Search results

Social media, such as Twitter, has become the first and the most frequent place to visit in order to gain information and establish situational awareness in emergencies and disasters. The purpose of this paper is to examine public opinion on Twitter in different disaster stages using the case of Hurricane Irma.

More than 3.5m tweets capturing the entire disaster lifecycle were collected and analyzed. Topic modeling was used to generate topics at each disaster stage based on Fink’s (1986) four-stage model of crisis and disaster: prodromal, acute, chronic and termination stages.

The results revealed that media reliance varied across different stages. All topics in the prodromal stage were associated with the early warning and real-time news. The topic of lessons learned from Hurricane Harvey was the most popular at this stage. The acute stage recorded the highest number of daily tweets. The most popular topic was the safety of people and animals. In the chronic stage too, the safety of people and animals remained a major concern. Heroic and anti-social behaviors also received substantial attention. In the termination stage, climate change was the most frequently discussed topic. Politics-related discussions were heated.

The results extended and enhanced the four-stage model of crisis and disaster. These findings can help government agencies and crisis managers address audience needs effectively at various crisis stages in a timely manner.

Natural disasters are increasingly more frequent and intense, which makes it critical for emergency managers to engage social media users during crises. This study examined emergency official accounts' social media engagement at each disaster stage based on Fink's four-stage model of crisis and disaster: prodromal, acute, chronic and termination stages and linked topics and sentiments to engagement.

Using text mining and sentiment analysis, 1,226 original tweets posted by 66 major emergency official Twitter accounts and more than 15,000 retweets elicited across the life cycle of Hurricane Irma were analyzed.

Results identified the most engaging official accounts and tweets. Most tweets and the most engaging tweets were posted in the prodromal stage. Tweets related to certain topics were significantly more engaging than others. The most frequently tweeted topics by official accounts were less engaging than some seldom tweeted topics. Negative sentiment words increased the engagingness of the tweet. Sadness was the strongest predictor of tweet engagement. Tweets that contained fewer sadness words were more engaging. Fear was stronger in positively predicting tweet engagement than anger. Results also demonstrated that words for fear and anger were critical in engaging social media discussions in the prodromal stage. Words for sadness made the tweets less engaging in the chronic stage.

This study provided detailed instructions on how to increase the engagingness of emergency management official accounts during disasters using computational methods. Findings have practical implications for both emergency managers and crisis researchers.

This paper aims to examine gender differences in credit access and credit default.

Using panel data drawn from 917 valid credit borrowers covering the period 2012 to 2015 drawn from among 6,849 study subjects and a national household financial survey (n = 29,500) conducted in China, this study focuses on gender differences in small and micro entrepreneurs’ financial behavior, specifically with respect to credit access and credit default.

The study revealed the following: Women expressed having more barriers to obtaining a business loan than men; gender had a significant effect on women’ credit default; and women were less likely to default a loan than male loan borrowers did. An exploration of the reasons for credit access and default found that female loan applicants were more likely to display a lack of knowledge and confidence in loan application.

The study contributes to literature by using the Marxian concept of reification in explaining women and their financial behaviors in China.

This paper aims to deal with the problem of multi‐plant purchase coordination in an assemble‐to‐order (ATO) environment, when volume discount schedules are provided by each of the suppliers.

This paper uses linear programming and a multi‐agent system to coordinate multi‐plant purchasing activities in order to minimize the total purchasing cost.

An integrated linear programming model and multi‐agent approach is perfectly suited to the purchase coordination in multi‐plant organizations in order to achieve the global profit.

The proposed model provides an effective and efficient coordination mechanism that helps multi‐plant organization and suppliers to maintain the availability of materials in the right quantity, with the right quality and at minimum possible cost.

The 2030 United Nations Agenda has framed Sustainable Development Goal 9 around eight targets outlined in Resolution A/RES/71/313 (U.N. General Assembly, 2017). The purpose of this chapter is that the lectors, without much previous knowledge on SDG9, understand the fundamental concepts involved in each of the eight targets. Multiple discussion points emerge when reflecting on the nature of these concepts and others emerge when reflecting on them in the industry settings. The first section of this chapter covers issues concerning resilient infrastructure. Resilient infrastructure is related to targets 9.1, 9.4, and 9.a. This concept needs to cope with extreme natural events potentially associated with global warming and climate change. The second section focusses on the importance of technological innovation in the context of targets 9.5 and 9.b. In a business domain, innovation allows to strengthen industrial competitiveness and increases corporate sustainability. The third concept covered in this chapter is the Information and Communication Technology that is a key to understand target 9.c. Last but not the least, two essential ideas are discussed: Inclusive and sustainable industrialisation and financial services, which are fundamental elements in target 9.2 and target 9.3. In a certain way, it is possible to conclude that both concepts integrate all previous conceptions.

Climate change will have a significant impact on China’s potential agricultural production and change the distribution of the population in various regions of China, thus producing population migration. This paper aims to analyze China’s population migration in response to climate change and its socio-economic impact.

In this paper, the Potential Agriculture Production Index is introduced as an analytical tool with which to estimate the scale of the population migration induced by climate change. Also, this paper constructs a multi-regional computable general equilibrium (CGE) model and analyzes the effect of change in the population distribution pattern on regional economies, regional disparity and resident welfare.

