transactions by 2.3%. Finally, we propose three novel clustering heuristics to capture specific behavior patterns in unconfirmed transactions, which further reduce the number of entities after the application of the co-spend heuristics by 9.8%. Our results demonstrate the utility of unconfirmed transactions in address clustering and further shed light on the limitations of anonymity in cryptocurrencies. To the best of our knowledge, this paper is the first to apply the unconfirmed transactions in Bitcoin to cluster addresses., Comment: 15 pages, 13 figures, 4 tables. typos corrected

Additional file 2. Results of quality assessment using the Newcastle-Ottawa Scale for cohort studies.

Additional file 2. Results of quality assessment using the Newcastle-Ottawa Scale for cohort studies.

Additional file 3. Sensitivity analyses of association between smoking status and risk of prostate cancer.

Diffusion models have been remarkably successful in data synthesis. Such successes have also driven diffusion models to apply to sensitive data, such as human face data, but this might bring about severe privacy concerns. In this work, we systematically present the first privacy study about property inference attacks against diffusion models, in which adversaries aim to extract sensitive global properties of the training set from a diffusion model, such as the proportion of the training data for certain sensitive properties. Specifically, we consider the most practical attack scenario: adversaries are only allowed to obtain synthetic data. Under this realistic scenario, we evaluate the property inference attacks on different types of samplers and diffusion models. A broad range of evaluations shows that various diffusion models and their samplers are all vulnerable to property inference attacks. Furthermore, one case study on off-the-shelf pre-trained diffusion models also demonstrates the effectiveness of the attack in practice. Finally, we propose a new model-agnostic plug-in method PriSampler to mitigate the property inference of diffusion models. PriSampler can be directly applied to well-trained diffusion models and support both stochastic and deterministic sampling. Extensive experiments illustrate the effectiveness of our defense and it makes adversaries infer the proportion of properties as close as random guesses. PriSampler also shows its significantly superior performance to diffusion models trained with differential privacy on both model utility and defense performance.

Recent years have witnessed the tremendous success of diffusion models in data synthesis. However, when diffusion models are applied to sensitive data, they also give rise to severe privacy concerns. In this paper, we systematically present the first study about membership inference attacks against diffusion models, which aims to infer whether a sample was used to train the model. Two attack methods are proposed, namely loss-based and likelihood-based attacks. Our attack methods are evaluated on several state-of-the-art diffusion models, over different datasets in relation to privacy-sensitive data. Extensive experimental evaluations show that our attacks can achieve remarkable performance. Furthermore, we exhaustively investigate various factors which can affect attack performance. Finally, we also evaluate the performance of our attack methods on diffusion models trained with differential privacy.

Additional file 3. Sensitivity analyses of association between smoking status and risk of prostate cancer.

To address the vaccine hesitancy which impairs the efforts of the COVID-19 vaccination campaign, it is imperative to understand public vaccination attitudes and timely grasp their changes. In spite of reliability and trustworthiness, conventional attitude collection based on surveys is time-consuming and expensive, and cannot follow the fast evolution of vaccination attitudes. We leverage the textual posts on social media to extract and track users' vaccination stances in near real time by proposing a deep learning framework. To address the impact of linguistic features such as sarcasm and irony commonly used in vaccine-related discourses, we integrate into the framework the recent posts of a user's social network neighbours to help detect the user's genuine attitude. Based on our annotated dataset from Twitter, the models instantiated from our framework can increase the performance of attitude extraction by up to 23% compared to state-of-the-art text-only models. Using this framework, we successfully validate the feasibility of using social media to track the evolution of vaccination attitudes in real life. We further show one practical use of our framework by validating the possibility to forecast a user's vaccine hesitancy changes with information perceived from social media.

