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The purpose of this study is to introduce the top-level design ideas and the overall architecture of earthquake early-warning system for high speed railways in China, which is based on P-wave earthquake early-warning and multiple ways of rapid treatment.

The paper describes the key technologies that are involved in the development of the system, such as P-wave identification and earthquake early-warning, multi-source seismic information fusion and earthquake emergency treatment technologies. The paper also presents the test results of the system, which show that it has complete functions and its major performance indicators meet the design requirements.

The study demonstrates that the high speed railways earthquake early-warning system serves as an important technical tool for high speed railways to cope with the threat of earthquake to the operation safety. The key technical indicators of the system have excellent performance: The first report time of the P-wave is less than three seconds. From the first arrival of P-wave to the beginning of train braking, the total delay of onboard emergency treatment is 3.63 seconds under 95% probability. The average total delay for power failures triggered by substations is 3.3 seconds.

The paper provides a valuable reference for the research and development of earthquake early-warning system for high speed railways in other countries and regions. It also contributes to the earthquake prevention and disaster reduction efforts.

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 many decades, it has been recognized that maintenance activities should be adapted to the specific usage of a system. For that reason, many advanced policies have been developed, such as condition-based and load-based maintenance policies. However, these policies require advanced monitoring techniques and rather detailed understanding of the failure behavior, which requires the support of an OEM or expert, prohibiting application by an operator in many cases. The present work proposes a maintenance policy that relieves the high (technical) demands set by these existing policies and provides a more accurate specification of the required (dynamic) maintenance interval than traditional usage-based maintenance.

The methodology followed starts with a review and critical assessment of existing maintenance policies, which are classified according to six different aspects. Based on the need for a technically less demanding policy that appears from this comparison, a new policy is developed. The consecutive steps required for this functional usage profiles based maintenance policy are then critically discussed: usage profile definition, monitoring, profile severity quantification and the possible extension to the fleet level. After the description of the proposed policy, it is demonstrated in three case studies on real systems.

A maintenance policy based on a simple usage registration procedure appears to be feasible, which enables a significantly more efficient maintenance process than the traditional usage-based policies. This is demonstrated by the policy proposed here.

The proposed maintenance policy based on functional usage profiles offers the operators of fleets of systems the opportunity to increase the efficiency and effectiveness of their maintenance process, without the need for a high investment in advanced monitoring systems and in experts interpreting the results.

The original contribution of this work is the explicit definition of a new maintenance policy, which combines the benefits of considering the effects of usage or environment severity with a limited investment in monitoring technology.

This work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.

The proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.

The proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.

Thanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.

The monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance.

Purpose – This study aims to design and build a wireless supervisory control and data acquisition (SCADA) system based on Protocol AX.25 with the aim of monitoring the performance of several parameters in Microhydro Power Plant (MHPP). This system can monitor several MHPP parameters such as voltage, current, frequency, and turbine rotation so that it can be accessed directly at one central location.

Design/Methodology/Approach – The design is done by taking into account the real parameters that exist in the MHPP. Some parameters that become the main object to see the performance of MHPP are voltage, current, frequency, and turbine rotation. The voltage generated by the MHPP must be adjusted to the voltage supplied by State Electricity Company to the consumer, including the phase used. The resulting stream should also be monitored for power to be adjusted to the turbine spin. The generator frequency is kept stable according to the standard frequency of the State Electricity Company generator.

Findings – The remote terminal unit (RTU) system has been simulated using 2 ACS712 current sensors, voltage sensor, zero crossing point, frequency sensor, and rotation sensor functionalized to monitor MHPP parameters. The AX.25 protocol has been applicable in the wireless SCADA network for monitoring the performance of MHPP by embedding in KYL-1020UA transceiver radio using the 433 MHz frequency and the audio frequency shift keying modulation system. Radio transmitter KYL-1020UA has been successfully simulated to send data from sensors to display on the computer through SCADA built applications. The data changes in the RTU section can be displayed properly on the graphic user interface in accordance with the existing display at the MHPP location.

Research Limitations/Implications – There are only two RTUs that will be connected to communicate, in this case MHPP-1 with callsign “RTU-001” and MHPP-2 with callsign “RTU-002.” While the existing devices in the data access section parameters MHPP as master station with callsign “MSSCADA” monitoring the performance of parameters sent from the RTU. There is no collision or error in data transmission. Baudrate is varied at 1,200 bps, 2,400 bps, 4,800 bps, and 9,600 bps for effective throughput calculation and AX.25 protocol efficiency. The transmission distance is varied at 100 m, 200 m, 300 m, and 500 m to see the bit error rate with baudrate 1,200 bps and 9,600 bps.

Practical Implications – This product is expected to be applied to several MHPP locations in Aceh Province so that its monitoring system is more centralized and efficient.

