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The introduction of the maritime autonomous surface ship (MASS) to the maritime industry will open up a new era and bring about a new paradigm shift in terms of cost efficiency, maritime accidents, and human resources. Various studies are currently being conducted to realize the MASS. Understanding the scope and direction of these studies will be of great help for future MASS research. In this study, the current development status of technologies for autonomous ships is identified, and considerations and directions of improvement are suggested for six major research fields that cover all technological issues of MASS. Firstly, the results of the regulatory scoping exercise (RSE) on the International Maritime Organization (IMO) conventions to accept MASSs are identified; in particular, human elements are identified as vital issues to be considered for the design and operation of MASSs. Secondly, various studies on the decision-making system are identified, and the future direction is suggested. Thirdly, in terms of ship design and propulsion system, design changes for autonomous cargo ships are investigated, with their potential impacts to be considered. Fourthly, the communication system will need to be robust and supported by multiple systems to minimize potential risk with third-party infrastructures, and suitable protection of systems, networks, and data will be required as an integral part of the safety system for cybersecurity. Fifthly, issues of maintenance and repair are identified, with a maintenance strategy to be considered. Lastly, hazard analysis of the autonomous ship is explored, and system-theoretic process analysis (STPA) and the functional resonance analysis method (FRAM) are identified as the most representative new methods that can be used for hazard analysis of autonomous ships.

The increase in telecommuting during COVID-19 and advances in digital technology have necessitated the establishment of guidelines for maximizing productivity through indoor space design for telecommuters. Additionally, understanding the physiological response of individuals working in indoor spaces has attracted attention. This study applied mixed-reality environment to alter the design of the indoor space in real-time, while monitoring the task performance and representative psycho-physiological indicators (electroencephalogram and heart rate variability) of 30 individuals with telecommuting experience. To this end, four tasks, including spatial memory, attention, execution, and working memory, were conducted, and the psycho-physiological data from these tests were statistically analyzed. The results revealed that the design of the indoor space did not affect the spatial memory; however, the parasympathetic nerves were stimulated in visually non-preferred spaces, thus reducing mental stress and leading to high efficiency in short-term work. According to the Yerkes-Dodson law, the working memory of an individual is generally efficient and physically stable over time if they adjust to a preferred or decision-making space. Thus, the future design of telecommuting spaces must consider the type of work being done, and guidelines for spatial design should be developed by recognizing the psycho-physiological status of users, while increasing efficiency.

Low-dimensional transition-metal dichalcogenides (TMDs) have recently emerged as promising materials for electronics and optoelectronics. In particular, photodetectors based on mono- and multilayered molybdenum disulfide (MoS2) have received much attention owing to their outstanding properties, such as high sensitivity and responsivity. In this study, photodetectors based on dispersed MoS2 nanoflakes (NFs) are demonstrated. MoS2 NFs interact with Ag nanoparticles (NPs) via low-temperature annealing, which plays a crucial role in determining device characteristics such as good sensitivity and short response time. The fabricated devices exhibited a rapid response and recovery, good photo-responsivity, and a high on-to-off photocurrent ratio under visible light illumination with an intensity lower than 0.5 mW/cm2.

We investigated characteristics of highly flexible and stretchable electrodes consisting of Galinstan (i.e., a gallium-based liquid metal alloy) under various conditions including sub-zero temperature (i.e., <0 °C) and demonstrated solar-blind photodetection via the spontaneous oxidation of Galinstan. For this work, a simple and rapid method was introduced to fabricate the Galinstan electrodes with precise patterns and to exfoliate their surface oxide layers. Thin conductive films possessing flexibility and stretchability can be easily prepared on flexible substrates with large areas through compression of a dried suspension of Galinstan microdroplets. Furthermore, a laser marking machine was employed to facilitate patterning of the Galinstan films at a high resolution of 20 μm. The patterned Galinstan films were used as flexible and stretchable electrodes. The electrical conductivity of these electrodes was measured to be ~1.3 × 106 S m−1, which were still electrically conductive even if the stretching ratio increased up to 130% below 0 °C. In addition, the surface oxide (i.e., Ga2O3) layers possessing photo-responsive properties were spontaneously formed on the Galinstan surfaces under ambient conditions, which could be solely exfoliated using elastomeric stamps. By combining Galinstan and its surface oxide layers, solar-blind photodetectors were successfully fabricated on flexible substrates, exhibiting a distinct increase of up to 14.7% in output current under deep ultraviolet irradiation (254 nm wavelength) with an extremely low light intensity of 0.1 mW cm−2, whereas no significant change was observed under visible light irradiation.

Green retrofit is regarded as an effective environmental measure to reduce greenhouse gas emissions in high energy-consuming commercial buildings. However, the current retrofitting rate of complex structures is lower than the expected rate. This study proposed a method of stimulating the interaction of multiple agents (government, developers, and occupants) involved in the green renovation of China’s commercial buildings. To this end, the evolutionary game theory was applied to determine the multiple interaction mechanism of the behaviors of the agents, after which the key factors affecting the contrasting behavior of developers and occupants were demonstrated, and a sensitivity analysis was performed to distinguish detailed set parameters. The major results observed are as follows: (1) occupants are less sensitive to varied conditions owing to their vulnerable economic scale, meaning that a more friendly policy environment is essential to facilitate their support; (2) government financial support, such as subsidies or compensation costs, can strongly induce more positive behavior in developers to promote green retrofit; and (3) life-cycle awareness of developers should be improved as a reasonable energy-saving performance can act as a key motivating factor to support green renovation. This research provided a comparative perspective to that of a public–private partnership model.