The combinations of evolutionary algorithms (EA) and analytical methods have been extensively studied in the fields of machine learning in recent years. This paper focuses on how to combine a differential evolution (DE) algorithm with the semi-supervised classification algorithm based on extreme learning machine (ELM). Firstly, this paper proposes a semi-supervised classification algorithm based on DE and ELM (DE-ELM-SSC) with roughly three steps. Firstly, multiple differential evolution strategies are adopted to optimize the input weights and hidden biases of extreme learning machine, and an optimal strategy for the target data set is selected according to the root mean square error (RMSE). Secondly, the optimal evolutionary strategy selected in the previous step is applied to the DE algorithm to optimize the ELM network parameters. Thirdly, in order to construct a semi-supervised classification model, tri-training technology is used to realize the cooperative training of three improved ELM base classifiers. Then, a nonlinear method is adopted to improve the existing inertial strategy method and realize adaptive adjustment of scaling factor, so as to optimize the DE-ELM-SSC algorithm to obtain the DE-ELM-SSC+ algorithm. Lastly, a large number of experimental results on UCI data sets show that the DE-ELM-SSC+ algorithm outperforms the baseline methods with higher classification accuracy because of evolution strategy selection and improved scaling factor adaptive adjustment.

Network embedding (NE) approaches have emerged as a predominant technique to represent complex networks and have benefited numerous tasks. However, most NE approaches rely on a homophily assumption to learn embeddings with the guidance of supervisory signals, leaving the unsupervised heterophilous scenario relatively unexplored. This problem becomes especially relevant in fields where a scarcity of labels exists. Here, we formulate the unsupervised NE task as an r-ego network discrimination problem and develop the SELENE framework for learning on networks with homophily and heterophily. Specifically, we design a dual-channel feature embedding pipeline to discriminate r-ego networks using node attributes and structural information separately. We employ heterophily adapted self-supervised learning objective functions to optimise the framework to learn intrinsic node embeddings. We show that SELENE's components improve the quality of node embeddings, facilitating the discrimination of connected heterophilous nodes. Comprehensive empirical evaluations on both synthetic and real-world datasets with varying homophily ratios validate the effectiveness of SELENE in homophilous and heterophilous settings showing an up to 12.52% clustering accuracy gain., Accepted to Transactions on Machine Learning Research

Graph Neural Networks (GNNs) have been predominant for graph learning tasks; however, recent studies showed that a well-known graph algorithm, Label Propagation (LP), combined with a shallow neural network can achieve comparable performance to GNNs in semi-supervised node classification on graphs with high homophily. In this paper, we show that this approach falls short on graphs with low homophily, where nodes often connect to the nodes of the opposite classes. To overcome this, we carefully design a combination of a base predictor with LP algorithm that enjoys a closed-form solution as well as convergence guarantees. Our algorithm first learns the class compatibility matrix and then aggregates label predictions using LP algorithm weighted by class compatibilities. On a wide variety of benchmarks, we show that our approach achieves the leading performance on graphs with various levels of homophily. Meanwhile, it has orders of magnitude fewer parameters and requires less execution time. Empirical evaluations demonstrate that simple adaptations of LP can be competitive in semi-supervised node classification in both homophily and heterophily regimes.

Boolean network is a popular and well-established modelling framework for gene regulatory networks. The steady-state behaviour of Boolean networks can be described as attractors, which are hypothesised to characterise cellular phenotypes. In this work, we study the target control problem of Boolean networks, which has important applications for cellular reprogramming. More specifically, we want to reduce the total number of attractors of a Boolean network to a single target attractor. Different from existing approaches to solving control problems of Boolean networks with node perturbations, we aim to develop an approach utilising edgetic perturbations. Namely, our objective is to modify the update functions of a Boolean network such that there remains only one attractor. The design of our approach is inspired by Thomas’ first rule, and we primarily focus on the removal of cycles in the interaction graph of a Boolean network. We further use results in the literature to only remove positive cycles which are responsible for the appearance of multiple attractors. We apply our solution to a number of real-life biological networks modelled as Boolean networks, and the experimental results demonstrate its efficacy and efficiency.

