Assessing progress in freedom of expression in a Chinese newspaper: a comparison of Guang Ming Daily coverage of the Tang Shan and Si Chuan earthquakes

This study examines ways in which press coverage of earthquakes has changed from 1976 (Tang Shan earthquake) to 2008 (Si Chuan earthquake) in a major Party newspaper, Guan Ming Daily. The expectation was that over that period the press would become freer, with less story emphasis on government and ideology, and more emphasis on common individuals and information. A total of 118 articles were examined for each earthquake. Results show that coverage has changed in important ways. Ideological statements such as Long live Chairman Mao, which were a staple of coverage in 1976, had disappeared by 2008. Stories in 2008 were shorter (but much more numerous), and focused more on common individuals and less on government. Although the tone of most stories continued to be positive, the 2008 stories also contained some negative material describing suffering and damage. However, no stories in either time period ever criticized government or government officials. The international media ratings service Freedomhouse considers China\u27s media as being not free. In the sense that they do not serve as a place for public debate or criticism, this is true. However, this fact obscures the real and important changes that have occurred. In 2008, the public learned a great deal about the Si Chuan earthquake in terms of numbers killed and injured, damage, and efforts of assistance that included other countries. Much of this type of information was never printed in the newspaper in the 40 days following the Tang Shan 1976 quake

Identifying Functional Imaging Markers in Psychosis Using fMRI

Major types of psychotic disorders include schizophrenia (SCZ), bipolar disorder (BP) and schizoaffective disorder (SZA). These disorders have profound and overlapping symptoms with marked cognitive deficits, and their diagnosis relies on symptom clusters. The treatments for psychosis are usually focused on positive symptoms such as delusions and hallucinations. Although cognitive impairments underlie both positive and negative symptoms, functional brain imaging biomarkers that can reliably predict a patient\u27s cognitive deficits are still lacking. Therefore, this project used functional MRI to explore the feasibility of using functional connectivity (FC) to predict cognitive performance. A total of 207 subjects (BP: 79, SZ/SZA: 48, and HC: 80) with high functional MRI image (fMRI) quality (SNR\u3e 100, motion \u3c 0.3) were selected from the McLean MATRICS dataset. Subjects were divided into a discovery cohort (n=104) and an age, gender, and head motion matched validation cohort (n=103). The hypothesis was that FC could predict cognitive performance in the discovery cohort and that the prediction models could be generalized to the validation cohort. The connectomes for each subject were obtained by calculating the whole- brain connectivity using networks from the individualized functional parcellation as region of interests (ROIs). Models were trained to predict the 8 cognitive scores in the discovery cohort, respectively. The generalizability of these models was tested by applying these models to the validation cohort. The trained models were able to predict 6 out of 8 cognitive scores using a LOOCV procedure. Models for working memory, composite score and attention score could be generalized to the validation cohort. A total of 35 FC features were identified as important for predicting performance in these cognitive domains. Significant differences between patients and controls were found for 13 of these features when considered individually. In summary, this project has established a framework for biomarker discovery that may have clinical relevance for the diagnosis of psychosis early in the disease process by providing possible FC features that can be detected using fMRI and may help guide therapeutic interventions. The identified biomarkers also provide convergent evidence for network dysfunction in psychosis and suggest personalized treatment targets

Energy saving technologies and optimisation of energy use for decarbonised iron and steel industry

The iron and steel industry relies significantly on fossil energy use and is one of the largest energy consumers and carbon emitters in the manufacturing sector. Simultaneously, a huge amount of waste heat is directly discharged into the environment during steel production processes. Conservation of energy and energy-efficient improvement should be a holistic target for iron and steel industry. There is a need to investigate and analyse potential effects of application i.e., a number of primary and secondary energy saving and decarbonisation technologies to the basic energy performance and CO2 emissions profile of iron and steel industry. A 4.7Mt annual steel capacity iron and steel plant in the UK is selected as a case study. By carrying out a comprehensive literature review of current primary and secondary energy saving and decarbonisation technologies, suitable technologies are categorised based on their purpose of utilisation and installation positions. It is found that fuel substitution technologies and waste heat recovery technologies have wide application prospects in iron and steel industry. To further investigate effects of these technologies on the UK integrated steelwork, a comprehensive model of iron and steel production processes is built by using the software Aspen Plus. The model is fully validated and is used to examine the specific energy consumption and direct CO2 emissions. Energy consumption and CO2 emissions of whole production chain to produce a ton of crude steel are 17.5 GJ and 1.06 t. Waste heat from hot coke and gas cooling could cover 40% of electricity consumed in the plant if coking process has the maximum coke capacity. To implement primary energy saving and decarbonisation technologies, the performance of blast furnace is optimised first by substituting coke with bio-reducers based on the proposed model. Three biomass substitutions are considered to reduce coke rate and CO2 emissions of ironmaking process. Results show that coke demand of per ton of hot metal and CO2 emissions of the ironmaking process are improved by replacing partial coke with biomass. An optimal coke replacement is operated with 200 kg bio-oil and 222 kg coke when producing one ton of product. The reaction involving bio-syngas has the most potential to reduce CO2 emissions. To find a sustainable way to capture CO2 and recover waste heat onsite, a model of adopting organic Rankine cycle with amine-based CO2 capture in ironmaking process is introduced. In comparison with different reducing agents injected into BF, bio-oil has the most advantage to improve energy consumption of CO2 capture system. CO2 emissions from total sites can be maximumly reduced by 69% through the method of CO2 capture with waste heat recovery technologies. The combination of various decarbonised technologies creates great opportunity to reduce CO2 emissions. A mass-thermal network of iron and steel industry is finally built up, where primary and secondary energy saving technologies are implemented to optimise energy use and reduce CO2 emissions. The general guideline i.e., 5-step method is summarised to optimise the mass-thermal network. Exergy analysis is used to evaluate overall network after applications of energy saving and decarbonisation technologies. Injection of biomass-based syngas can maximumly increase the exergy efficiency of ironmaking process. Sinter and BOF steelmaking processes are related with mass ratio of hot metal. Optimisation insights of energy use and decarbonisation for steelwork are revealed based on exergy efficiency and destruction results

