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The article aims to investigate the effects of ownership and capital structure on postacquisition operating performance.

The article extends the ongoing literature from an operating loss perspective and provides empirical evidence on the probability of acquirers’ operating loss in relation to ownership and capital structure. The operating performance of publicly listed manufacturing firms in China was tracked up to five years since the completion of the mergers and acquisitions (M&A) during 2003–2014.

The empirical results show that, in a five-year postacquisition period, state-owned enterprises (SOEs) are more likely to experience operating loss than non-SOEs. The likelihood of the operating loss is negatively associated with ownership concentration, implying that concentrated ownership may serve as an effective corporate governance mechanism in the emerging economy and improve postacquisition performance. The rise in leverage increases the likelihood of postacquisition operating loss, indicating that the costs of debt may outweigh the benefits.

The findings contribute to the literature on ownership, debt governance and post-M&A performance from an emerging economy perspective.

An accurate detection of overhead ground wire under open surroundings with varying illumination is the premise of reliable line grasping with the off-line arm when the inspection robot cross obstacle automatically. This paper aims to propose an improved approach which is called adaptive homomorphic filter and supervised learning (AHSL) for overhead ground wire detection.

First, to decrease the influence of the varying illumination caused by the open work environment of the inspection robot, the adaptive homomorphic filter is introduced to compensation the changing illumination. Second, to represent ground wire more effectively and to extract more powerful and discriminative information for building a binary classifier, the global and local features fusion method followed by supervised learning method support vector machine is proposed.

Experiment results on two self-built testing data sets A and B which contain relative older ground wires and relative newer ground wire and on the field ground wires show that the use of the adaptive homomorphic filter and global and local feature fusion method can improve the detection accuracy of the ground wire effectively. The result of the proposed method lays a solid foundation for inspection robot grasping the ground wire by visual servo.

This method AHSL has achieved 80.8 per cent detection accuracy on data set A which contains relative older ground wires and 85.3 per cent detection accuracy on data set B which contains relative newer ground wires, and the field experiment shows that the robot can detect the ground wire accurately. The performance achieved by proposed method is the state of the art under open environment with varying illumination.

This paper studies the lateral stability regulation of intelligent electric vehicle (EV) based on model predictive control (MPC) algorithm.

Firstly, the bicycle model is adopted in the system modelling process. To improve the accuracy, the lateral stiffness of front and rear tire is estimated using the real-time yaw rate acceleration and lateral acceleration of the vehicle based on the vehicle dynamics. Then the constraint of input and output in the model predictive controller is designed. Soft constraints on the lateral speed of the vehicle are designed to guarantee the solved persistent feasibility and enforce the vehicle’s sideslip angle within a safety range.

The simulation results show that the proposed lateral stability controller based on the MPC algorithm can improve the handling and stability performance of the vehicle under complex working conditions.

The MPC schema and the objective function are established. The integrated active front steering/direct yaw moments control strategy is simultaneously adopted in the model. The vehicle’s sideslip angle is chosen as the constraint and is controlled in stable range. The online estimation of tire stiffness is performed. The vehicle’s lateral acceleration and the yaw rate acceleration are modelled into the two-degree-of-freedom equation to solve the tire cornering stiffness in real time. This can ensure the accuracy of model.

Resilience concepts in integrated urban transport refer to the performance of dealing with external shock and the ability to continue to provide transportation services of all modes. A robust transportation resilience is a goal in pursuing transportation sustainability. Under this specified context, while before the perturbations, robustness refers to the degree of the system’s capability of functioning according to its design specifications on integrated modes and routes, redundancy is the degree of duplication of traffic routes and alternative modes to maintain persistency of service in case of perturbations. While after the perturbations, resourcefulness refers to the capacity to identify operational problems in the system, prioritize interventions and mobilize necessary material/ human resources to recover all the routes and modes, rapidity is the speed of complete recovery of all modes and traffic routes in the urban area. These “4R” are the most critical components of urban integrated resilience.

The trends of transportation resilience's connotation, metrics and strategies are summarized from the literature. A framework is introduced on both qualitative characteristics and quantitative metrics of transportation resilience. Using both model-based and mode-free methodologies that measure resilience in attributes, topology and system performance provides a benchmark for evaluating the mechanism of resilience changes during the perturbation. Correspondingly, different pre-perturbation and post-perturbation strategies for enhancing resilience under multi-mode scenarios are reviewed and summarized.

Cyber-physic transportation system (CPS) is a more targeted solution to resilience issues in transportation. A well-designed CPS can be applied to improve transport resilience facing different perturbations. The CPS ensures the independence and integrity of every child element within each functional zone while reacting rapidly.

This paper provides a more comprehensive understanding of transportation resilience in terms of integrated urban transport. The fundamental characteristics and strategies for resilience are summarized and elaborated. As little research has shed light on the resilience concepts in integrated urban transport, the findings from this paper point out the development trend of a resilient transportation system for digital and data-driven management.

Debt, dividend and investment policy constitutes a company's important financial decisions to determine firm performance. The research emphasizes on the problem of overinvestment, a phenomenon that worsens firm operation. Furthermore, it clarifies the moderation role of debt and dividend policy in mitigating the negative effect of overinvestment on firm performance in the case of Vietnamese listed companies.

The research uses all financial statement of non-financial Vietnamese listed companies on Ho Chi Minh and Hanoi Stock Exchange in the period of 2008–2018. The data are collected from Thomson Reuters Eikon. The final data set is comprised of 669 listed companies. The study measures overinvestment though investment demand function and HP filter. Moreover, the research employs the dynamic model, so it has to apply the SGMM method to deal with the problem of endogeneity caused by the lagged dependent variable.