Drug-based hyperbranched polymers have attracted broad interest from scientists due to their attractive and distinctive characteristics in cancer chemotherapy. However, the only use of the single-drug treatment modality could lead to low or unsatisfactory anticancer activity because of the limited treatment strategy and multidrug resistance of tumour cells. In this work, a novel dual drug-based hyperbranched polymer (DHBP) simultaneously containing methotrexate (MTX) and chlorambucil (Cb) was proposed to realize synergistic cancer chemotherapy. After modification with PEG, the obtained DHBP-g-PEG self-assembled into hyperbranched polymer micelles (HBPMs) in aqueous solution with a high drug loading capacity and stable drug transport. Notably, HBPMs with excellent controlled drug release in tumour cells exhibited an obviously superior synergistic effect and possessed significant therapeutic efficiency and in vitro anticancer activity, as compared to either the individual drug or mixture drug treatment group. All these results demonstrate that the unique advantage of HBPMs may have significant potential for application in the field of synergistic cancer chemotherapy.

In the form of satellite ephemerides and clock parameters, the space datum and system time information of one global navigation satellite system (GNSS) is transferred to users. With the continuous updating in the satellite payload such as the high-precision atomic clock, the monitoring and tracking technique such as the inter-satellite link, and in the data processing technique, the accuracy and real-time performance of the satellite ephemeris and clock error products are steadily improved. Starting from December 27th, 2018, the BeiDou Navigation System 3, or BDS-3, has provided the accurate and reliable basic positioning, navigation, and timing (PNT) service for the users in the countries within the “one belt and one road”. This paper has summarized the faced challenges of the precise orbit determination and time synchronization from the regional BDS-2 system to the BDS-3 global system, and the specific solutions at the control segment. In addition, this paper has compared the BDS with other GNSS systems in terms of technical characteristics. Finally, aiming at a higher accuracy and more reliable PNT service, the road map of precise orbit determination and time synchronization technique for the next generation navigation systems is discussed, which will provide a reference for developing the global navigation satellite systems with an even higher accuracy.

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

Macroeconomic models are the most common analytical tools to assess economy-wide impacts of climate change policies. These models are, however, not capable of representing detailed physical characteristics of energy production and combustion technologies and often lead to the overestimation of economic impacts. One solution to address this problem is to link top-down macroeconomic models with bottom-up energy sector models that can represent technological details. This study develops a hybrid model by linking a top-down computable general equilibrium model with a bottom-up energy sector model and implements it to assesses economic impacts of emission reduction targets in China set under the Paris Climate Agreement. Results show that economic impacts assessed by the hybrid model are nearly three times smaller than that assessed by the top-down model alone.

An information outbreak occurs on social media along with the COVID-19 pandemic and leads to infodemic. Predicting the popularity of online content, known as cascade prediction, allows for not only catching in advance hot information that deserves attention, but also identifying false information that will widely spread and require quick response to mitigate its impact. Among the various information diffusion patterns leveraged in previous works, the spillover effect of the information exposed to users on their decision to participate in diffusing certain information is still not studied. In this paper, we focus on the diffusion of information related to COVID-19 preventive measures. Through our collected Twitter dataset, we validated the existence of this spillover effect. Building on the finding, we proposed extensions to three cascade prediction methods based on Graph Neural Networks (GNNs). Experiments conducted on our dataset demonstrated that the use of the identified spillover effect significantly improves the state-of-the-art GNNs methods in predicting the popularity of not only preventive measure messages, but also other COVID-19 related messages.

This paper reports on recent research advances in supervisor synthesis, as well as industrial applications and future research challenges, especially in the context of a research project funded by Rijkswaterstaat, responsible for the construction and maintenance of infrastructure in the Netherlands.

Following advances in hardware engineering (multi-core processors) and software engineering (agile practices), there is now a large demand for unit-testing techniques for concurrent code. This paper presents the motivation, problem, proposed solution, first results, and open challenges of an early-stage research project (2019-2022) that aims to develop innovative such techniques. Founded on existing work on coordination models and languages, the project's idea is to use a combination of domain-specific language, compilation, and model-checking to build a fully automated framework for unit-testing concurrency.