The Impact of Corporate ESG Performance on Environmental Investment

As a standard for measuring corporate sustainability and long-term value growth investment, corporate ESG performance is of great significance for evaluating corporate sustainability, guiding investment decisions and promoting corporate improvement. Based on the sample of A-share listed companies in Shanghai and Shenzhen that have obtained SynTao Green Finance’s ESG rating from 2011 to 2021, this paper empirically examines the role of corporate ESG performance on green investment based on the theory of “stakeholders” and the theory of “sustainable development”. The results show that: (1) good ESG performance can significantly improve the level of environmental investment, and this conclusion is still valid under a series of robustness tests; (2) The mechanism test shows that good ESG performance can expand the scale of enterprises and indirectly promote corporate green investment; (3) The intermediary mechanism test shows that there is a partial mediating effect of managerial risk appetite (Mrip) on ESG performance in environmental investment. In the new stage of accelerating the green transformation of China’s industry, Chinese enterprises urgently need to improve the green financial system, accelerate the process of green transformation, and help achieve the goals of “carbon peak” and “carbon neutrality”

Feedback-efficient Active Preference Learning for Socially Aware Robot Navigation

Socially aware robot navigation, where a robot is required to optimize its trajectory to maintain comfortable and compliant spatial interactions with humans in addition to reaching its goal without collisions, is a fundamental yet challenging task in the context of human-robot interaction. While existing learning-based methods have achieved better performance than the preceding model-based ones, they still have drawbacks: reinforcement learning depends on the handcrafted reward that is unlikely to effectively quantify broad social compliance, and can lead to reward exploitation problems; meanwhile, inverse reinforcement learning suffers from the need for expensive human demonstrations. In this paper, we propose a feedback-efficient active preference learning approach, FAPL, that distills human comfort and expectation into a reward model to guide the robot agent to explore latent aspects of social compliance. We further introduce hybrid experience learning to improve the efficiency of human feedback and samples, and evaluate benefits of robot behaviors learned from FAPL through extensive simulation experiments and a user study (N=10) employing a physical robot to navigate with human subjects in real-world scenarios. Source code and experiment videos for this work are available at:https://sites.google.com/view/san-fapl.Comment: To appear in IROS 202

Simple spatial scaling rules behind complex cities

Although most of wealth and innovation have been the result of human interaction and cooperation, we are not yet able to quantitatively predict the spatial distributions of three main elements of cities: population, roads, and socioeconomic interactions. By a simple model mainly based on spatial attraction and matching growth mechanisms, we reveal that the spatial scaling rules of these three elements are in a consistent framework, which allows us to use any single observation to infer the others. All numerical and theoretical results are consistent with empirical data from ten representative cities. In addition, our model can also provide a general explanation of the origins of the universal super- and sub-linear aggregate scaling laws and accurately predict kilometre-level socioeconomic activity. Our work opens a new avenue for uncovering the evolution of cities in terms of the interplay among urban elements, and it has a broad range of applications.This work is supported by the National Natural Science Foundation of China under Grant Nos. 61673070, 61773069, 71731002 and the Fundamental Research Funds for the Central Universities with the Grant No. 2015KJJCB13, and also partially supported by NSF Grants PHY-1505000, CMMI-1125290, CHE-1213217, DTRA Grant HDTRA1-14-1-0017, DOE Grant DE-AC07-05Id14517. J.Z. acknowledges discussions with Prof. Bettencourt of the Santa Fe Institute, Dr. Lingfei Wu of Arizona State University, and Profs. Yougui Wang and Qinghua Chen of Beijing Normal University. R.L. acknowledges helpful discussions with and comments from Dr. Remi Louf in CASA, University College London, Dr. Longfeng Zhao from Huazhong (Central China) Normal University, and selfless help from Prof. Yougui Wang. R.L. is also supported by the Chinese Scholarship Council. (61673070 - National Natural Science Foundation of China; 61773069 - National Natural Science Foundation of China; 71731002 - National Natural Science Foundation of China; 2015KJJCB13 - Fundamental Research Funds for the Central Universities; PHY-1505000 - NSF; CMMI-1125290 - NSF; CHE-1213217 - NSF; HDTRA1-14-1-0017 - DTRA Grant; DE-AC07-05Id14517 - DOE; Chinese Scholarship Council)Published versio