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This article outlines the challenges faced by the next Chief Executive of the Hong Kong Special Administrative Region (HKSAR) in 2022 – the need to address economic problems resulting from Hong Kong’s slow growth; its inability to restructure its economy to broaden job opportunities and improve upward mobility for young people; and the government’s belated attempt to deploy innovation and technology.

This article is based on the author’s in-depth analysis of the current situation and insights on the challenges faced by the next Chief Executive.

Tensions are inherent in the concept of “One Country, Two Systems”. Back in November 2012, Deputy Director of the Hong Kong and Macao Office Zhang Xiaoming already reminded Hong Kong of the need to manage well three sets of relationships: (1) maintaining the “One Country” principle while preserving the SARs’ “separate systems”; (2) upholding Central Authority while preserving the SARs’ “high degree of autonomy”; and (3) unleashing the economic potential of mainland China while raising the competitiveness of the SARs. These three sets of relationships represent three fundamental difficulties in implementing “One Country, Two Systems”. However, Hong Kong kept ignoring Beijing’s advice and failed to resolve the tension between the mainland and Hong Kong SAR, culminating in the riotous events of 2019, which morphed into a dangerous, anti-China insurgency.

The next Chief Executive needs to mediate between the constitutional requirements of the Central Authority while preserving Hong Kong SAR’s “high degree of autonomy”, its unique character and lifestyle. He or she also needs to deal with continuity and change. Hong Kong cannot stand still, and should not allow itself to be lulled by the “50 years no change” mantra into overlooking the need to move with the times. Much reform needs to be implemented by the next Chief Executive to give people hope, faith in “One Country, Two Systems” and true love of the country.

Under the global climate change, carbon footprint has become a hot issue at home and abroad. However, there is no consensus on the concept, measurement and application of carbon footprint.

In this paper, first, the concept and connotation of carbon footprint are reviewed; then, different methods of carbon footprint measurement are compared, and it is found that “bottom-up” life cycle assessment and “top-down” input–output analysis are applicable to different research scales.

Finally, the problems in the process of carbon footprint assessment in textile industry are analyzed and further research directions are proposed.

Analyzed and further research directions are proposed.

Projecting the population distribution in geographical regions is important for many applications such as launching marketing campaigns or enhancing the public safety in certain densely populated areas. Conventional studies require the collection of people’s trajectory data through offline means, which is limited in terms of cost and data availability. The wide use of online social network (OSN) apps over smartphones has provided the opportunities of devising a lightweight approach of conducting the study using the online data of smartphone apps. This paper aims to reveal the relationship between the online social networks and the offline communities, as well as to project the population distribution by modeling geo-homophily in the online social networks.

In this paper, the authors propose the concept of geo-homophily in OSNs to determine how much the data of an OSN can help project the population distribution in a given division of geographical regions. Specifically, the authors establish a three-layered theoretic framework that first maps the online message diffusion among friends in the OSN to the offline population distribution over a given division of regions via a Dirichlet process and then projects the floating population across the regions.

By experiments over large-scale OSN data sets, the authors show that the proposed prediction models have a high prediction accuracy in characterizing the process of how the population distribution forms and how the floating population changes over time.

This paper tries to project population distribution by modeling geo-homophily in OSNs.

Human group behavior is the driving force behind many complex social and economic phenomena. Few studies have integrated multi-dimensional travel patterns and city interest points to construct urban security risk indicators. This paper combines traffic data and urban alarm data to analyze the safe travel characteristics of the urban population. The research results are helpful to explore the diversity of human group behavior, grasp the temporal and spatial laws and reveal regional security risks. It provides a reference for optimizing resource deployment and group intelligence analysis in emergency management.

Based on the dynamics index of group behavior, this paper mines the data of large shared bikes and ride-hailing in a big city of China. We integrate the urban interest points and travel dynamic characteristics, construct the urban traffic safety index based on alarm behavior and further calculate the urban safety index.

This study found significant differences in the travel power index among ride-sharing users. There is a positive correlation between user shared bike trips and the power-law bimodal phenomenon in the logarithmic coordinate system. It is closely related to the urban public security index.

Based on group-shared dynamic index integrated alarm, we innovatively constructed an urban public safety index and analyzed the correlation of travel alarm behavior. The research results fully reveal the internal mechanism of the group behavior safety index and provide a valuable supplement for the police intelligence analysis.

