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With the development of maritime technology and equipment, most ships are equipped with an automatic identification system (AIS) to store navigation information. Over time, the size of the data increases, rendering its storage and processing difficult. Hence, it is necessary to transform the AIS data into trajectories, and then simplify the AIS trajectories to remove unnecessary information that is not related to route shape. Moreover, topographic information must be considered because otherwise, the simplified trajectory can intersect obstacles. In this study, we propose an AIS trajectory simplification algorithm considering topographic information. The proposed algorithm simplifies the trajectories without the intersection of the trajectory and obstacle using the improved Douglas–Peucker algorithm. Polygon map random (PMR) quadtree was used to consider topographic information on the coast, and the intersection between topographic information and simplified trajectories was efficiently computed using the PMR quadtree. To verify the effectiveness of the proposed algorithm, experiments were conducted on real-world trajectories in the Korean sea. The proposed algorithm yielded simplified trajectories with no intersections of the trajectory and obstacle. In addition, the computational efficiency of the proposed algorithm with the PMR quadtree was superior to that without the PMR quadtree.

Ship collision risk assessment has advanced over recent years, enhancing maritime safety. However, existing studies often describe ship domains and collision risk assessments in a static manner, lacking interactivity. Interactive visualization of collision risk, especially in multi-ship scenarios has not been sufficiently developed. This gap prompted the development of “FURIOUS: Fully Unified Risk-assessment with Interactive Operational User System for vessels.” This tool aids in visualizing and analyzing collision risk of multi-ship encounter situation through real-time visualization. Our system processes data from Automatic Identification System (AIS). The system performs ship domain calculations and collision risk assessments supported by geographical computations, and includes features like real-time vessel display and collision type detection. Interactive and user-selectable elements, along with dynamic maps enhance real-time decision-making to ensure navigation safety. Additionally, the system aids both experienced and novice users in understanding complicated maritime dynamic environments. Users can adjust parameters like ship type, ship IDs, time window and map type for tailored analyses and proactive collision avoidance. We conducted a user study to validate these features, confirming that they effectively improve situational awareness and enhance decision-making capabilities in real-world scenarios. This paper details the design, implementation, and evaluation of this tool, highlighting its potential to transform maritime decision-making by improving situational awareness and enhancing operational efficiency.

Increased grain yield will be critical to meet the growing demand for food, and could be achieved by delaying crop senescence. Here, via quantitative trait locus (QTL) mapping, we uncover the genetic basis underlying distinct life cycles and senescence patterns of two rice subspecies, indica and japonica . Promoter variations in the Stay-Green ( OsSGR ) gene encoding the chlorophyll-degrading Mg ++ -dechelatase were found to trigger higher and earlier induction of OsSGR in indica , which accelerated senescence of indica rice cultivars. The indica -type promoter is present in a progenitor subspecies O. nivara and thus was acquired early during the evolution of rapid cycling trait in rice subspecies. Japonica OsSGR alleles introgressed into indica -type cultivars in Korean rice fields lead to delayed senescence, with increased grain yield and enhanced photosynthetic competence. Taken together, these data establish that naturally occurring OsSGR promoter and related lifespan variations can be exploited in breeding programs to augment rice yield. Breeding crops with delayed senescence could plausibly increase grain yield. Here the authors show that variation at the rice SGR locus contributes to differences in senescence between indica and japonica subspecies and show that introgression can increase yield in an elite indica rice variety.

