Extensive floating macroalgae have drifted from the East China Sea to Japan’s offshore area, and field observation cannot sufficiently grasp their extensive spatial and temporal changes. High-spatial-resolution satellite data, which contain multiple spectral bands, have advanced remote sensing analysis. Several indexes for recognizing vegetation in satellite images, namely, the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and floating algae index (FAI), are useful for detecting floating macroalgae. Thresholds are defined to separate macroalgae-containing image pixels from other pixels, and adaptive thresholding increases the reliability of image segmentation. This study proposes adaptive thresholding using Sentinel-2 satellite data with a 10 m spatial resolution. We compare the abilities of Otsu’s, exclusion, and standard deviation methods to define the floating macroalgae detection thresholds of NDVI, NDWI, and FAI images. This comparison determines the most advantageous method for the automatic detection of floating macroalgae. Finally, the spatial coverage of floating macroalgae and the reproducible combination needed for the automatic detection of floating macroalgae in Kagoshima, Japan, are examined.

This study introduces a novel approach to estimate the higher heating value of coal using real-time operational data from coal-fired power plants, addressing a significant gap in conventional methodologies. Traditionally, coal quality assessments involve extensive laboratory testing, which is impractical for real-time applications. This research develops a practical alternative by leveraging operational parameters such as main steam pressure, temperature, load, condensate flow, and coal flow as indicators of coal’s calorific value. The model developed in this study bypasses the time-consuming processes associated with traditional methods, enabling real-time estimation of coal’s higher heating value. Empirical validation shows the model’s high predictive accuracy, evidenced by an R2 value of 0.9666, indicating that it accounts for approximately 96.66 % of the variance in higher heating value. These results are supported by low mean square error and root mean square error values, underscoring superior performance compared to conventional methods. The effective use of operational data not only addresses the challenge of real-time higher heating value estimation but also optimizes the combustion process and enhances power plant efficiency. The practical application of these findings is pivotal for real-time coal quality control and plant performance management, providing a crucial tool for optimizing energy management. In conclusion, this research successfully develops and validates a data-driven approach for the real-time prediction of coal’s calorific value. This approach holds potential for widespread application, thereby improving energy management and operational efficiency in an industry that remains a major global energy provider

Dynamic changes in the global market demand affect ship development. Correspondingly, big data have provided the ability to comprehend the current and future conditions in numerous sectors and understand the dynamic circumstances of the maritime industry. Therefore, we have developed a basic ship-planning support system utilizing big data in maritime logistics. Previous studies have used a ship allocation algorithm, which only considered the ship cost (COST) along limited target routes; by contrast, in this study, a basic ship-planning support system is reinforced with particularized COST attributes and greenhouse gas (GHG) features incorporated into a ship allocation algorithm related to the International Maritime Organization GHG reduction strategy. Additionally, this system is expanded to a worldwide shipping area. Thus, we optimize the operation-level ship allocation using the existing ships by considering the COST and GHG emissions. Finally, the ship specifications demanded worldwide are ascertained by inputting the new ships instance.

The shipping industry, a critical component of global logistics, faces persistent operational risks that threaten safety, environmental integrity, and economic stability. Traditional risk assessments, often reliant on descriptive statistics, fail to capture the probabilistic and multifaceted nature of maritime accidents. This study bridges this gap by developing a robust Monte Carlo simulation framework to quantify incident probabilities for a tanker fleet. Utilizing a comprehensive dataset from a shipping company, including incident reports, tanker characteristics, and root causes, the model iteratively samples operational and technical variables up to 50,000 iterations to project risk distributions and identify critical failure pathways. The results demonstrate that risk is highly contextual and not an intrinsic tanker property. The analysis reveals that mid-sized tankers (20,000–35,000 GT) are most susceptible to technical failures like propulsion and auxiliary machinery breakdowns, aligning with their high risk for asset loss and security breaches. Conversely, larger tankers (> 60,000 GT) exhibit systematically lower risk across most categories, which is attributed to advanced safety systems and stricter protocols. A notable exception is environmental risk, where smaller tankers (≤ 5000 GT) pose the lowest threat due to their limited spillage potential. The simulation achieved convergence at 10,000 iterations for personnel injury and security breach incidents, and 5000 for asset loss and environmental impacts, providing a validated threshold for reliable prediction. This study concludes that the Monte Carlo method effectively translates historical data into actionable insights, enabling proactive, precisely timed mitigations tailored to specific tanker profiles and incident types. The findings offer a paradigm shift from reactive to predictive risk management in maritime operations.

The dynamic and complex nature of the shipbuilding industry necessitates efficient facility layout planning to optimize operational efficiency and minimize costs. Traditional layout design approaches, often based on practical experience, fall short of achieving optimal results. This paper reviews three advanced methodologies for shipyard facility layout design: heuristic algorithms, Systematic Layout Planning (SLP), and graph theory. Heuristic algorithms, including genetic algorithms and simulated annealing, offer flexible and rapid solutions but may not always achieve global optimization. SLP provides a structured and methodical approach, ideal for stable environments, yet lacks flexibility in dynamic settings. Graph theory enhances the layout design process by optimizing spatial relationships between facilities through weighted planar graphs. The study highlights the strengths and limitations of each method, with a focus on their impact on material handling costs and overall layout efficiency. Among these, the combination of Genetic Algorithms (GA) and Stochastic Growth Algorithms (SGA) stands out, demonstrating significant reductions in material handling costs, up to 23.1%. The review concludes that while each methodology has its merits, the integration of GA and SGA offers the most robust solution for optimizing shipyard layouts, particularly in complex and large-scale environments. Future research should explore hybrid models that combine these methodologies, incorporating advanced computational techniques and real-time data analytics to create more dynamic and adaptable layout solutions, addressing the evolving needs of the shipbuilding industry.

As a consequence of the global greenhouse gas problem, various adjustments are required, particularly in the energy sector. The government program envisages the development and conversion of power plant fuels to LNG to ensure energy distribution throughout the region, especially in Indonesia's remote islands. One of the biggest challenges in distributing energy to remote locations is the logistical difficulties in reaching these areas. Utilization-scale LNG carriers as a mode of transportation offers a potential solution to this problem. This study aims to optimize the distribution of LNG using small-scale LNG carrier transportation mode to meet the needs of power plants. The application of the Capacitated Vehicle Routing Problem method to a single vessel with different speed variations is used to optimize the distribution of LNG aiming to maximize its cargo capacity. The results of this study show that using a vessel with identical specifications as in Eastern Indonesia, operating at a specific speed, optimizes cargo capacity for a single round trip. These results will serve as a basis for future studies and facilitate the further development of existing methods.