Model extraction attacks aim to duplicate a machine learning model through query access to a target model. Early studies mainly focus on discriminative models. Despite the success, model extraction attacks against generative models are less well explored. In this paper, we systematically study the feasibility of model extraction attacks against generative adversarial networks (GANs). Specifically, we first define accuracy and fidelity on model extraction attacks against GANs. Then we study model extraction attacks against GANs from the perspective of accuracy extraction and fidelity extraction, according to the adversary's goals and background knowledge. We further conduct a case study where an adversary can transfer knowledge of the extracted model which steals a state-of-the-art GAN trained with more than 3 million images to new domains to broaden the scope of applications of model extraction attacks. Finally, we propose effective defense techniques to safeguard GANs, considering a trade-off between the utility and security of GAN models.

We study the target control of asynchronous Boolean networks, to identify efficacious interventions that can drive the dynamics of a given Boolean network from any initial state to the desired target attractor. Based on the application time, the control can be realised with three types of perturbations, including instantaneous, temporary and permanent perturbations. We develop efficient methods to compute the target control for a given target attractor with three types of perturbations. We compare our methods with the stable motif-based control on a variety of real-life biological networks to evaluate their performance. We show that our methods scale well for large Boolean networks and they are able to identify a rich set of solutions with a small number of perturbations., arXiv admin note: substantial text overlap with arXiv:2006.02304

The research field between Artificial Intelligence and Health sciences has established itself as a central research direction in recent years and has also further increased social interest. On the one hand, this is due to the emergence of medical mass data and their use for AI-related fields, such as machine learning, human-computer interfaces and natural language-processing systems, and on the other hand, it is also due to the steadily growing social interest, which is not determined by the current Covid 19 pandemic. To this end, the lecture series is intended to provide an opportunity for scientific exchange.

Recently, increasing attention has been paid to heterogeneous graph representation learning (HGRL), which aims to embed rich structural and semantic information in heterogeneous information networks (HINs) into low-dimensional node representations. To date, most HGRL models rely on hand-crafted meta-paths. However, the dependency on manually-defined meta-paths requires domain knowledge, which is difficult to obtain for complex HINs. More importantly, the pre-defined or generated meta-paths of all existing HGRL methods attached to each node type or node pair cannot be personalised to each individual node. To fully unleash the power of HGRL, we present a novel framework, Personalised Meta-path based Heterogeneous Graph Neural Networks (PM-HGNN), to jointly generate meta-paths that are personalised for each individual node in a HIN and learn node representations for the target downstream task like node classification. Precisely, PM-HGNN treats the meta-path generation as a Markov Decision Process and utilises a policy network to adaptively generate a meta-path for each individual node and simultaneously learn effective node representations. The policy network is trained with deep reinforcement learning by exploiting the performance improvement on a downstream task. We further propose an extension, PM-HGNN++, to better encode relational structure and accelerate the training during the meta-path generation. Experimental results reveal that both PM-HGNN and PM-HGNN++ can significantly and consistently outperform 16 competing baselines and state-of-the-art methods in various settings of node classification. Qualitative analysis also shows that PM-HGNN++ can identify meaningful meta-paths overlooked by human knowledge.