The geometric correction of thermal infrared (TIR) orthophotos generated by unmanned aerial vehicles (UAVs) presents significant challenges due to low resolution and the difficulty of identifying ground control points (GCPs). This study addresses the limitations of real-time kinematic (RTK) UAV data acquisition, such as network instability and the inability to detect GCPs in TIR images, by proposing a method that utilizes RGB orthophotos as a reference for geometric correction. The accelerated-KAZE (AKAZE) method was applied to extract feature points between RGB and TIR orthophotos, integrating binary descriptors and absolute coordinate-based matching techniques. Geometric correction results demonstrated a significant improvement in regions with stable and changing environmental conditions. Invariant regions exhibited an accuracy of 0.7~2 px (0.01~0.04), while areas with temporal and spatial changes saw corrections within 5~7 px (0.10~0.14 m). This method reduces reliance on GCP measurements and provides an effective supplementary technique for cases where GCP detection is limited or unavailable. Additionally, this approach enhances time and economic efficiency, offering a reliable alternative for precise orthophoto generation across various sensor data.

Engineered two-phase microfluidic systems have recently shown promise for computation, encryption, and biological processing. For many of these systems, complex control of dispersed-phase frequency and switching is enabled by nonlinearities associated with interfacial stresses. Introducing nonlinearity associated with fluid inertia has recently been identified as an easy to implement strategy to control two-phase (solid-liquid) microscale flows. By taking advantage of inertial effects we demonstrate controllable self-assembling particle systems, uncover dynamics suggesting a unique mechanism of dynamic self-assembly, and establish a framework for engineering microfluidic structures with the possibility of spatial frequency filtering. Focusing on the dynamics of the particle—particle interactions reveals a mechanism for the dynamic self-assembly process; inertial lift forces and a parabolic flow field act together to stabilize interparticle spacings that otherwise would diverge to infinity due to viscous disturbance flows. The interplay of the repulsive viscous interaction and inertial lift also allow us to design and implement microfluidic structures that irreversibly change interparticle spacing, similar to a low-pass filter. Although often not considered at the microscale, nonlinearity due to inertia can provide a platform for high-throughput passive control of particle positions in all directions, which will be useful for applications in flow cytometry, tissue engineering, and metamaterial synthesis.

Existing studies on reducing urban heat island phenomenon and building temperature have been actively conducted; however, studies on investigating the warm roof phenomenon to in-crease the temperature of buildings are insufficient. A cool roof is required in a high-temperature region, while a warm roof is needed in a low-temperature or cold region. Therefore, a warm roof evaluation was conducted in this study using the roof color (black, blue, green, gray, and white), which is relatively easier to install and maintain compared to conventional insulation materials and double walls. A remote sensing method via an unmanned aerial vehicle (UAV)-mounted thermal infrared (TIR) camera was employed. For warm roof evaluation, the accuracy of the TIR camera was verified by comparing it with a laser thermometer, and the correlation between the surface temperature and the room temperature was also confirmed using Pearson correlation. The results showed significant surface temperature differences ranging from 8 °C to 28 °C between the black-colored roof and the other colored roofs and indoor temperature differences from 1 °C to 7 °C. Through this study, it was possible to know the most effective color for a warm roof according to the color differences. This study gave us an idea of which color would work best for a warm roof, as well as the temperature differences from other colors. We believe that the results of this study will be helpful in heating load research, providing an objective basis for determining whether a warm roof is applied.

Abstract Maritime Autonomous Surface Ships are in the development stage and they play an important role in the upcoming future. Present generation ships are semi-autonomous and controlled by the ship crew. The performance of the ship is predicted using the data collected from the ship with the help of machine learning and deep learning methods. Path planning for an autonomous ship is necessary for estimating the best possible route with minimum travel time and it depends on the weather. However, even during the navigation, there will be changes in weather and it should be predicted in order to reroute the ship. The weather information such as wave height, wave period, seawater temperature, humidity, atmospheric pressure, etc., is collected by ship external sensors, weather stations, buoys, and satellites. This paper investigates the ensemble machine learning approaches and seasonality approach for wave data prediction. The historical meteorological data are collected from six stations near Puerto Rico offshore and Hawaii offshore. We explore ensemble machine learning techniques on the data collected. The collected data are divided into training and testing data and apply machine learning models to predict the test data. The hyperparameter optimization is performed to find the best parameters before fitting on train data, this is essential to find the best results. Multivariate analysis is performed with all the methods and errors are computed to find the best models. Graphical Abstract Graphical Abstract