The key finding of this paper is that, as a result of changes in potential agricultural production induced by climate change, the Circum-Bohai-Sea region, the industrialized region and the industrializing region, which are the main destination regions of the migrating population, will face a severe labor shortage. In response to population migration, the economic growth rate of the immigrating population regions has accelerated. Correspondingly, the economic growth rate of the emigrating population regions has decreased. In addition, the larger the scale of population migration is, the larger the economic impact is. Migration increases inner-regional disparity and decreases inter-regional disparity. However, overall regional disparity is only somewhat decreased.

This paper introduces a Potential Agriculture Production Index to estimate the scale of the population migration and introduce a multi-regional CGE model to analyze the correlated social-economic impacts.

Density-based spatial clustering of applications with noise (DBSCAN) is the most commonly used density-based clustering algorithm, while it cannot be directly applied to the railway investment risk assessment. To overcome the shortcomings of calculation method and parameter limits of DBSCAN, this paper proposes a new algorithm called Improved Multiple Density-based Spatial clustering of Applications with Noise (IM-DBSCAN) based on the DBSCAN and rough set theory.

First, the authors develop an improved affinity propagation (AP) algorithm, which is then combined with the DBSCAN (hereinafter referred to as AP-DBSCAN for short) to improve the parameter setting and efficiency of the DBSCAN. Second, the IM-DBSCAN algorithm, which consists of the AP-DBSCAN and a modified rough set, is designed to investigate the railway investment risk. Finally, the IM-DBSCAN algorithm is tested on the China–Laos railway's investment risk assessment, and its performance is compared with other related algorithms.

The IM-DBSCAN algorithm is implemented on China–Laos railway's investment risk assessment and compares with other related algorithms. The clustering results validate that the AP-DBSCAN algorithm is feasible and efficient in terms of clustering accuracy and operating time. In addition, the experimental results also indicate that the IM-DBSCAN algorithm can be used as an effective method for the prospective risk assessment in railway investment.

This study proposes IM-DBSCAN algorithm that consists of the AP-DBSCAN and a modified rough set to study the railway investment risk. Different from the existing clustering algorithms, AP-DBSCAN put forward the density calculation method to simplify the process of optimizing DBSCAN parameters. Instead of using Euclidean distance approach, the cutoff distance method is introduced to improve the similarity measure for optimizing the parameters. The developed AP-DBSCAN is used to classify the China–Laos railway's investment risk indicators more accurately. Combined with a modified rough set, the IM-DBSCAN algorithm is proposed to analyze the railway investment risk assessment. The contributions of this study can be summarized as follows: (1) Based on AP, DBSCAN, an integrated methodology AP-DBSCAN, which considers improving the parameter setting and efficiency, is proposed to classify railway risk indicators. (2) As AP-DBSCAN is a risk classification model rather than a risk calculation model, an IM-DBSCAN algorithm that consists of the AP-DBSCAN and a modified rough set is proposed to assess the railway investment risk. (3) Taking the China–Laos railway as a real-life case study, the effectiveness and superiority of the proposed IM-DBSCAN algorithm are verified through a set of experiments compared with other state-of-the-art algorithms.

Climate change is a massive multidimensional shift. Temperature shifts, in particular, have important implications for urbanization, agriculture, health, productivity, and poverty, among other things. While much research has documented rising mean temperature levels, the authors also examine range-based measures of daily temperature volatility. Specifically, using data for select US cities over the past half-century, the authors compare the evolving time series dynamics of the average daily temperature (AVG) and the diurnal temperature range (DTR; the difference between the daily maximum and minimum temperatures). The authors characterize trend and seasonality in these two series using linear models with time-varying coefficients. These straightforward yet flexible approximations provide evidence of evolving DTR seasonality and stable AVG seasonality.

The aim of this paper was to attempt to investigate the transformation of traditional Chinese medicine (TCM) industry in Guangdong Province of China by applying a perspective of sectoral system of innovation (SSI). TCM industry in China has experienced an evolution path from low-tech to modern industry.

An analytical framework of sectoral system innovation for explaining the change in TCM industry in Guangdong Province has been conducted.

It shows that during the successful transformation of the TCM industry in Guangdong from low-tech to modern sector, national and provincial institution are acting as main drivers. Knowledge integration is the decision factor of modernization and innovation strategy as an actor that makes the transformation adjust and operate efficiently. Other actors, such as demand and external networks interplay together and led to a gradual organizational, structural and institutional change and modernization of TCM industry.

SSI analyses of TCM in China have never been conducted before, this paper also contributes to enrich the experience of low-tech industry transformation and provide references to other low-tech industries around the world.

This paper aims to present a direct analysis to demonstrate why markedly different tensile and compressive behaviors of concretes could not be simulated with the Drucker–Prager yield criterion.

This study proposed an extended form of the latter for establishing a new elastoplasticity model with evolving yield strengths.

Explicit closed-form solutions to non-symmetric tensile and compressive responses of uniaxial specimens at finite strain are for the first time obtained from hardening to softening.

With such exact solutions, the yield strengths in tension and compression can be explicitly prescribed by uniaxial tensile and compressive stress-strain functions. Then, the latter two are further provided in explicit forms toward accurately simulating tensile and compressive behaviors. Numerical examples are supplied for meso-scale heterogeneous concrete (MSHC) and high-performance concrete (HPC), etc. Model predictions are in good agreement with test data.