Jun Li,1,* Donggen Jiang,1,* Qian Zhang,2,* Shubin Peng,3 Guolong Liao,1 Xiangwei Yang,1 Jiani Tang,1 Haiyun Xiong,1 Jun Pang1 1Department of Urology, Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 518107, People’s Republic of China; 2Department of Rehabilitation Medicine, Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 518107, People’s Republic of China; 3Department of Urology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jun Pang; Haiyun XiongDepartment of Urology, Seventh Affiliated Hospital, Sun Yat-Sen University, 628 Zhenyuan Road, Shenzhen 518107, People’s Republic of ChinaTel +86-18802044177; +86-18922869986Email pangjun2@mail.sysu.edu.cn; xionghy7@mail.sysu.edu.cnObjective: Renal cell carcinoma (RCC) displays an increasing incidence and mortality rate worldwide in recent years. More and more evidence demonstrated microRNAs function as positive or negative regulatory factors in many cancers, while the role of miR-301a in RCC is still unclear.Material and Methods: The expression and clinical significance of miR-301a were assessed via bioinformatic software on open microarray datasets of the Cancer Genome Atlas (TCGA) and then confirmed by quantitative real-time PCR (qRT-PCR) in RCC cell lines. Loss of function assays were performed in RCC cell lines both in vitro and in vivo. Cell Counting Kit-8 (CCK-8), flow cytometry, luciferase reporter assays, Western blotting, and immunohistochemistry were employed to explore the mechanisms of the effect of miR-301a on RCC.Results: By analyzing RCC clinical specimens and cell lines, we found a uniform increased miR-301a in expression in comparison with normal renal tissue or normal human proximal tubule epithelial cell line (HK-2). In addition, miR-301a upregulation correlated advanced stage and poor prognosis of clear cell RCC (ccRCC). Anti-miR-301a could inhibit growth and cell cycle G1/S transition in RCC cell lines. Moreover, we found that PTEN was identified as a direct target of miR-301a that might partially interrupt miR-301a-induced G1/S transition. Importantly, nude-mouse models revealed that knockdown of miR-301a delayed tumor growth.Conclusion: These results indicate that miR-301a functions as a tumor-promoting miRNA through regulating PTEN expression, representing a novel therapeutic target for RCC.Keywords: miR-301a, PTEN, renal cell carcinoma, proliferation, G1/S transition

Supplemental Digital Content is available in the text, Background: Traditional coronary angiography (CA) as a main technique has been used to determine the coronary artery anatomy and guide percutaneous coronary intervention (PCI). We mainly focused on whether the new techniques could improve the patients’ mortality, major adverse cardiovascular events (MACEs), and myocardial infarction. Methods: For the network meta-analysis, we searched the trials of different PCI guidances from MEDLINE, Current Contents Connect, Google Scholar, EMBASE, Cochrane Library, PubMed, Science Direct, and Web of Science. The last search date was December 10, 2018. Results: The analyses of all results found that there was no significant difference in mortality among the groups. Randomized clinical trials (RCT) analysis showed that intravascular ultrasound (IVUS)-guided PCI was significantly superior to CA, fractional flow reserve, instantaneous wave-free ratio, optical coherence tomography. However, CA, fractional flow reserve, instantaneous wave-free ratio, and optical coherence tomography showed no difference in reducing mortality. The analyses of all results found that there was no significant difference in the incidence of MACEs among the groups. RCTs analysis showed that IVUS-guided PCI was significantly superior to CA, but there was no significant difference among the other groups. The analyses of all results or RCTs showed that there was no significant difference in myocardial infarction incidence among the groups. Conclusion: IVUS-guided PCI is an effective method to decrease all-cause death MACEs.

Direct cell reprogramming makes it feasible to reprogram abundant somatic cells into desired cells. It has great potential for regenerative medicine and tissue engineering. In this work, we study the control of biological networks, modelled as Boolean networks, to identify control paths driving the dynamics of the network from a source attractor (undesired cells) to the target attractor (desired cells). Instead of achieving control in one step, we develop attractor-based sequential temporary and permanent control methods (AST and ASP) to identify a sequence of interventions that can alter the dynamics in a stepwise manner. To improve their feasibility, both AST and ASP only use biologically observable attractors as intermediates. They can find the shortest sequential paths and guarantee 100% reachability of the target attractor. We apply the two methods to several real-life biological networks and compare their performance with the attractor-based sequential instantaneous control (ASI). The results demonstrate that AST and ASP have the ability to identify a richer set of control paths with fewer perturbations than ASI, which will greatly facilitate practical applications.