The research finds that overinvestment is negatively associated with firm performance. Debt or dividend policy separately can moderate the negative effect of overinvestment on firm performance. However, when these two policies are combined, they lessen the positive interaction impact of each policy due to the substitution between debt and dividend policy.

The research may have two limitations. Firstly, the research measures overinvestment indirectly through investment demand function and HP filter. These two measures only help identify the sign that companies may have the problem of overinvestment because we cannot determine whether they overinvest or not in reality. Secondly, when using interaction variables, the problem of multicollinearity may be higher, and this may adjust the signs and significance level of variables in the models.

Practically, the research proposes three policy recommendations. Firstly, a company can exploit debt or dividend policy to limit excessive free cash flow in order to constrain the problem of overinvestment. Secondly, a company should enhance its corporate governance to resolve agency problems. Thirdly, the government should make the financial sector more transparent and effective to improve monitoring functions of various parties in the capital market.

Overinvestment sometimes can cause social issues. Overinvestment means that companies make ineffective investment. If they continue this situation over a long time, companies may have financial distress or even go bankruptcy. As a result, it will slow down economic growth and increase unemployment in the economy.

The research is supposed to make two great contributions to the existing empirical studies in two aspects. Firstly, it is the first attempt to take into consideration the interaction between overinvestment and financial policies. Secondly, it helps enhance the fundamental stance of the agency theory, which supports the interdependence of debt, dividend and investment policy.

Decentralized finance (DeFi), enabled by blockchain, could bring about a new financial system, where peers will interact directly, with little or no place for traditional intermediation. However, some crucial tasks cannot be left solely to an algorithm and, consequently, most DeFi applications still require human decisions. The aim of this research is to assess the role of intermediation in the light of DeFi, analysing how humans and algorithms will interact.

The authors based their work on a twofold qualitative methodology, first analysing publicly available secondary data, particularly from white papers and DeFi Pulse (a website providing data on DeFi solutions) and then running two focus group discussions.

DeFi does not eliminate financial intermediation, but enables it to be performed in new ways, where decentralization means that no single entity can hold too much power or monopoly. DeFi has, however, inherited risks from the underlying technologies that unintentionally facilitate illegal behaviour and can hamper the authorities’ supervision. The complex duality algorithm- vs human-based actions will not be solved indisputably in favour of the former, as DeFi solutions can range from requiring algorithms to play a dominant role, to enabling greater human interaction by actively involving more people.

This research contributes to the emerging debate between algorithm- and human-based intermediation, especially in relation to the standing literature on financial intermediation, where considerations made in the light of the newest theories on blockchain and DeFi are still scarce.

This paper aims to provide a typical example of accounting for the carbon dioxide equivalent (CO₂e) in underdeveloped cities, especially for the Poyang Lake area in China. The accounting can increase public understanding and trust in climate mitigation strategies by showing more detailed data.

The paper uses the “Global Protocol for Community-scale greenhouse gas emission inventories (GPC)” method, a worldwide comparable framework for calculating urban CO₂e emission (CE). The empirical case is an underdeveloped city, Nanchang, in China.

The results show the total CE of Nanchang, containing the electricity CE of Scope 2, grew rapidly from 12.49 Mt in 1994 to 55.00 Mt in 2014, with the only recession caused by the global financial crisis in 2008. The biggest three contributors were industrial energy consumption, transportation and industrial processes, which contributed 44.71-72.06, 4.10-25.07 and 9.07-22.28 per cent, respectively, to the total CE. Almost always, more than 74.41 per cent of Nanchang’s CE was related to coal. When considering only the CEs from coal, oil and gas, these CEs per unit area of Nanchang were always greater than those of China and the world. Similarly, these CEs per gross domestic product of Nanchang were always bigger than those of the world. Thus, based on these conclusions, some specific countermeasures were recommended.

This paper argues that the CO₂e accounting of underdeveloped cities by using the GPC framework should be promoted when designing climate mitigation policies. They can provide more scientific data to justify related countermeasures.

This paper aims to assess the progress in the provision of basic services in urban slums in Bangladesh during the transition period of millennium development goals (MDGs) to sustainable development goals (SDGs).

The study used a mixed method of research. The empirical part of the research was conducted in three Blocks of Rupsha slum in Khulna city. Randomly selected 120 households were interviewed through a structured questionnaire; three focus group discussion sessions (FGDs) were also conducted. Progress in the slum residents’ access to basic services during the transition from MDGs to SDGs is tacked based on primary data. The User Satisfaction Index (USI) and Network Analysis tools in ArcGIS are used to identify the gaps in service provision.

Findings show that a very significant proportion of families (56.67%) encounter an acute level of difficulties to gain smooth access to water services. About 89% of respondents have only access to a common or shared toilet facility where one common toilet is used by 20–25 persons. About 31% of families are unable to send their children to primary school even after four years of the adoption of SDGs. Achievements in most indicators of basic services in the slum are in general lower than the national level. Moreover, there exists spatial variability within the same slum. After four years of the transition from MDGs to SDGs, most of the services are poorly satisfying the residents of the Rupsha slum, and water service provision is in worse condition. The findings of this study have unveiled that while achievement in target areas is appreciable at the macro level, at the micro-level; however, good achievement in the provision of few basic services in the low-income settlement is more rhetoric than reality. Therefore, a lot more work needs to be done during the SDG phase to give the slum residents a decent quality of life as they have missed the MDGs’ train.

Study single-out works need to be done during the SDGs phase to give the slum residents a decent quality of life as they have missed the MDGs’ train.

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.