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The purpose of this paper is to provide the extensive review on dynamic monitoring of forestry area in China.

Countermeasure and suggestions were proposed for three aspects including the establishment of data sets with unified standards, top-level design of monitoring and assessment and analysis models, and establishment of the decision support platform with multiple scenario simulation.

Finally, the authors proposed key research area in this field, i.e., improving the systematic and optimal forest management through integrating and improving the data, models and simulation platforms and coupling the data integration system, assessment system and decision support system.

The authors explored the limitation of dynamic monitoring and state of the art research on data accumulation, professional model development and the analytical platform.

For the purpose of reducing the incidence of hazardous materials transport accident, eliminating the potential threats and ensuring their safety, aiming at the shortcomings in the process of current hazardous materials transportation management, this paper aims to construct the framework of hazardous materials transportation safety management system under the vehicle-infrastructure connected environment.

The system takes the intelligent connected vehicle as the main supporter, integrating GIS, GPS, eye location, GSM, networks and database technology.

By analyzing the transportation characteristics of hazardous materials, this system consists of five subsystems, which are vehicle and driver management subsystem, dangerous sources and hazardous materials management subsystem, route analysis and optimization subsystem, early warning and emergency rescue management subsystem, and basic information query subsystem.

Hazardous materials transportation safety management system includes omnibearing real-time monitoring, timely updating of system database, real-time generation and optimization of emergency rescue route. The system can reduce the transportation cost and improve the ability of accident prevention and emergency rescue of hazardous materials.

This study aims to investigate the service performances of a new full-section asphalt concrete waterproof sealing structure (FSACWSS) for the high-speed railway subgrade through on-site tracking, monitoring and post-construction investigation.

Based on the working state of the waterproof sealing structure, the main functional characteristics were analyzed, and a kind of roller-compacted high elastic modulus asphalt concrete (HEMAC) was designed and evaluated by several groups of laboratory tests. It is applied to an engineering test section, and the long-term performance monitoring and subgrade dynamic performance testing system were installed to track and monitor working performances of the test section and the adjacent contrast section with fiber-reinforced concrete.

Results show that both the dynamic performance of the track structure and the subgrade in the test section meet the requirements of the specification limits. The water content in the subgrade of the test section is maintained at 8–18%, which is less affected by the weather. However, the water content in the subgrade bed of the contrast section is 10–35%, which fluctuates significantly with the weather. The heat absorption effect of asphalt concrete in the test section makes the temperature of the subgrade at the shoulder larger than that in the contrastive section. The monitoring value of the subgrade vertical deformation in the test section is slightly larger than that in the contrastive section, but all of them meet the limit requirements. The asphalt concrete in the test section is in good contact with the base, and there are no diseases such as looseness or spalling. Only a number of cracks are found at the joints of the base plates. However, there are more longitudinal and lateral cracks in the contrastive section, which seriously affects the waterproof and sealing effects. Besides, the asphalt concrete is easier to repair, featuring good maintainability.

This research can provide a basis for popularization and application of the asphalt concrete waterproof sealing structure in high-speed railways.

This study aims to assess the effect of updating the Phase I data – to enhance the parameters' estimates – on the control charts' detection power designed to monitor social networks.

A dynamic version of the degree corrected stochastic block model (DCSBM) is used to model the network. Both the Shewhart and exponentially weighted moving average (EWMA) control charts are used to monitor the model parameters. A performance comparison is conducted for each chart when designed using both fixed and moving windows of networks.

Our results show that continuously updating the parameters' estimates during the monitoring phase delays the Shewhart chart's detection of networks' anomalies; as compared to the fixed window approach. While the EWMA chart performance is either indifferent or worse, based on the updating technique, as compared to the fixed window approach. Generally, the EWMA chart performs uniformly better than the Shewhart chart for all shift sizes. We recommend the use of the EWMA chart when monitoring networks modeled with the DCSBM, with sufficiently small to moderate fixed window size to estimate the unknown model parameters.

This study shows that the excessive recommendations in literature regarding the continuous updating of Phase I data during the monitoring phase to enhance the control chart performance cannot generally be extended to social network monitoring; especially when using the DCSBM. That is to say, the effect of continuously updating the parameters' estimates highly depends on the nature of the process being monitored.

This paper critically aims to review existing monitoring strategies of Target 4.7 of the Sustainable Development Goals and proposes an alternative approach for reporting country progress on relevant Target 4.7 themes. Since this target constitutes one of the most ambitious and transformative education targets there is considerable value in developing a comprehensive reporting and monitoring strategy.

The paper draws on key policy documents to clarify processes leading up to the definition and measurement of a global indicator for Target 4.7. It also discusses limitations associated with the current reporting and measurement strategy.

The paper finds that the current monitoring approach to Target 4.7, based on an existing reporting mechanism for the 1974 Recommendation concerning Education for International Understanding, Co-operation and Peace and Education relating to Human Rights and Fundamental Freedom, is unfit for purpose and needs to be overhauled. The current process for revising the 1974 Recommendation is unlikely to result in a new monitoring strategy that would address existing weaknesses in the current strategy.