Digital twin (DT) technology has garnered increasing attention across various sectors, particularly in the construction and road infrastructure domains. To fully realize its potential and systematically apply it in practice, adherence to a formalized approach is necessary. However, numerous DT-related standards and models currently exist, creating uncertainty in the selection of appropriate frameworks. Moreover, no widely accepted standard or reference model has yet been developed in the field of road infrastructure management. Therefore, this study examined the current standards and models employed in the adoption and implementation of DTs in road infrastructure management, focusing on their dimensions (layers) and functional components. A bottom-up approach was adopted by comprehensively reviewing the existing literature on road networks, bridges, tunnels, and other civil infrastructures and urban DTs. Ultimately, a DT framework was developed, comprising five core layers with their respective components and functionalities, to facilitate network-level integrated road infrastructure management. Moreover, the proposed framework’s implementation scenario enhances its applicability in the field. Overall, this study provides valuable insights for researchers and practitioners involved in DT implementation in infrastructure management and supports future standardization efforts in this domain.

This study utilizes Thermal Infrared (TIR) imaging technology to detect hotspots in photovoltaic (PV) modules of solar power plants. Unmanned aerial vehicle (UAV)-based TIR imagery is crucial for efficiently analyzing fault detection in solar power plants. This research explores optimal operational parameters for generating high-quality TIR images using UAV technology. In addition to existing variables such as humidity, emissivity, height, wind speed, irradiance, and ambient temperature, newly considered variables including the angle of incidence between the target object and the thermal infrared camera are analyzed for their impact on TIR images. Based on the solar power plant’s tilt (20°) and the location coordinate data of the hotspot modules, the inner and outer products of the vectors were used to obtain the normal vector and angle of incidence of the solar power plant. It was discovered that the difference between measured TIR temperature data and Land Surface Temperature (LST) data varies with changes in the angle of incidence. The analysis presented in this study was conducted using multiple regression analysis to explore the relationships between dependent and independent variables. The Ordinary Least Squares (OLS) regression model employed was able to explain 63.6% of the variability in the dependent variable. Further, the use of the Condition Number (Cond. No.) and the Variance Inflation Factor (VIF) revealed that the multicollinearity among all variables was below 10, ensuring that the independence among variables was well-preserved while maintaining statistically significant correlations. Furthermore, a positive correlation was observed with the actual measured temperature values, while a negative correlation was observed between the TIR image data values and the angle of incidence. Moreover, it was found that an angle of incidence between 15° and 20° yields the closest similarity to LST temperature data. In conclusion, our research emphasizes the importance of adjusting the angle of incidence to 15–20° to enhance the accuracy of TIR imaging by mitigating overestimated TIR temperature values.

Dementia has become a profound public health problem due to the number of patients increasing every year. Previous studies have reported that environmental factors, including greenness, may influence the development and progression of dementia. Studies have found that exposure to green space is associated with a lower incidence of dementia. However, many definitions of green space exist, and the effects of its use may differ with the type of green space. Therefore, two types of green space measures were considered in this study to assess the differences in their impact on the prevalence of dementia among females and males. This study used five years of data (2017–2021) from 235 districts in South Korea. The two green space measures used were open space density and normalized difference vegetation index (NDVI), which were derived from satellite images. The analysis utilized a combination of traditional and spatial panel analyses to account for the spatial and temporal effects of independent variables on dementia prevalence. The spatial autocorrelation results revealed that both measures of greenness were spatially correlated with dementia prevalence. The spatial panel regression results revealed a significant positive association between NDVI and dementia prevalence, and open space had a negative association with dementia prevalence in both genders. The difference in the findings can serve as the basis for further research when choosing a greenspace measure, as it affects the analysis results, depending on the objective of the study. This study adds to the knowledge regarding improving dementia studies and the application of spatial panel analysis in epidemiological studies.