We have grown germanium selenide (GeSe) triangular nanoplate arrays (TNAs) with a high density (3.82E+6 / mm2) on the Si (111) substrate using a simple thermal evaporation method. The thickness and trilateral lengths of a single triangular nanoplate were statistically estimated by atomic force microscopy (AFM) as 44 nm, 365 nm, 458 nm and 605 nm, respectively. Transmission electron microscopy (TEM) images and X-ray diffraction (XRD) patterns show that the TNAs were composed of single crystalline GeSe phase. The Se-related defects in the lattice were also revealed by TEM images and Raman vibration modes. Unlike previously reported GeSe compounds, the GeSe TNAs exhibited temperature-dependent photoluminescence (PL). In addition, not previously reported PL peak (1.25 eV) of the 44 nm thick TNAs at 5 K was in the gaps between those of GeSe monolayers (1.5 nm) and thin films (400 nm), revealing a close relationship between the PL peak and the thickness of GeSe. The high-density structure and temperature-dependent PL of the TNAs on the Si substrate may be useful for temperature controllable semiconductor nanodevices.

The research field between Artificial Intelligence and Health sciences has established itself as a central research direction in recent years and has also further increased social interest. On the one hand, this is due to the emergence of medical mass data and their use for AI-related fields, such as machine learning, human-computer interfaces and natural language-processing systems, and on the other hand, it is also due to the steadily growing social interest, which is not determined by the current Covid 19 pandemic. To this end, the lecture series is intended to provide an opportunity for scientific exchange.

Vibration signal is the main measurement signal of mechanical component fault diagnosis, and the presence of noise affects the feature extraction of the signal and the final fault diagnosis, so the test signal in practice needs to be denoised. The use of wavelet transform does not filter out the noise in the signal very well. This is because the hard threshold function is not continuous and some useful information is filtered out. There is a deviation in the soft threshold function and the noise in the signal cannot be completely filtered out. And the traditional threshold is fixed. In order to solve the problem of threshold function and threshold, this paper proposes a new threshold function, and uses artificial fish swarm algorithm to get the optimal threshold. Finally, the superiority and practicability of the method are verified by the unsteady test signal and the bearing dataset of Case Western Reserve University. From the final noise reduction result, the method can achieve better performance in noise reduction than other existing methods. The signal-to-noise ratio obtained by this method is 13%∼16% higher than other methods. The root mean square error is 10%∼41% lower than other methods.

Reinforced concrete beams can be effectively strengthened by FRP plates or attaching steel plates on the soffit. The performance of this type of composite beam is controlled by the interaction at the steel-concrete interface. In this study, a total of three full-scale reinforced concrete beams, strengthened with steel plates in use of different types of strengthening methods, were tested, including one reinforced concrete beam without strengthening. In this program, a new simplified anchoring system (direct shear bolting-plate system) has been proposed for strengthening a reinforced concrete beam. Its behavior was compared to the available test results from the commonly used strengthening methods. The experimental results shows that this modified anchoring system is capable of effectively reducing the debonding failure between concrete and steel plates and controlling crack development after steel yielding. It was also found that the direct shear anchoring system is more effective in increasing the flexural strength of a reinforced concrete beam and reducing the longitudinal slipping between structural components, which was eventually for increasing the capability between concrete beams and steel plates to work together.

Many countries have undertaken market-oriented reforms of the power sector over the past four decades. However, the literature has not investigated whether the reforms have contributed to economic development. This study aims to assess the potential macroeconomic impacts of an element of the power sector reform process that China started in 2015. It uses an energy sector TIMES model and a computable general equilibrium model. The study finds that the price of electricity in China would be around 20 percent lower than the country is likely to experience in 2020, if the country follows the market principle to operate the power system. The reduction in the price of electricity would spill over throughout the economy, resulting in an increase in gross domestic product of more than 1 percent in 2020. It would also increase household income, economic welfare, and international trade.

