Justifying a privacy guardian in discourse and behaviour : the People’s Republic of China’s strategic framing in data governance

The People’s Republic of China’s (PRC) approach to data governance, centred on data sovereignty, is much debated in academic literature. However, it remains unclear how the PRC’s different state actors justify this approach. Based on an analysis of the discourse and behaviour of the PRC’s state actors through strategic framing theory, their role as a privacy guardian can arguably be described as strategically constructed. The Chinese government and legislative bodies have tailored their communications to present themselves as champions of individual privacy, aiming to secure support for state policies. This strategic framing encompasses four mechanisms: the reframing of privacy threats through political narratives; legal ambiguities; selective framing; and the implementation of censorship to influence public discourse. An examination of how the Chinese government responded differently to data breaches in the cases of Didi and the Shanghai National Police Database leak highlights the Chinese government’s efforts in maintaining framing consistency to construct itself as a guardian, rather than a violator, of individual privacy.Peer reviewe

Justifying a privacy guardian in discourse and behaviour:the People’s Republic of China’s strategic framing in data governance

The People’s Republic of China’s (PRC) approach to data governance, centred on data sovereignty, is much debated in academic literature. However, it remains unclear how the PRC’s different state actors justify this approach. Based on an analysis of the discourse and behaviour of the PRC’s state actors through strategic framing theory, their role as a privacy guardian can arguably be described as strategically constructed. The Chinese government and legislative bodies have tailored their communications to present themselves as champions of individual privacy, aiming to secure support for state policies. This strategic framing encompasses four mechanisms: the reframing of privacy threats through political narratives; legal ambiguities; selective framing; and the implementation of censorship to influence public discourse. An examination of how the Chinese government responded differently to data breaches in the cases of Didi and the Shanghai National Police Database leak highlights the Chinese government’s efforts in maintaining framing consistency to construct itself as a guardian, rather than a violator, of individual privacy

Ensemble multi-objective evolutionary algorithm for gene regulatory network reconstruction based on fuzzy cognitive maps

Many methods aim to use data, especially data about gene expression based on high throughput genomic methods, to identify complicated regulatory relationships between genes. The authors employ a simple but powerful tool, called fuzzy cognitive maps (FCMs), to accurately reconstruct gene regulatory networks (GRNs). Many automated methods have been carried out for training FCMs from data. These methods focus on simulating the observed time sequence data, but neglect the optimisation of network structure. In fact, the FCM learning problem is multi-objective which contains network structure information, thus, the authors propose a new algorithm combining ensemble strategy and multi-objective evolutionary algorithm (MOEA), called EMOEA(FCM)-GRN, to reconstruct GRNs based on FCMs. In EMOEA(FCM)-GRN, the MOEA first learns a series of networks with different structures by analysing historical data simultaneously, which is helpful in finding the target network with distinct optimal local information. Then, the networks which receive small simulation error on the training set are selected from the Pareto front and an efficient ensemble strategy is provided to combine these selected networks to the final network. The experiments on the DREAM4 challenge and synthetic FCMs illustrate that EMOEA(FCM)-GRN is efficient and able to reconstruct GRNs accurately

A time series driven decomposed evolutionary optimization approach for reconstructing large-scale gene regulatory networks based on fuzzy cognitive maps

