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Security breaches have been arising issues that cast a large amount of financial losses and social problems to society and people. Little is known about how social media could be used a surveillance tool to track messages related to security breaches. This paper aims to fill the gap by proposing a framework in studying the social media surveillance on security breaches along with an empirical study to shed light on public attitudes and concerns.

In this study, the authors propose a framework for real-time monitoring of public perception to security breach events using social media metadata. Then, an empirical study was conducted on a sample of 1,13,340 related tweets collected in August 2015 on Twitter. By text mining a large number of unstructured, real-time information, the authors extracted topics, opinions and knowledge about security breaches from the general public. The time series analysis suggests significant trends for multiple topics and the results from sentiment analysis show a significant difference among topics.

The study confirms that social media monitoring provides a supplementary tool for the traditional surveys which are costly and time-consuming to track security breaches. Sentiment score and impact factors are good predictors of real-time public opinions and attitudes to security breaches. Unusual patterns/events of security breaches can be detected in the early stage, which could prevent further destruction by raising public awareness.

The sample data were collected from a short period of time on Twitter. Future study could extend the research to a longer period of time or expand key words search to observe the sentiment trend, especially before and after large security breaches, and to track various topics across time.

The findings could be useful to inform public policy and guide companies responding to consumer security breaches in shaping public perception.

This study is the first of its kind to undertake the analysis of social media (Twitter) content and sentiment on public perception to security breaches.

This study aims to examine how degree of servitization and breadth of servitization affect the firm performance, and the moderating roles of corporate social responsibility (CSR) and green innovation.

The hypotheses are tested using panel data regression models from 2010 to 2020 based on 10,539 firm-year observations from 2,362 Chinese listed manufacturers.

Considering the classification of servitization, the results show that both degree and breadth of servitization have positive impacts on firm performance. In addition, CSR strengthens these impacts, while green innovation only strengthens the impact of degree of servitization on firm performance.

This study suggests that manufacturers should obtain greater profits by actively implementing the servitization strategy, and effectively take their social responsibility by achieving value co-creation with stakeholders. Also, they should carry out green innovation, develop green products and provide eco-friendly services.

This study contributes to the servitization literature by deepening the understanding of the value creation effect of services based on the service-oriented logic. Also, this study contributes to the sustainability literature stream by revealing the moderating roles of CSR and green innovation based on the stakeholder theory.

The purpose of this paper is to explore a new eco-friendly green textile dyeing. Natural plant Buddleja officinalis is traditionally used as yellow pigment addition in rice. It is worth developing its application and dyeing performance in cotton fabric.

Buddleja officinalis dried flower was extracted with ethanol aqueous. The extraction conditions including ethanol concentration, material to liquor ratio, extract time and temperature were optimized. Then cotton fabrics were dyed with Buddleja officinalis extraction under conventional and ultrasonic conditions. The effects of dyeing time, bath ratio, pH value of dyeing bath, dyeing temperature and mordants on K/S values were studied and the resulting color strength obtained by conventional and ultrasonic dyeing were compared. The ultraviolet (UV) transmittance of Buddleja officinalis dyed cotton fabric was also evaluated.

The color strength of the fabric dyed with Buddleja officinalis under ultrasonic conditions was higher than that under conventional conditions. Alum, Fe and Cu as simultaneous mordants improved the K/S value of the dyed cotton fabrics. Both washing fastness and rubbing fastness were fairly good in all Buddleja officinalis dyed cotton fabrics, washing fastness = 3–4 and rubbing fastness = 4. What’s more, the dyed cotton fabrics showed lower transmittance values as compared to undyed cotton fabrics and indicated potential UV protection capability.

Buddleja officinalis can be a new natural dye source for the ultrasonic dyeing of cotton fabric.

It is for the first time that Buddleja officinalis is used as a natural dye in cotton fabric dyeing with less water and the dyeing using ultrasound has been found to have an obvious improvement in the color strength and color-fastness.

Unmanned aerial/ground vehicles (UAV/UGV) collaboration systems are increasingly being used to perform reconnaissance and rescue missions autonomously, especially in disaster areas. The paper aims to discuss this issue.

To improve visibility, this study proposes a path-planning algorithm based on map matching. Continuous ground images are first collected aerially using the UAV vision system. Subsequently, a global map of the ground environment is created by processing the collected images using the methods of image correction, image mosaic and obstacle recognition. The local map of the ground environment is obtained using the 2D laser radar sensor of the UGV. A set of features for both global and local maps is established. Unknown values during map matching are determined via the least squares method. Based on the matched mapping, the traditional A* algorithm is used for the planning of global path in the global map, and the dynamic window method is used for adjustment of the local map.

Simulation experiments were carried out to demonstrate the effectiveness of the proposed algorithm. The experimental results show that the proposed algorithm can construct a global map of the wide environment and effectively bypass the obstacles missed by the UAV.

Prior to map matching, there is a need to extract the edge of obstacles in the global map.

This paper proposed a path planning algorithm based on map matching, yielding insights into the application of the UAV/UGV collaboration systems in disaster areas.

The underdevelopment of the financial sector could be one of the barriers to the deployment of renewable energies in developing countries. The purpose of this paper is therefore to analyse the effect of financial development in the deployment of renewable energies in sub-Saharan African countries.

The empirical analysis is based on a production approach and a cross-sectionally augmented autoregressive distributive lag error correction model estimate for 25 sub-Saharan African countries over the period 1990–2018. The augmented mean group (AMG) and common correlated effects mean group (CCEMG) estimators were used for the robustness analysis.

Two results emerge: financial development contributes positively to renewable energy deployment in sub-Saharan African countries in the short and long run; and fossil fuel dependence impedes significantly renewable energy deployment in the short and long run. The robustness analyses using the AMG and CCEMG methods confirm these results.

These results suggest the need for policies to support and strengthen the development of the financial sector to improve its ability to effectively finance investments in renewable energy technologies.

The originality of this paper lies in the fact that the analysis is based on a renewable energy production approach. Indeed, the level of renewable energy deployment is measured by the production and not the consumption of renewable energy, unlike other previous work. In addition, this research uses recent econometric estimation techniques that overcome the problems of cross-sectional dependence and slope heterogeneity.