Top-down economic models, such as computable general equilibrium models, are the common tools to assess the economic impacts of climate change policies. However, these models are incapable of representing the detailed technological characteristics of the sources of greenhouse gas emissions. The economic impacts measured by the top-down economic models are likely to be overestimated. This study attempts to quantify the overestimation by measuring the economic impacts linking the top-down model with a bottom-up engineering model for the energy sector. The study uses meeting China's pledges under the Paris Agreement for testing this hypothesis. The study shows that the economic impacts measured by the stand-alone top-down model are almost three times as high as those resulting from the model after linking it with the bottom-up model. However, the findings are sensitive to the assumptions and existing or planned policies on energy technologies considered in the bottom-up model.

This study analyzes the potential impacts of a national emission trading scheme on provincial economies in China of meeting China's emission reduction pledges, the Nationally Determined Contributions announced under the Paris Agreement. The study developed a multiregional, multisectoral, recursive-dynamic computable general equilibrium model and calibrated it with the latest provincial-level social accounting matrices (2012). The study shows that meeting China's Nationally Determined Contributions through an emission trading scheme would reduce almost 30 percent of the emission reduction from the business as usual scenario in 2030. If the baseline is corrected based on information from a bottom-up energy sector model, TIMES, the required reduction of emissions from the baseline in 2030 drops by half, to 15 percent. At the national level, the emission trading scheme would cause a 1.2 to 1.5 percent reduction in gross domestic product from the business as usual scenario in 2030. If the baseline is corrected, the impact on gross domestic product drops by two-thirds. The emission trading scheme would cause some provincial economies to gain and others to lose. The economic impacts are highly sensitive to the allowance allocation rules. Not only the magnitudes, but also the directions of the economic impacts alter when the allocation rules change. The provinces that rely on coal mining or coal-intensive manufacturing industries are found to experience relatively larger economic losses irrespective of the allowance allocation rules.

Given the sensitive nature of health data, security and privacy in e-health systems is of prime importance. It is crucial that an e-health system must ensure that users remain private - even if they are bribed or coerced to reveal themselves, or others: a pharmaceutical company could, for example, bribe a pharmacist to reveal information which breaks a doctor's privacy. In this paper, we first identify and formalise several new but important privacy properties on enforcing doctor privacy. Then we analyse the security and privacy of a complicated and practical e-health protocol (DLV08). Our analysis uncovers ambiguities in the protocol, and shows to what extent these new privacy properties as well as other security properties (such as secrecy and authentication) and privacy properties (such as anonymity and untraceability) are satisfied by the protocol. Finally, we address the found ambiguities which result in both security and privacy flaws, and propose suggestions for fixing them.

Given a Boolean network BN and a subset A of attractors of BN, we study the problem of identifying a minimal subset C of vertices of BN, such that the dynamics of BN can reach from a state s in any attractor As in A to any attractor At in A by controlling or toggling a subset of vertices in C in a single time step. We describe a method based on the decomposition of the network structure into strongly connected components called blocks. The control subset can be locally computed for each such block and the results then merged to derive the global control subset C. This potentially improves the efficiency for many real-life networks that are large but modular and well-structured. We are currently in the process of implementing our method in software., arXiv admin note: text overlap with arXiv:1804.07221

We study the problem of computing a minimal subset of nodes of a given asynchronous Boolean network that need to be controlled to drive its dynamics from an initial steady state (or attractor) to a target steady state. Due to the phenomenon of state-space explosion, a simple global approach that performs computations on the entire network, may not scale well for large networks. We believe that efficient algorithms for such networks must exploit the structure of the networks together with their dynamics. Taking such an approach, we derive a decomposition-based solution to the minimal control problem which can be significantly faster than the existing approaches on large networks. We apply our solution to both real-life biological networks and randomly generated networks, demonstrating promising results., Comment: 18 pages