Originality/Value – This research if for the efficient monitoring of several MHPP located far apart and can be monitored in one central location so that operators do not have to be located at the plant site.

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.

The future construction site will be pervasive, context aware and embedded with intelligence. The purpose of this paper is to explore and define the concept of the digital skin of the future smart construction site.

The paper provides a systematic and hierarchical classification of 114 articles from both industry and academia on the digital skin concept and evaluates them. The hierarchical classification is based on application areas relevant to construction, such as augmented reality, building information model-based visualisation, labour tracking, supply chain tracking, safety management, mobile equipment tracking and schedule and progress monitoring. Evaluations of the research papers were conducted based on three pillars: validation of technological feasibility, onsite application and user acceptance testing.

Technologies learned about in the literature review enabled the envisaging of the pervasive construction site of the future. The paper presents scenarios for the future context-aware construction site, including the construction worker, construction procurement management and future real-time safety management systems.

Based on the gaps identified by the review in the body of knowledge and on a broader analysis of technology diffusion, the paper highlights the research challenges to be overcome in the advent of digital skin. The paper recommends that researchers follow a coherent process for smart technology design, development and implementation in order to achieve this vision for the construction industry.

Purpose – The purpose of this paper is to develop prototype of the information system of the flood monitoring based internet of things (IoT). This prototype serves to assist users in accessing flood levels through water levels and rainy weather conditions.

Design/Methodology/Approach – This paper presents the design of information system of flood monitoring based internet of things (IoT). This prototype study acquires water level and rainfall data using ultrasonic sensors HC-SR04 and rain sensor. Data of flood height and rain levels detected by sensors are processed using Arduino Uno Microcontroller to produce output data in HTML format. Flood altitude information system and rainy weather from the microcontroller are distributed using ethernet module as web server integrated with Wireless N Router TL-MR3020 as a gateway path to the user.

Findings – This research produces a prototype of web-based flood monitoring information system that has been able to distribute data of flood height and rainy weather in real time.

Research Limitations/Implications – In the implementation of measurement, the information system only accesses one flood detector or one flooded location.

Practical Implications – This research produces a prototype of web-based flood monitoring information system that has been able to distribute data of flood height and rainy weather in real time.

Originality/Value – System prototype is the first development that uses internet of things (IoTs) method in real time web-based flood measurement with information in the form of flood hazard information in the form of water level and rainy weather conditions.

This study aims to explore the need for highly technological complexes for control and monitoring, as well as, new concepts and methodologies for maritime resource exploration and exploitation, which are in great demand nowadays.

This paper provides an analysis of demand, means of creation and development of the methodology and infrastructure for global monitoring, pollution control and supervision of smart systems for activities in exploration, future resource exploitation in deep-water and coastal areas based on Smarthub architecture, Unmanned Aircraft System (UAS), Continuous Acquisition and Life-Cycle Support (CALS) and Blockchain technologies.

Observational, experimental, simulation, derivational, hybrid descriptive and analytical models, as well as, surrogate models were created, analyzed and implemented for assigned tasks realization. Concept of distributed system for marine environmental monitoring, control and supervising as pilot technology in the context of Technology Readiness Levels (TRL) 3–5 was designed and evaluated.

The activities described in this article should be realized in the design and development of a complex, reliable, robust and sustainable monitoring and inspection system for the control and evaluation of the impact and risk assessment of the exploration and future exploitation of maritime resources.

The potential applications of information and communication technologies in the workplace are wide-ranging and, especially since the COVID-19 pandemic, have increasingly found their way into the field of electronic performance monitoring (EPM) of employees. This study aims to examine the influence of EPM on individual performance considering the aspects of privacy invasion, organizational trust and individual stress within an organization. Thus, important insights are generated for academia as well as business.

A theoretical framework was developed which conceptualizes perceived EPM as independent variable and individual performance as dependent variable. Moreover, the framework conceptualizes three mediator variables (privacy invasion, organizational trust and individual stress). Based on a large-scale survey (N = 1,119), nine hypotheses were tested that were derived from the developed framework.

The results indicate that perception of EPM significantly increases privacy invasion, reduces organizational trust, increases individual stress and ultimately reduces individual performance. Moreover, it was found that privacy invasion reduces organizational trust and that this lowered trust increases individual stress. Altogether, these findings suggest that the use of EPM by employers may be associated with significant negative consequences.

This research enriches the literature on digital transformation, as well as human–machine interaction, by adopting a multidimensional theoretical and empirical perspective regarding EPM in the workplace context, in which the influence of EPM perceptions on individual performance is examined under the influence of different aspects (privacy invasion, organizational trust and individual stress) not currently considered in this combination in the literature.