Sentiment, richness, authority, and relevance model of information sharing during social Crises—the case of #MH370 tweets

The study introduces a model of crisis information sharing based on Twitter discussions of the missing Malaysian Airlines Flight 370. Grounded in the Elaboration Likelihood Model, the study tests four salient factors: Sentiment, Richness, Authority, and Relevance, which can be measured by peripheral cues in tweets and in user profiles. Findings suggest that information sharing is positively associated with the presence of peripheral cues indicative of a confident, self-revealing and positive emotional language style, and is negatively related to an angry and informal style. Additionally, information sharing is related to the presence of multimedia cues and cues indicating source popularity

Variational principle for non-additive neutralized Bowen topological pressure

Ovadia and Rodriguez-Hertz \cite{OH} defined the neutralized Bowen open ball as $$B_n(x,e^{-n\varepsilon})=\{y\in X:d(T^j(x),T^j(y))<e^{-n\varepsilon},\forall 0\leq j\leq n-1\}.$$ Yang, Chen and Zhou \cite{YCZ} introduced the notion of neutralized Bowen topological entropy of subsets by replacing the usual Bowen ball by neutralized Bowen open ball. And they established variational principles for this notion. In this note, we extend this result to the non-additive neutralized Bowen topological pressure and established the variational principle for non-additive potentials with tempered distortion

NGO participation in geoengineering in the UK and China: a causal study

This thesis addresses the issue of why NGOs do or do not become involved in geoengineering (GE) as a policy area in the UK and China. GE refers to the employment of large-scale manipulation to exert an influence on the global environment in order to deal with climate change. Governance of GE is a key issue in the academic literature and public policy. In terms of governance related issues, public participation has been frequently discussed in the existing literature within environmental law and related fields. Among all the stakeholders involved in GE, the issue of NGOs’ participation stands out due to their limited involvement. The thesis is generally based on the literature on public participation in environmental law. Although public participation has long been an interest of study, the literature has paid little attention to the causes of participation. The thesis contributes to the existing literature by way of adding a consideration of why certain groups participate or not in particular areas of environmental law and policy. The main research question of the thesis is ‘why do NGOs participate in GE or not in the UK and China?’ In order to explore the causes of participation, qualitative interviews were employed: notably in-depth interviews were conducted among environmental NGOs in the UK and China. The thesis then employs the literature on social movements and public policy to generate variables for analysing the relevant data. Through analysing the data with variables generated from these literatures, two basic findings were identified: involvement and non-involvement of NGOs in the UK can be considered as intentional and the deliberate outcome of strategic choices; however in v China, only international NGOs make strategic decisions on non-involvement, while domestic Chinese NGOs were unintentionally not involved with GE. In conclusion, the contribution of the thesis is three-fold. It adds to the literature on social movements and public policy by concentrating on whether NGOs make strategic choices on becoming involved in GE or not and why. It also contributes to the future governance framework of GE by understanding what may lead NGOs, as a potentially critical part of this framework, to become involved. Aside from this, the thesis makes a contribution in an empirical way by mapping the picture of NGO involvement with GE in the UK and China

Genetic Evolution and Molecular Selection of the HE Gene of Influenza C Virus

Influenza C virus (ICV) was first identified in humans and swine, but recently also in cattle, indicating a wider host range and potential threat to both the livestock industry and public health than was originally anticipated. The ICV hemagglutinin-esterase (HE) glycoprotein has multiple functions in the viral replication cycle and is the major determinant of antigenicity. Here, we developed a comparative approach integrating genetics, molecular selection analysis, and structural biology to identify the codon usage and adaptive evolution of ICV. We show that ICV can be classified into six lineages, consistent with previous studies. The HE gene has a low codon usage bias, which may facilitate ICV replication by reducing competition during evolution. Natural selection, dinucleotide composition, and mutation pressure shape the codon usage patterns of the ICV HE gene, with natural selection being the most important factor. Codon adaptation index (CAI) and relative codon deoptimization index (RCDI) analysis revealed that the greatest adaption of ICV was to humans, followed by cattle and swine. Additionally, similarity index (SiD) analysis revealed that swine exerted a stronger evolutionary pressure on ICV than humans, which is considered the primary reservoir. Furthermore, a similar tendency was also observed in the M gene. Of note, we found HE residues 176, 194, and 198 to be under positive selection, which may be the result of escape from antibody responses. Our study provides useful information on the genetic evolution of ICV from a new perspective that can help devise prevention and control strategies

Exponentially weighted particle filter for simultaneous localization and mapping based on magnetic field measurements

This paper presents a simultaneous localization and mapping (SLAM) method that utilizes the measurement of ambient magnetic fields present in all indoor environments. In this paper, an improved exponentially weighted particle filter was proposed to estimate the pose distribution of the object and a Kriging interpolation method was introduced to update the map of the magnetic fields. The performance and effectiveness of the proposed algorithms were evaluated by simulations on MATLAB based on a map with magnetic fields measured manually in an indoor environment and also by tests on the mobile devices in the same area. From the tests, two interesting phenomena have been discovered; one is the shift of location estimation after sharp turning and the other is the accumulated errors. While the latter has been confirmed and investigated by a few researchers, the reason for the first one still remains unknown. The tests also confirm that the interpolated map by using the proposed method improves the localization accuracy

Spatiotemporal evolution and drivers of carbon inequalities in urban agglomeration:An MLD-IDA inequality indicator decomposition

Increasing countries are articulating ambitious goals of carbon neutrality. However, large inequalities in regional emissions within a country may hinder progress toward a carbon–neutral future, as the unequal distribution of reduction responsibilities among regions could impair just transition and exacerbate uneven development, which necessitates an in-depth understanding of the mechanism of multi-scale carbon inequalities within country, region, and city. Yet, the evolution of carbon inequalities within urban agglomerations and the differences between adjacent or distant urban agglomerations have not been well understood, especially in countries undergoing rapid urbanization. Using the data of 89 cities in China’s Yangtze River Economic Belt (YREB) during 2006–2021, this paper quantifies carbon emissions inequality (CEI) at different scales in a systematic regional-urban agglomeration-city hierarchical structure. Then, under the integrated mean logarithmic deviation-logarithmic mean Divisia index (MLD-LMDI) decomposition framework, multi-scale CEIs are perfectly decomposed into six interrelated drivers, i.e., industrial emission structure, energy emission intensity, industrial energy mix, energy intensity, industrial structure, and economic development. The results show that economic development, energy intensity, and industrial energy mix disparities are the main determinants accounting for CEIs at different scales. The decreasing CEI in YREB is mainly due to the changes in industrial structure and economic development, while the energy intensity effect partially hinders the mitigation of CEI. In the upper reaches of the YREB, the energy intensity effect accounts for over 94% growth of CEI during 2006–2021, while the decline in CEIs in middle and lower reaches is primarily caused by the effects of industrial energy mix and industrial structure, respectively. Further spatial decomposition analysis reveals more refined city-level heterogeneous effects and emphasizes the prioritized emission reduction direction for each city. This paper offers implications for reducing carbon inequality and insights into coordinated carbon emissions mitigation at the regional level for a carbon–neutral future