Air pollution sources and the hazards of high particulate matter 2.5 (PM_2.5) concentrations among air pollutants have been well documented. Shipping emissions have been identified as a source of air pollution; therefore, it is necessary to predict air pollutant concentrations to manage seaport air quality. However, air pollution prediction models rarely consider shipping emissions. Here, the PM_2.5 concentrations of the Busan North and Busan New Ports were predicted using a recurrent neural network and long short-term memory model by employing the shipping activity data of Busan Port. In contrast to previous studies that employed only air quality and meteorological data as input data, our model considered shipping activity data as an emission source. The model was trained from 1 January 2019 to 31 January 2020 and predictions and verifications were performed from 1–28 February 2020. Verifications revealed an index of agreements (IOA) of 0.975 and 0.970 and root mean square errors of 4.88 and 5.87 µg/m³ for Busan North Port and Busan New Port, respectively. Regarding the results based on the activity data, a previous study reported an IOA of 0.62–0.84, with a higher predictive power of 0.970–0.975. Thus, the extended approach offers a useful strategy to prevent PM_2.5 air pollutant-induced damage in seaports.

As the today's Web provides open communication environment for a variety of web resources, the Web of Things (WoT) offers new opportunity and challenges about the interoperation among the smart things. The well-known Web technologies can leverage the Web-enabled things to publish and exchange their resource information over the Web, then the Web-enabled thing should cope with the security threat regarding the information exposures over the Web, particularly, access permissions to the thing's resource information. Thus, in this paper we analyse access permission control mechanism considering both the WoT characteristics and the REST-compliant resource-oriented Web architecture. In contrast to existing access control logics, the proposed mechanism utilizes not only the requester information such as the typical identity and the internet addresses, but also the context of the thing itself. Based on this mechanism, we present web-resource structure for access permission control, and describe an exemplary procedure in detail. This research contributes to the flexible and decentralized access permission control for WoT.

Traditional factories are turning into smart factories with the advent of various ICT technologies, and various control decisions are derived by AI technologies. In this circumstance, runtime verification of a control command is important for zero-defect manufacturing processes but challengeable because factories of the future are highly complex and heterogeneous systems. In this paper, we propose DigTwinOps, a Digital Twin framework for Runtime Verification of Cyber-Physical Production Systems (CPPSs). DigTwinOps features a Digital Twin Execution Engine (DTEE) that manages a Digital Twin Model to synchronize states of a real CPPS object in a production environment. With a monitoring and simulation combination process, a human worker can observe the states of the CPPS object and verify the effectiveness of control commands before applying it to a real production environment. The proposed framework is applied to a CPPS prototype production system, and the results show that the framework can work effectively in the controllability verification of control commands.