Building a "double first-class university" on China's Qing-Zang Plateau: opportunities, strategies and challenges

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Building a "double first-class university" on China's Qing-Zang Plateau: opportunities, strategies and challenges

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The role of vaccines in COVID-19 control strategies in Singapore and China

Objectives: In this article, we critically review the development and implementation of COVID-19 vaccination in Singapore and China during the pandemic. Methods: We collect and analyze data from a range of sources, including scholarly articles, statistics and documents from national governments in the two countries, and reports from international organizations. Results: There are important differences in the two countries’ approaches to the evolving pandemic, and thus the roles that COVID-19 vaccination plays in the overall response strategies in these two countries. Conclusions: Whereas Singapore adopted a “living with the virus” strategy, China continued to pursue a COVID-zero strategy. The overall COVID-19 response strategy of Singapore was largely shared by many countries in the world, while that of China was more unique and hardly imitated elsewhere. Nevertheless, vaccination played a significant role in both countries’ responses to the pandemic. A comparison and contrast between the vaccination processes in these two countries thus shed important light on the drivers and outcomes of COVID-19 vaccination in different settings

Quantum: May be a new-found messenger in biological systems

Current studies on biological communications mainly focus on chemical signals. Since organisms are extremely complex, different kinds of signals may exist in the process of cell communication. The most probable candidate for alternative forms of organism communications is electromagnetic radiation, as many experiments have confirmed that electromagnetic radiation widely exists in cells, tissues, organisms and even between organisms and their surroundings. The well-known connection between electromagnetic radiation and quantization of the energy transfer makes us to suggest a bold, but fresh view that quantum can serve as a biological messenger. This view also coincides with the medium of Qi in the human body according to traditional Chinese medicine (TCM). Relating Qi with quantum may further explain a number of phenomena that cannot be explained solely by conventional chemical signaling systems

China's pivot from zero-COVID strategy and the role of vaccines

Objective: This paper aims to examine China's vaccine policy within the context of broader policy interventions and evaluate their impact on both health and non-health outcomes. Method: We first utilize the categorizing Policy & Technology Interventions (CPTI) framework to assess the intensities of different policy responses during various stages of the COVID-19 pandemic. We adopt a process inspired by the Delphi method to evaluate the timelines and intensities of the policy measures comprehensively. Subsequently, we probe the results generated from this process to identify distinctive patterns in China's pandemic policy changes, particularly in relation to the country's reopening process. To explain this distinctive pattern, we employ the governmentality perspective, drawing on Foucault's theories, which focus on the power dynamics between techniques and governance. Results: The policy interventions in China during the COVID-19 pandemic significantly differ from those in the other countries in the four policy areas. Despite the massive vaccination efforts, vaccines did not play a decisive role in China's reopening in late 2022. Our analysis reveals that the vaccines are only used in China as part of a broader social governing system in conjunction with zero-COVID policy, such as lockdowns, travel restrictions, and mass tracking. Conclusions: China's approach to COVID vaccines and the policies governing their use are distinctive, shaped by a governmentality perspective that prioritizes the strengthening of governance

Reconceptualizing vaccine nationalism: A multi-perspective analysis on security, technology, and global competition

In this perspective paper, we argue that the broader economic, political, and geo-strategic considerations leading to a nationalist approach in the development, deployment, and use of COVID-19 vaccines remain largely unexplored in the existing literature. We propose to expand and reframe the current discourse on vaccine nationalism (VN). This involves examining nationalist practices and policies beyond merely securing vaccine access during the global COVID-19 vaccine shortage. We seek to identify the core characteristics of this nationalist approach to COVID-19 vaccines by drawing on existing nationalism literature. We then examine the root causes of vaccine nationalism from three distinctive yet interrelated perspectives, each aimed at uncovering its root causes: national security, technological catch-up, and rising geo-strategic competition in technology and ideology. Notably, our analysis of VN draws extensively on the vaccine-related policies and practices observed in China. By considering these perspectives and their interplay, we contend that a more holistic and nuanced understanding of vaccine nationalism can be achieved.</p

Listen to the Companies: Exploring BIM Job Competency Requirements by Text Mining of Recruitment Information in China