Abstract Background Reconstructing gene regulatory networks (GRNs) from expression data plays an important role in understanding the fundamental cellular processes and revealing the underlying relations among genes. Although many algorithms have been proposed to reconstruct GRNs, more rapid and efficient methods which can handle large-scale problems still need to be developed. The process of reconstructing GRNs can be formulated as an optimization problem, which is actually reconstructing GRNs from time series data, and the reconstructed GRNs have good ability to simulate the observed time series. This is a typical big optimization problem, since the number of variables needs to be optimized increases quadratically with the scale of GRNs, resulting an exponential increase in the number of candidate solutions. Thus, there is a legitimate need to devise methods capable of automatically reconstructing large-scale GRNs. Results In this paper, we use fuzzy cognitive maps (FCMs) to model GRNs, in which each node of FCMs represent a single gene. However, most of the current training algorithms for FCMs are only able to train FCMs with dozens of nodes. Here, a new evolutionary algorithm is proposed to train FCMs, which combines a dynamical multi-agent genetic algorithm (dMAGA) with the decomposition-based model, and termed as dMAGA-FCMD, which is able to deal with large-scale FCMs with up to 500 nodes. Both large-scale synthetic FCMs and the benchmark DREAM4 for reconstructing biological GRNs are used in the experiments to validate the performance of dMAGA-FCMD. Conclusions The dMAGA-FCMD is compared with the other four algorithms which are all state-of-the-art FCM training algorithms, and the results show that the dMAGA-FCMD performs the best. In addition, the experimental results on FCMs with 500 nodes and DREAM4 project demonstrate that dMAGA-FCMD is capable of effectively and computationally efficiently training large-scale FCMs and GRNs

Application of mercury intrusion method and digital image analysis in quantitative analysis of micro-scale pores in tight sandstone reservoirs: a case study of X block in Wuqi Oil Field, Ordos Basin

In order to investigate the pore structures of the tight sandstone reservoirs in the 4+5th and 6th members of the Triassic Yanchang Formation (Chang4+5 and Chang6, respectively), and the 9th and 10th members of the Jurassic Yan'an Formation (Yan9 and Yan10, respectively) in the X block of Wuqi Oil Field, Ordos Basin, 12 samples were collected to analyze reservoir properties with the approaches of scanning electron microscope observation, X-ray diffraction and high pressure mercury intrusion. We also quantitatively characterized the pore parameter and fractal dimension of the tight sandstones by the using of digital image analysis and fractal geometry. In addition, we discussed the relationship between fractal dimension and sample properties (porosity, permeability), pore structure parameter (average pore-throat radius, sorting coefficient), pore geometric parameters (dominant pore size, perimeter over area, and pore body-to-throat ratio). The influence of sedimentary facies and diagenetic environment on pore structures were also quantitatively analyzed. Results show that the pore structure fractal dimension ranges from 2.164 to 2.895, with an average value of 2.395. Fractal dimension is negatively correlated to permeability, porosity and average pore-throat radius, and positively related to sorting coefficient. Tight sandstones in the study area generally show properties of low dominant pore size, high perimeter over area, lower body-to-throat ratio, and high dimensions. The fractal dimension is positively related to body-to-throat and perimeter-to-area ratio, and negatively related to pore size. It is indicated that the pore structure of the samples is relatively complex and has strong heterogeneity. Depositional environment affects the compositional maturity and structural maturity of reservoir

Improving Knowledge about Children’s Environmental Health in Northwest China

The main purpose of this study was to identify policy maker opinions and attitudes towards children’s environmental health (CEH), potential barriers to child-specific protective legislation and implementation in northwest China, and evaluate knowledge and attitudes about CEH before and after an educational conference. We conducted seventy-two interviews with regional officials, researchers and non-governmental organization representatives from five provinces, and surveyed participants (forty-seven) before and after an educational conference in northwest China about CEH. Interviews identified general consensus among participants of the adverse effects of air pollution on children, yet few participants knew of policies to protect them. Barriers identified included limited funding and enforcement, weak regional governments and absence of child-specific policy-making. After the conference, substantially greater self-efficacy was identified for lead, mercury, air pollution and polychlorinated biphenyls (+0.57–0.72 on a 1–5 Likert scale, p = 0.002–0.013), and the scientific knowledge for the role of environment in children’s health (+0.58, p = 0.015), and health care provider control (+0.52, p = 0.025) were rated more strongly. We conclude that policy makers in Northwest China appreciate that children are uniquely vulnerable, though additional regulations are needed to account for that vulnerability. Further research should examine effectiveness of the intervention on a larger scale and scope, and evaluate the usefulness of such interventions in translating research into improved care/reduced exposure to environmental hazards