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Decarbonization is the most urgent task for the shipping industry. The International Maritime Organization, which makes the rules for shipping companies, has strengthened their carbon emissions regulation in order to reduce emissions to 70% of 2008’s carbon emissions by 2050. However, 75% of the existing fleet cannot satisfy this carbon emission regulation. The building of new vessels makes it possible to reduce carbon emissions and satisfy this regulation through adopting eco-friendly propulsion methods, such as LNG, ammonia, and methanol propulsion. However, the existing vessels on the sea find it difficult to dock and change their propulsion equipment. This research aims to propose a novel voyage operation method—a just-in-time arrival policy—that converts vessels’ waiting time into voyage time. The proposed method can reduce carbon emissions without propulsion system alteration and expand a vessel’s lifespan, thus satisfying carbon regulations. The carbon intensity indicator, invented by the IMO to regulate vessel carbonization, assesses the quantity of reduced carbon emissions. This research investigated the variation in the carbon intensity indicators of vessels when the just-in-time arrival policy was applied through studying an actual vessel’s arrival and departure dates at the Pusan International container terminal. According to the results of our analysis, ship carbon emissions decreased by an average of 45.8%, and by a maximum of 91%, compared to the levels before applying the proposed method. In addition, 87.0% of vessels obtained a carbon intensity indicator rank improvement and expanded the period that can satisfy the carbon intensity regulation by an average of eleven years and a maximum of twenty-seven years through applying the proposed just-in-time arrival policy. Additionally, the improvement effect of the carbon intensity rank positively correlates with ship size and waiting time at the port.

The maritime industry is a major carbon emission contributor. Therefore, the global maritime industry puts every effort into reducing carbon emissions in the shipping chain, which includes vessel fleets, ports, terminals, and hinterland transportation. A representative example is the carbon emission reduction standard mandated by the International Maritime Organization for international sailing ships to reduce carbon emissions this year. Among the decarbonization tools, the most immediate solution for reducing carbon emissions is to reduce vessel waiting time near ports and increase operational efficiency. The operation efficiency improvement in maritime stakeholders’ port operations can be achieved using data. This data collection and operational efficiency improvement can be realized using a digital twin. This study develops a digital twin that measures and reduces carbon emissions using the collaborative operation of maritime stakeholders. In this study, the authors propose a data structure and backbone scheduling algorithm for a port digital twin. The interactive scheduling between a port and its vessels is investigated using the digital twin. The digital twin’s interactive scheduling for the proposed model improved predictions of vessel arrival time and voyage carbon emissions. The result of the proposed digital twin model is compared to an actual operation case from the Busan New Port in September 2022, which shows that the proposed model saves over 75 % of the carbon emissions compared with the case.

This study introduces global path planning for autonomous ships in port environments, with a focus on the Port of Ulsan, where various environmental factors are modeled for analysis. Global path planning is considered to take place from departure to berth, specifically accounting for scenarios involving a need to navigate via anchorage areas as waypoints due to unexpected increases in port traffic or when direct access to the berth is obstructed. In this study, a navigable grid for autonomous ships was constructed using land, breakwater, and water depth data. The modeling of the Port of Ulsan’s traffic lanes and anchorage areas reflects the port’s essential maritime characteristics for global path planning. In this study, an improved A* algorithm, along with grid-based path planning, was utilized to determine a global path plan. We used smoothing algorithms to refine the global paths for practical navigation, and the validation of these paths was achieved through conducting ship maneuvering simulations from model tests, which approximate real-world navigation in navigational simulation. This approach lays the groundwork for enhanced route generation studies in complex port environments.

The molecular dynamics (MD) simulations were applied to the melting transition of the BCC metal Fe using the modified embedded atom method (MEAM) potential proposed by Jin et al. [Appl. Phys. A120 (2015) 189], and the newly derived formulas were adopted to calculate the forces acting on atoms in the MD simulations. We first determined the structural and energetic properties of the effectively infinite solid with no boundaries, and then investigated the Fe samples with low-index surfaces, namely Fe(100), Fe(110), and Fe(111). The simulations show that as the temperature increases, the (111) surface firstly disorders, followed by the (100) surface, while the (110) surface remains stable up to the melting temperature. The disorder phenomenon diffuses from the surface to the entire block, and as the density of atoms on the surface decreases, the effect of the premelting phenomenon also increases, being most pronounced on Fe(111) which has the lowest surface density. This conclusion is in line with the behavior found for BCC metal V in the previous simulation study.

Coastal regions worldwide face increasing air pollution due to maritime activities. This technical note focuses on assessing the air pollution in the Daesan port area, Republic of Korea, using hourly emission measurements. Leveraging Automatic Identification System (AIS) data, we estimate vessel-induced air pollutant emissions and correlate them with real-time measurements. Vessel navigational statuses are categorized from the AIS data, enabling an estimation of fuel oil consumption. Random Forest models predict specific fuel oil consumption and maximum continuous ratings for vessels with unknown engine details. Using emission factors, we calculate the emissions (CO2, NO2, SO2, PM-10, and PM-2.5) from vessels visiting the port. These estimates are compared with actual air pollutant concentrations, revealing a qualitative relationship with an average correlation coefficient of approximately 0.33.