Recent years have witnessed the rapid development of massive open online courses (MOOCs). With more and more courses being produced by instructors and being participated by learners all over the world, unprecedented massive educational resources are aggregated. The educational resources include videos, subtitles, lecture notes, quizzes, etc., on the teaching side, and forum contents, Wiki, log of learning behavior, log of homework, etc., on the learning side. However, the data are both unstructured and diverse. To facilitate knowledge management and mining on MOOCs, extracting keywords from the resources is important. This paper aims to adapt the state-of-the-art techniques to MOOC settings and evaluate the effectiveness on real data. In terms of practice, this paper also tries to answer the questions for the first time that to what extend can the MOOC resources support keyword extraction models, and how many human efforts are required to make the models work well.

Based on which side generates the data, i.e instructors or learners, the data are classified to teaching resources and learning resources, respectively. The approach used on teaching resources is based on machine learning models with labels, while the approach used on learning resources is based on graph model without labels.

From the teaching resources, the methods used by the authors can accurately extract keywords with only 10 per cent labeled data. The authors find a characteristic of the data that the resources of various forms, e.g. subtitles and PPTs, should be separately considered because they have the different model ability. From the learning resources, the keywords extracted from MOOC forums are not as domain-specific as those extracted from teaching resources, but they can reflect the topics which are lively discussed in forums. Then instructors can get feedback from the indication. The authors implement two applications with the extracted keywords: generating concept map and generating learning path. The visual demos show they have the potential to improve learning efficiency when they are integrated into a real MOOC platform.

Conducting keyword extraction on MOOC resources is quite difficult because teaching resources are hard to be obtained due to copyrights. Also, getting labeled data is tough because usually expertise of the corresponding domain is required.

The experiment results support that MOOC resources are good enough for building models of keyword extraction, and an acceptable balance between human efforts and model accuracy can be achieved.

This paper presents a pioneer study on keyword extraction on MOOC resources and obtains some new findings.

Civil service workers are valuable resources for any nation and play a crucial role in driving their country’s economic development. Per the supervisor, this research examines the impact of mindfulness, proactive personality, and career competencies on employee job performance. The study also analyzes the effects of career adaptability and identity on this aspect.

To test the model of this study, questionnaires were administered to a sample of 500 civil service employees whose career-based knowledge and skills were measured in various cities in the province of Punjab, Pakistan.

Mindfulness and career competencies significantly impact supervisor-rated task performance, whereas a proactive personality does not substantially relate to supervisor-rated task performance. Research indicated that the two hypotheses about mediation were accepted. However, career adaptability does not play a significant role in the link between mindfulness and how well a supervisor rates task performance. Regarding moderation, career identity did not significantly moderate the relation between proactive personality and supervisor-rated task performance. However, the other two moderate hypotheses have been proven to be significant.

The findings offer compelling support for career construction theory (CCT) in this study area by analyzing the connections related to career adaptability and identity within the framework. In the future, researchers can build on this model by adding theories like conservation of resources (COR), looking into possible moderators that might change specific pathways in this network of relationships and using longitudinal designs to find stronger causal relationships.

Considering the evolving workplace due to the COVID-19 pandemic, the study offers fresh perspectives on the post-COVID situation, understanding and integrating various variables. For future studies, more variables can be explored in this model with the expansion of sample size and change of context.

In this paper, we present a new hybrid binary version of bat and enhanced particle swarm optimization algorithm in order to solve feature selection problems. The proposed algorithm is called Hybrid Binary Bat Enhanced Particle Swarm Optimization Algorithm (HBBEPSO). In the proposed HBBEPSO algorithm, we combine the bat algorithm with its capacity for echolocation helping explore the feature space and enhanced version of the particle swarm optimization with its ability to converge to the best global solution in the search space. In order to investigate the general performance of the proposed HBBEPSO algorithm, the proposed algorithm is compared with the original optimizers and other optimizers that have been used for feature selection in the past. A set of assessment indicators are used to evaluate and compare the different optimizers over 20 standard data sets obtained from the UCI repository. Results prove the ability of the proposed HBBEPSO algorithm to search the feature space for optimal feature combinations.

The purpose of this study is to examine the mediating role of career adaptability (CA) in the relationship between supervisor support (SS) and career satisfaction (CS).

Data were collected from 193 full-time employees working in private health-care institutions in Ankara, Turkey. The participants were asked to respond to a self-reported survey. Structural equation modeling was used to examine the hypothesized relationships between the research variables.

The results indicated that SS has a significant and positive effect on CS. It further reveals that CA mediates the effect of SS on CS.

As this study had a cross-sectional research design, causality could not be established between study variables.

CA ought to be considered by the managers and it ought to be advanced as it provides the employees fundamental instruments to deal with their career advancement efficiently.

The present study adds to the existing literature by providing additional evidence for the relationship among SS, CA and CS by examining a sample of health-care professionals.