Science and Technology Governance and Ethics - A Global Perspective from Europe, India and China

This book analyzes the possibilities for effective global governance of science in Europe, India and China. Authors from the three regions join forces to explore how ethical concerns over new technologies can be incorporated into global science and technology policies. The first chapter introduces the topic, offering a global perspective on embedding ethics in science and technology policy. Chapter Two compares the institutionalization of ethical debates in science, technology and innovation policy in three important regions: Europe, India and China. The third chapter explores public perceptions of science and technology in these same three regions. Chapter Four discusses public engagement in the governance of science and technology, and Chapter Five reviews science and technology governance and European values. The sixth chapter describes and analyzes values demonstrated in the constitution of the People’s Republic of China. Chapter Seven describes emerging evidence from India on the uses of science and technology for socio-economic development, and the quest for inclusive growth. In Chapter Eight, the authors propose a comparative framework for studying global ethics in science and technology. The following three chapters offer case studies and analysis of three emerging industries in India, China and Europe: new food technologies, nanotechnology and synthetic biology. Chapter 12 gathers all these threads for a comprehensive discussion on incorporating ethics into science and technology policy. The analysis is undertaken against the backdrop of different value systems and varying levels of public perception of risks and benefits. The book introduces a common analytical framework for the comparative discussion of ethics at the international level. The authors offer policy recommendations for effective collaboration among the three regions, to promote responsible governance in science and technology and a common analytical perspective in ethics

Probing the Higgs boson-gluon coupling via the jet energy profile at e+e−e^+e^- colliders

The effective coupling of the Higgs boson to a gluon pair is one of the most important parameters to test the Standard Model and search for the new physics beyond. In this paper, we propose several new observables based on the jet energy profile to extract the effective coupling. The statistical uncertainties of the effective coupling extracted by using new observables are derived and estimated based on the simulation at the future $e^+e^-$ collider for $250$ GeV center-of-mass energy and 5 ab$^{-1}$ integrated luminosity. We found that the statistical uncertainties of effective coupling via the optimized observable can reach about $1.6\%$ in the channels of a $Z$ boson decaying to lepton pairs and is reduced by $52\%$ compared to the relevant uncertainties in the conventional approach. These new observables potentially can be helpful for the measurement of effective coupling at future $e^+e^-$ colliders.Comment: 8 pages, 9 figure

Numerical Simulation of Cracked Reinforced Concrete Slabs Subjected to Blast Loading

Crack is one of the most common defects observed in reinforced concrete (RC) structures. An initial crack will lead to severe changes in the stress state when the structure subjected to blast loadings. Target on acquiring the dynamic data, a finite element method is applied to simulate the response of cracked RC slab subjected to blast loading. The theoretical results of damage distribution and mid-span deflection of normal specimens are first compared with experimental test, which indicates that the dynamic behaviour of RC slab under blast loading can be well predicted by the finite element model. Then blast responses of cracked RC slabs with varied crack parameters (e.g. orientation, width and depth) are systematically studied. Results show that damage of the cracked slab initiates from the initial crack tip of the bottom surface, and then it propagates quickly with cracks found in the support areas on the top surface. In addition, the existence of initial cracks in the RC slab make it subject to more serious damages than the normal RC slab under the same explosive loads, as well as a short reacted failure time. Moreover, variations of crack parameters have slight influences on the distributions of cracked RC slab

Low-Tubal-Rank Tensor Recovery via Factorized Gradient Descent

This paper considers the problem of recovering a tensor with an underlying low-tubal-rank structure from a small number of corrupted linear measurements. Traditional approaches tackling such a problem require the computation of tensor Singular Value Decomposition (t-SVD), that is a computationally intensive process, rendering them impractical for dealing with large-scale tensors. Aim to address this challenge, we propose an efficient and effective low-tubal-rank tensor recovery method based on a factorization procedure akin to the Burer-Monteiro (BM) method. Precisely, our fundamental approach involves decomposing a large tensor into two smaller factor tensors, followed by solving the problem through factorized gradient descent (FGD). This strategy eliminates the need for t-SVD computation, thereby reducing computational costs and storage requirements. We provide rigorous theoretical analysis to ensure the convergence of FGD under both noise-free and noisy situations. Additionally, it is worth noting that our method does not require the precise estimation of the tensor tubal-rank. Even in cases where the tubal-rank is slightly overestimated, our approach continues to demonstrate robust performance. A series of experiments have been carried out to demonstrate that, as compared to other popular ones, our approach exhibits superior performance in multiple scenarios, in terms of the faster computational speed and the smaller convergence error.Comment: 13 pages, 4 figure

DDCO model based false news detection research

With the rapid development of the information age, while the popularity of social media brings great convenience, it also brings some negative effects, such as the spread of false news. At present, the identification of fake news is still based on the personal screening ability, therefore, the intelligent and information-based automatic detection algorithm has become one of the hot issues of current research. Based on the characteristics of DCAN and DEFEND models, this paper proposes an novel model DDCO, which uses multi-layer collaborative attention mechanism to extract the most relevant information from the three dimensions of sentence level, word level and sentence-comment level respectively. Finally, the model designed in this paper is tested on Weibo and Twitter data sets, and the results show that the DDCO has a higher accuracy than the existing models, which provides an important reference for false news detection