To the best of the authors’ knowledge, a critical review of measurement, reporting and monitoring strategies of Target 4.7 has not been undertaken. Also new in this paper is the proposed global observatory of Target 4.7 policies, practices and initiatives, which, if established, would work to: (1) create a more dynamic and informative monitoring infrastructure; (2) foster peer learning among countries; and (3) identify notable strategies of national, regional and international action in relation to Target 4.7.

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

The safety and reliability of high-speed trains rely on the structural integrity of their components and the dynamic performance of the entire vehicle system. This paper aims to define and substantiate the assessment of the structural integrity and dynamical integrity of high-speed trains in both theory and practice. The key principles and approaches will be proposed, and their applications to high-speed trains in China will be presented.

First, the structural integrity and dynamical integrity of high-speed trains are defined, and their relationship is introduced. Then, the principles for assessing the structural integrity of structural and dynamical components are presented and practical examples of gearboxes and dampers are provided. Finally, the principles and approaches for assessing the dynamical integrity of high-speed trains are presented and a novel operational assessment method is further presented.

Vehicle system dynamics is the core of the proposed framework that provides the loads and vibrations on train components and the dynamic performance of the entire vehicle system. For assessing the structural integrity of structural components, an open-loop analysis considering both normal and abnormal vehicle conditions is needed. For assessing the structural integrity of dynamical components, a closed-loop analysis involving the influence of wear and degradation on vehicle system dynamics is needed. The analysis of vehicle system dynamics should follow the principles of complete objects, conditions and indices. Numerical, experimental and operational approaches should be combined to achieve effective assessments.

The practical applications demonstrate that assessing the structural integrity and dynamical integrity of high-speed trains can support better control of critical defects, better lifespan management of train components and better maintenance decision-making for high-speed trains.

Forest as a vital natural resource in China plays an irreplaceable important role in safeguarding ecological security and human survival and development. Due to the vast territory, huge population and widespread forest landscape of China, forest management is a complex system involving massive data and various management activities. To effectively implement sustainable forest management, the big data technology has been utilized to analyze China’s forestry resources. Thus, the purpose of this paper is to clarify the role of big data technology in China’s forest management.

In this paper, the authors revisited the roles of big data in forest ecosystem monitoring, forestry management system development, and forest policy implementation.

It demonstrates that big data technology has a great potential in forest ecosystem protection and management, as well as the government’s determination for forest ecosystem protection. However, to deepen the application of big data in forest management, several challenges still need to be tackled.

Thus, enhancing modern science and technology to improve big data, cloud computing, and information technologies and their combinations will contribute to tackle the challenges and achieve wisdom of forest management.

This paper aims to explore the design of management teams when the critical task facing individual managers is monitoring the performance of worker teams and producing performance measures under uncertain information environments.

The authors use a multi-agent LEN framework – linear contract, exponential utility and normal density – to model the incentive provision and organizational design.

The main lesson is that the use of performance measures under uncertainty is greatly affected by the potential for free-riding in the very monitoring activities which generate the measures to begin with. Accordingly, the value of having a management team, that is the incremental benefit of having a second manager, depends on the monitoring technology. Of particular importance are the potential free-riding in monitoring effort among multiple managers and synergies gained from having more than one manager, such as correlation among the performance measures produced or improvement due to splitting workers pool into separate groups for each manager to monitor separately.

The paper pushes this line of research further by explicitly modeling the endogenous process of signal generation within a rich economic environment. In this environment, number of workers being evaluated and number of managers who produce the signals are both endogenous. Furthermore, both workers and managers are subject to moral hazard problem. In particular, the managers suffer from potential free-riding problems but may benefit from synergistic forces due to team monitoring.

The intelligent Central Traffic Control (CTC) system plays a vital role in establishing an intelligent high-speed railway (HSR) system. As the core of HSR transportation command, the intelligent CTC system is a new HSR dispatching command system that integrates the widely used CTC in China with the practical service requirements of intelligent dispatching. This paper aims to propose key technologies and applications for intelligent dispatching command in HSR in China.

This paper first briefly introduces the functions and configuration of the intelligent CTC system. Some new servers, terminals and interfaces are introduced, which are plan adjustment server/terminal, interface for automatic train operation (ATO), interface for Dynamic Monitoring System of Train Control Equipment (DMS), interface for Power Supervisory Control and Data Acquisition (PSCADA), interface for Disaster Monitoring, etc.

The key technologies applied in the intelligent CTC system include automatic adjustment of train operation plans, safety control of train routes and commands, traffic information data platform, integrated simulation of traffic dispatching and ATO function. These technologies have been applied in the Beijing-Zhangjiakou HSR, which commenced operations at the end of 2019. Implementing these key intelligent functions has improved the train dispatching command capacity, ensured the safe operation of intelligent HSR, reduced the labor intensity of dispatching operators and enhanced the intelligence level of China's dispatching system.

This paper provides further challenges and research directions for the intelligent dispatching command of HSR. To achieve the objectives, new measures need to be conducted, including the development of advanced technologies for intelligent dispatching command, coping with new requirements with the development of China's railway signaling system, the integration of traffic dispatching and train control and the application of AI and data-driven modeling and methods.