Building information modeling (BIM) is a pivotal technology to realizing the digital transformation of the construction industry. Lack of BIM professionals, however, is one of the reasons the application of BIM technology in the construction industry is hindered. Identifying BIM competency requirements is critical for BIM professionals' training. This paper uses the structural topic model (STM) to mine the topics of BIM recruitment information to deeply understand the BIM competency requirements from a 360° view of the construction industry. The company size, salary level, year of experience, and education in BIM recruitment information are taken as covariates to examine their impact on BIM recruitment topic prevalence. And the changing trend of the topic prevalence and topic correlations are observed through visual analysis. The results reveal that the current BIM competency requirements in the construction industry contain three aspects: management competencies, professional and technical competencies, and personal characteristics. In particular, the requirements for BIM application, construction drawing design, and information technology (IT) skills are relatively strong, and personnel professionalism is also a concern of BIM job recruitment. Companies of different sizes have evident preferences for competencies. Salary levels and years of experience requirements also affect the intensity of corporate demand for BIM competencies. However, education is not the main factor affecting the recruitment of BIM positions. The results can provide a reliable theoretical basis for educational institutions to build a proper BIM professional course system, for companies to develop BIM job recruitment plans, and for individuals to choose their employment goals

Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection

Abstract: (Aim) Classification of brain images as pathological or healthy case is a key pre-clinical step for potential patients. Manual classification is irreproducible and unreliable. In this study, we aim to develop an automatic classification system of brain images in magnetic resonance imaging (MRI). (Method) Three datasets were downloaded from the Internet. Those images are of T2-weighted along axial plane with size of 256 ˆ 256. We utilized an s-level decomposition on the basis of dual-tree complex wavelet transform (DTCWT), in order to obtain 12s “variance and entropy (VE)” features from each subband. Afterwards, we used support vector machine (SVM) and its two variants: the generalized eigenvalue proximal SVM (GEPSVM) and the twin SVM (TSVM), as the classifiers. In all, we proposed three novel approaches: DTCWT + VE + SVM, DTCWT + VE + GEPSVM, and DTCWT + VE + TSVM. (Results) The results showed that our “DTCWT + VE + TSVM” obtained an average accuracy of 99.57%, which was not only better than the two other proposed methods, but also superior to 12 state-of-the-art approaches. In addition, parameter estimation showed the classification accuracy achieved the largest when the decomposition level s was assigned with a value of 1. Further, we used 100 slices from real subjects, and we found our proposed method was superior to human reports from neuroradiologists. (Conclusions) This proposed system is effective and feasible

Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection

Abstract: (Aim) Classification of brain images as pathological or healthy case is a key pre-clinical step for potential patients. Manual classification is irreproducible and unreliable. In this study, we aim to develop an automatic classification system of brain images in magnetic resonance imaging (MRI). (Method) Three datasets were downloaded from the Internet. Those images are of T2-weighted along axial plane with size of 256 ˆ 256. We utilized an s-level decomposition on the basis of dual-tree complex wavelet transform (DTCWT), in order to obtain 12s “variance and entropy (VE)” features from each subband. Afterwards, we used support vector machine (SVM) and its two variants: the generalized eigenvalue proximal SVM (GEPSVM) and the twin SVM (TSVM), as the classifiers. In all, we proposed three novel approaches: DTCWT + VE + SVM, DTCWT + VE + GEPSVM, and DTCWT + VE + TSVM. (Results) The results showed that our “DTCWT + VE + TSVM” obtained an average accuracy of 99.57%, which was not only better than the two other proposed methods, but also superior to 12 state-of-the-art approaches. In addition, parameter estimation showed the classification accuracy achieved the largest when the decomposition level s was assigned with a value of 1. Further, we used 100 slices from real subjects, and we found our proposed method was superior to human reports from neuroradiologists. (Conclusions) This proposed system is effective and feasible

Evolving graph-based video crowd anomaly detection

Detecting anomalous crowd behavioral patterns from videos is an important task in video surveillance and maintaining public safety. In this work, we propose a novel architecture to detect anomalous patterns of crowd movements via graph networks. We represent individuals as nodes and individual movements with respect to other people as the node-edge relationship of an evolving graph network. We then extract the motion information of individuals using optical flow between video frames and represent their motion patterns using graph edge weights. In particular, we detect the anomalies in crowded videos by modeling pedestrian movements as graphs and then by identifying the network bottlenecks through a max-flow/min-cut pedestrian flow optimization scheme (MFMCPOS). The experiment demonstrates that the proposed framework achieves superior detection performance compared to other recently published state-of-the-art methods. Considering that our proposed approach has relatively low computational complexity and can be used in real-time environments, which is crucial for present day video analytics for automated surveillance