Because high-molecular-weight glutenin subunits (HMW-GS) are important contributors to wheat end-use quality, there is a need for high-throughput identification of HMW-GS in wheat genetic resources and breeding lines. We developed an optimized method using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) to distinguish individual HMW-GS by considering the effects of the alkylating reagent in protein extraction, solvent components, dissolving volume, and matrix II components. Using the optimized method, 18 of 22 HMW-GS were successfully identified in standard wheat cultivars by differences in molecular weights or by their associations with other tightly linked subunits. Interestingly, 1Bx7 subunits were divided into 1Bx7 group 1 and 1Bx7 group 2 proteins with molecular weights of about 82,400 and 83,000 Da, respectively. Cultivars containing the 1Bx7 group 2 proteins were distinguished from those containing 1Bx7OE using well-known DNA markers. HMW-GS 1Ax2* and 1Bx6 and 1By8 and 1By8*, which are difficult to distinguish due to very similar molecular weights, were easily identified using RP-HPLC. To validate the method, HMW-GS from 38 Korean wheat varieties previously evaluated by SDS-PAGE combined with RP-HPLC were analyzed by MALDI-TOF-MS. The optimized MALDI-TOF-MS method will be a rapid, high-throughput tool for selecting lines containing desirable HMW-GS for breeding efforts.

Container shipping plays a pivotal role in global trade, and understanding the duration that vessels spend in ports is crucial for efficient voyage planning by shipping companies. However, these companies often rely solely on one-way communication for required arrival times provided by terminals. This reliance on fixed schedules can lead to vessels arriving punctually, only to face berths that are still occupied, resulting in unnecessary waiting times. Regrettably, limited attention has been given to these issues from the perspective of shipping companies. This study addresses this gap by focusing on the estimation of dwell times for container vessels at a terminal in the Port of Busan using various machine learning techniques. The estimations were compared against the terminal’s operational reference. To compile the dataset, a 41-month history of terminal berth schedules and vessel particulars data were utilized and preprocessed for effective training. Outliers were removed, and dimensions were reduced. Six regression machine learning algorithms, namely adaptive learning, gradient boosting, light gradient boosting, extreme gradient boosting, categorical boosting and random forest, were employed, and their parameters were fine-tuned for optimal performance on the validation dataset. The results indicated that all models exhibited superior performance compared to the terminal’s operating reference model.

As the amount of online video content is increasing, consumers are becoming increasingly interested in various product names appearing in videos, particularly in cosmetic-product names in videos related to fashion, beauty, and style. Thus, the identification of such products by using image recognition technology may aid in the identification of current commercial trends. In this paper, we propose a two-stage deep-learning detection and classification method for cosmetic products. Specifically, variants of the YOLO network are used for detection, where the bounding box for each given input product is predicted and subsequently cropped for classification. We use four state-of-the-art classification networks, namely ResNet, InceptionResNetV2, DenseNet, and EfficientNet, and compare their performance. Furthermore, we employ dilated convolution in these networks to obtain better feature representations and improve performance. Extensive experiments demonstrate that YOLOv3 and its tiny version achieve higher speed and accuracy. Moreover, the dilated networks marginally outperform the base models, or achieve similar performance in the worst case. We conclude that the proposed method can effectively detect and classify cosmetic products.

Ionizing radiation combined with in vitro tissue culture has been used for development of new cultivars in diverse crops. The effects of ionizing radiation on mutation induction have been analyzed on several orchid species, including Cymbidium. Limited information is available on the comparison of mutation frequency and spectrum based on phenotypes in Cymbidium species. In addition, the stability of induced chimera mutants in Cymbidium is unknown. In this study, we analyzed the radiation sensitivity, mutation frequency, and spectrum of mutants induced by diverse γ-ray treatments, and analyzed the stability of induced chimera mutants in the Cymbidium hybrid cultivars RB003 and RB012. The optimal γ-irradiation conditions of each cultivar differed as follows: RB003, mutation frequency of 4.06% (under 35 Gy/4 h); RB012, 1.51% (20 Gy/1 h). Re-irradiation of γ-rays broadened the mutation spectrum observed in RB012. The stability of leaf-color chimera mutants was higher than that of leaf-shape chimeras, and stability was dependent on the chimera type and location of a mutation in the cell layers of the shoot apical meristem. These results indicated that short-term γ-irradiation was more effective to induce mutations in Cymbidium. Information on the stability of chimera mutants will be useful for mutation breeding of diverse ornamental plants.

Radiation randomly induces chromosomal mutations in plants. However, it was recently found that the frequency of flower-color mutants could be specifically increased by upregulating anthocyanin pathway gene expression before radiation treatments. The mechanisms of chlorophyll biosynthesis and degradation are active areas of plant study because chlorophyll metabolism is closely connected to photosynthesis. In this study, we determined the dark/light treatment conditions that resulted in upregulation of the expression levels of six chlorophyll pathway genes, uroporphyrinogen III synthase (HEMD), uroporphyrinogen III decarboxylase (HEME2), NADPH-protochlorophyllide oxidoreductase (POR) A (PORA), chlorophyll synthase (CHLG), chlorophyllase (CLH2), and red chlorophyll catabolite reductase (RCCR), and measured their effects on the γ-irradiation-induced frequencies of leaf-color mutants in two Cymbidium cultivars. To degrade chlorophyll in rhizomes, 60–75 days of dark treatment were required. To upregulate the expressions of chlorophyll pathway genes, 10 days of light treatment appeared to be optimal. Dark/light treatments followed by γ-irradiation increased chlorophyll-related leaf mutants by 1.4- to 2.0-fold compared with γ-ray treatment alone. Dark/light treatments combined with γ-irradiation increased the frequency of leaf-color mutants in Cymbidium, which supports the wider implementation of a plant breeding methodology that increases the mutation frequency of a target trait by controlling the expression of target trait-related genes.