Based on the experiments of utilization of garlic processing wastewater in a lotus pond, this study demonstrates that lotus pond wetlands have a remarkable ability to remove organic pollutants and decrease chemical oxygen demand (CODCr), biochemical oxygen demand (BOD5), and suspended substances (SS) in garlic processing wastewater. Results also show evident effects of lotus roots on absorption of NH3-N. The pH value in a lotus pond with wastewater discharged was relatively stable. The water quality in the lotus pond reached the class II emission standard, according to the Integrated Wastewater Discharge Standard (GB8978-1996), seven days after pretreated garlic processing wastewater had been discharged into the lotus pond. Garlic processing wastewater irrigation does not produce pollution in the pond sediment and has no negative effect on the growth of lotus roots. Due to utilization of garlic processing wastewater, the output of lotus roots increased by 3.0% to 8.3%, and the quality of lotus roots was improved. Therefore, better purification and utilization results can be achieved.

Boolean networks is a well-established formalism for modelling biological systems. A vital challenge for analysing a Boolean network is to identify all the attractors. This becomes more challenging for large asynchronous Boolean networks, due to the asynchronous updating scheme. Existing methods are prohibited due to the well-known state-space explosion problem in large Boolean networks. In this paper, we tackle this challenge by proposing a SCC-based decomposition method. We prove the correctness of our proposed method and demonstrate its efficiency with two real-life biological networks., Comment: 28 pages, version 3 (correct a mistake in Table 2)

Based on the MAP-CGE model, this paper simulated the impacts on the output, energy consumption and pollutant emissions of different cement production processes when implementing a low carbon cement standard in China. It also calculated the impacts on the marginal abatement cost and equilibrium price of the cement industry, and analyzed the co-abatement effects of different pollutants. The results showed that implementing the low carbon cement standard will be beneficial in promoting an upgrading of cement production processes, and strengthening the energy conservation and emission reduction in the cement industry. If there is no change in the existing technology, the cement industry will reduce SO 2 emissions by 1.17 kg and NO x emissions by 4.44 kg per ton of CO 2 emission reduction. Implementing low carbon cement standard can also promote NO x abatement in the cement industry. However, the cement industry will bear the abatement costs, and their equilibrium price will increase slightly. Citation Pang, J., Y.-C. Shi, X.-Z. Feng, et al., 2014: Analysis on impacts and co-abatement effects of implementing the low carbon cement standard. Adv . Clim . Change Res ., 5 (1), doi: 10.3724/SP.J.1248.2014.041.

In order to discuss the features of electrons in negative corona, a 2D, multi-component, and plasma chemical improved model is detailedly presented for the simulation of rod-plate DC corona discharge in dry air (O 2 :N 2 =1:4). The model is based on plasma hydrodynamics and chemical models in which 28 reactions are considered. The simulation is established with a rod-plate electrode configuration with an inter-electrode gap of 3.3 mm, the voltage applied to the rod is DC −5.0 kV, the pressure in air discharge fixed at standard atmospheric pressure (1.0 atm), and the ambient temperature assumed to be a constant (300 K). Based on this improved model, electron mean energy distribution, electron density distribution, generation and dissipation rates of electron at 6 typical time points during a pulse period are analysed emphatically.

Probabilistic Boolean networks (PBNs) is an important mathematical framework widely used for modelling and analysing biological systems. PBNs are suited for modelling large biological systems, which more and more often arise in systems biology. However, the large system size poses a~significant challenge to the analysis of PBNs, in particular, to the crucial analysis of their steady-state behaviour. Numerical methods for performing steady-state analyses suffer from the state-space explosion problem, which makes the utilisation of statistical methods the only viable approach. However, such methods require long simulations of PBNs, rendering the simulation speed a crucial efficiency factor. For large PBNs and high estimation precision requirements, a slow simulation speed becomes an obstacle. In this paper, we propose a structure-based method for fast simulation of PBNs. This method first performs a network reduction operation and then divides nodes into groups for parallel simulation. Experimental results show that our method can lead to an approximately 10 times speedup for computing steady-state probabilities of a real-life biological network., 15 pages, 3 figures, for CMSB 2016