In the geochemical field, the chemical speciation of hexavalent uranium (U(VI)) has been widely investigated by performing measurements to determine its luminescence properties, namely the excitation, emission, and lifetime. Of these properties, the excitation has been relatively overlooked in most time-resolved laser fluorescence spectroscopy (TRLFS) studies. In this study, TRLFS and continuous-wave excitation–emission matrix spectroscopy are adopted to characterize the excitation properties of U(VI) surface species that interact with amorphous silica. The luminescence spectra of U(VI) measured from a silica suspension and silica sediment showed very similar spectral shapes with similar lifetime values. In contrast, the excitation spectra of U(VI) measured from these samples were significantly different. The results show that distinctive excitation maxima appeared at approximately 220 and 280 nm for the silica suspension and silica sediment, respectively.

To enhance the effectiveness of the FMS (flexible manufacturing system), it is necessary for the manufacturing control system to be upgraded by integrating the cyber and the physical manufacturing systems. Using the CPPS (Cyber-Physical Production System) concept, this study proposes a 4-stage vertical integration and control framework for an aircraft parts manufacturing plant. In the proposed framework, the process controller prepares the operations schedule for processing work orders generated from the APS (advanced planning & scheduling) system. The scheduled operations and the related control commands are assigned to equipments by the dispatcher of the line controller. The line monitor is responsible for monitoring the overall status of the FMS including work orders and equipments. Finally the process monitor uses the simulation model to check the performance of the production plan using real time plant status data. The W-FMCS (Wing rib-Flexible Manufacturing Control & Simulation) are developed to implement the proposed 4-stage CPPS based FMS control architecture. The effectiveness of the proposed control architecture is examined by the real plant’s operational data such as utilization and throughput. The performance improvement examined shows the usefulness of the framework in managing the smart factory’s operation by providing a practical approach to integrate cyber and physical production systems.

To develop a empirical model for predicting the spring flight period of overwintering Ips acuminatus adult, their density were monitored in Korean pine (Pinus koraiensis) forests at Chuncheon in 2015. The monitoring data of the beetles and temperature in the forests were used to develop the empirical predictive model based on degree-day model, and it was validated using the data from Korean pine forest at Wonju in 2018. The lower threshold temperature for flight (LTF) and a thermal requirement for the onset of flight activity of the beetles in spring were estimated. As the result, the LTF was estimated as 1.3 ℃ and 269.96 DD was required for the spring flight. The median flight date estimated by the empirical model was one day earlier than the observed flight date. Therefore, the model is suitable for predicting the spring flight of overwintering I. acuminatus.