Organizational clients with limited experience in strategic briefing often face challenges in identifying building project outcomes for achieving a competitive edge in their business. Communication with practitioners during strategic briefing facilitates clients in acknowledging the importance of timely decision-making and being involved in a knowledge spiral to acquire the information. Knowledge-acquisition by clients can lead to behavioral changes, both within themselves and their organizations. This study classifies knowledge-acquisition types (KATs) and investigates the potential for rational decision-making in briefings. A framework, developed through a literature review and practical insights, is validated by introducing the Action Research approach with stakeholders across ten building projects in sectors: manufacturing, retail, and public enterprises. The framework, refined during interactions among researchers, clients and service providers, identifies KAT1 as the domain knowledge, KAT2 as the administrative knowledge, and KAT3 is the facility knowledge of clients. KAT4, the difference between groups with and without construction-project experiences, relates to the procedures for achieving strategic objectives. This involves understanding the project and organizational characteristics through knowledge accumulation and managing client interactions to ensure successful projects. The Action Research framework facilitates knowledge exchange among clients and practitioners, empowering corporate clients to effectively achieve strategic objectives through group decision-making.

Sleep accounts for one-third of an individual’s life and is a measure of health. Both sleep time and quality are essential, and a person requires sound sleep to stay healthy. Generally, sleep patterns are influenced by genetic factors and differ among people. Therefore, analyzing whether individual sleep patterns guarantee sufficient sleep is necessary. Here, we aimed to acquire information regarding the sleep status of individuals in an unconstrained and unconscious state to consequently classify the sleep state. Accordingly, we collected data associated with the sleep status of individuals, such as frequency of tosses and turns, snoring, and body temperature, as well as environmental data, such as room temperature, humidity, illuminance, carbon dioxide concentration, and ambient noise. The sleep state was classified into two stages: nonrapid eye movement and rapid eye movement sleep, rather than the general four stages. Furthermore, to verify the validity of the sleep state classifications, we compared them with heart rate.

Establishing project costs in construction is crucial for project success, typically done through regression methods for prediction. While these methods are common, novel regression methods are less practiced in construction management. This study explores both traditional and modern regression techniques, analyzing data from 83 sewage pipeline projects in South Korea. The study implemented state-of-the-art frameworks, including hyperparameter optimization and k-fold cross-validation, to evaluate statistic, machine learning and deep learning based regression models using R2 score, RMSE, MAE, and MSE. Results revealed that performance metrics don’t always align with predictive accuracy. For instance, the random forest regressor achieved the best R2 score of 0.847 but ranked fifth in prediction accuracy. Moreover, polynomial regression outperformed novel methods with a 98.790% accuracy across the validation dataset.

PurposeThis study, a research project, aims to examine the distinct characteristics of the Fourth Industrial Revolution (4IR), with a focus on construction. Following this examination, the paper presents a field study to evaluate the impact of the 4IR on the construction process.Design/methodology/approachThe first half of this project is dedicated to defining the 4IR by reviewing existing literature. The other half of the project presents a case study to demonstrate the concept of the 4IR and measure the effect of its application. To validate the defined concept of the 4IR, the study focuses on the following: autonomous system for producing drawings and robotics in construction.FindingsThe intensive literature review revealed three unequivocal features of the 4IR: defined tasks, undefined tasks and improvement possibilities. The following case study showed that the incorporation of the three 4IR features resulted in improved productivity and efficiency during the construction of the podium for the Lotte World Tower. For example, the macro-based autonomous system achieved 5.52 shop drawings per hour, highlighting the potential impact of independent, autonomous machinery.Originality/valueThe originality of this project stems from its attempt to quantify the effectiveness of applying autonomous technologies to a practical project. While previous works in this field have focused on system development and improvement, this paper presents an autonomous system at work in an actual project, in which junior engineers were able to be entirely replaced. The system was successful in independently creating numerous required shop drawings. The value of this analysis is to generate scientific evidence to evaluate the efficacy of the adoption of 4IR-oriented technologies.

Construction projects are bound by uncertainties and changes by its nature. Thus, cost contingency needs to be allocated to construction project budget to cope with any deviation of actual costs from planned ones. However, existing methods for predicting cost contingencies, as studied and practiced, still present limitations in reliability and accuracy. Machine learning (ML) has gained popularity for enhancing prediction power in various fields. The paper aims to examine various ML algorithms to implement a cost contingency prediction model, employing both continuous and categorical predictor variables. To develop the model, construction transportation project datasets, which were bid between 2013‒2017, were collected from the Florida Department of Transportation (FDOT) website. To address imbalanced regression dataset issues, the synthetic minority over-sampling technique for regression with Gaussian noise (SMOGN) algorithm is introduced. ML random forest (RF) regression associated with random search hyperparameter optimization, achieved remarkably accurate predictions compared to extreme gradient boosting (XGBoost) regression and artificial neural network (ANN) models. The results also demonstrate that four parameters are significant factors in predicting construction cost contingency: project amount, project duration, and latitude and longitude factors. These findings provide new insights for researchers in developing models and for practitioners seeking more advanced method.