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This paper reports a feasible alternative to compile a landslide inventory map (LIM) from remote sensing datasets using the application of an artificial intelligence–driven methodology. A deep convolutional neural network model, called LanDCNN, was developed to generate segmentation maps of landslides, and its performance was compared with the benchmark model, named U-Net, and other conventional object-based methods. The landslides that occurred in Lantau Island, Hong Kong, were taken as the case study, in which the pre- and post-landslide aerial images, and a rasterized digital terrain model (DTM) were used. The assessment reveals that LanDCNN trained with bitemporal images and DTM yields the smoothest and most semantically meaningfully LIM, compared to other methods. This LIM is the most balanced segmentation results, represented by the highest F1 measure among all analyzed cases. With the encoding capability of LanDCNN, the application of DTM as the input renders better LIM production, especially when the landslide signatures are relatively subtle. With the computational setup used in this study, LanDCNN requires ~ 3 min to map landslides from the datasets of approximately 25 km2 in area and with a resolution of 0.5 m. In short, the proposed landslide mapping framework, featured LanDCNN, is scalable to handle the vast amount of remote sensing data from different types of measurements within a short processing period.

This paper reports the use of the deep learning-based technique to characterize the particle orientation of clay samples. The U-Net model was applied to perform semantic segmentation for identifying individual kaolinite particles, based on the scanning electron microscopic images taken from clay samples subjected to 1-D consolidation. The measurable elongated particles were manually annotated to facilitate the supervised learning. A fivefold cross-validation was used to ensure satisfactory generalization of the deep learning models. Dice loss and weighted cross-entropy were chosen as the loss functions to tackle the issue of imbalanced classification class. The customized weight maps incorporated in the weight cross-entropy were found effective in forcing the deep learning models to learn how to recognize the particle boundaries. With the trained deep learning models, the measurable elongated kaolinite particles were identified from the ~ 1280 patches within ~ 20 min and the particle directional distribution was quantified using the fabric tensor. The characterization results reveal that the kaolinite particles exhibit a tendency to gradually align along the horizontal plane as imposed by the applied vertical stress. In short, the proposed deep learning-based technique is potential to automate the laborious and visually intensive conventional labeling tasks in fabric characterization of clay samples.

The primary clientele of a harbor is vessels, and vessels are primarily influenced by external forces such as wind (on the water surface), currents (underwater), and waves (affecting vessel stability). Therefore, it is necessary to comprehensively consider safety factors such as marine environmental forces and port characteristics. As ship sailing falls under applied science, acquiring marine meteorological information regarding ship routes can enhance port navigational safety. However, in the face of changes in the environmental conditions of harbor waters, it is essential to fully consider the impact of the external environment on ship maneuvering. One can effectively navigate complex operating environments by devising reasonable ship-handling plans. In the context of sea level rise caused by extreme climatic events, long-term variations, trends, and random factors are at play. Previous assessments of sea level rise have often relied on linear regression and the least squares method to determine coefficients. However, these methods fail to accurately capture the actual trend of sea level rise. Additionally, traditional harmonic analysis methods are unable to analyze sea level rise as well. Therefore, in this study, the techniques of simple moving average (SMA), empirical mode decomposition (EMD), and ensemble empirical mode decomposition (EEMD) were applied to analyze sea level rise. The obtained results of sea level rise under different analysis conditions were integrated with a hydrodynamic model that incorporates both wave and tidal characteristics to calculate the overall coastal dynamics parameters, which are crucial for ship navigation. The research findings contribute to the study of ship navigational safety issues by examining the distribution characteristics of port meteorology under climate change conditions. They offer valuable insights for mariners to assess navigational safety and devise maneuvering strategies based on the actual water flow conditions. Furthermore, the findings help identify and address potential risks and issues, ultimately ensuring the safety of navigation.

Understanding the soil responses during monotonic jacking can help improve the estimation of axial capacity in pile foundation design. To examine the soil responses in terms of strain and stress distributions, stress paths, the movement of particles and contact force mobilization during monotonic jacking, a three-dimensional simulation of a centrifuge model pile test was performed using the discrete element method. The simulation results show great resemblance to the measurements from the centrifuge model pile test. The distribution of incremental deviatoric strain d ε q indicates the formation of a shear band in the shoulder of the ‘nose cone’ that is beneath the pile base. Based on this, the soil mass can be divided into three zones, i.e., the ‘nose cone’ (zone I), the shear band (zone II) and the surrounding soil beyond the shear band (zone III). The distributions of the radial stress σ r ′ , hoop stress σ θ ′ , vertical stress σ v ′ and shear stress τ r v ′ are in different modes and high-gradient distributions of mean stress p ′ and deviatoric stress q are found in zone II. As for the stress path, the surrounding soil experiences four phases in terms of changes in p ′ and q during monotonic jacking, and the change in the soil stress state decreases with increasing distance from the pile centerline. From a microscopic point of view, the particles in the shear band rotate strongly in a similar direction, fully mobilizing the frictions at the associated contacts. This provides a micromechanical explanation for the soil failure during monotonic jacking.

Automatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.

Due to the rapid development of computers, researchers have made efforts since the 1990s to develop typhoon forecasting models and stochastic typhoon simulation models to assess typhoon disasters and risks. Typhoon forecasting models are primarily used to predict and track the movement of typhoons and provide warning information to the general public before landfall. Stochastic typhoon simulation models can assess extreme wind speeds and compensate for the limitations of current observations and simulation data length. Taiwan experiences approximately three to four typhoons yearly, of varying intensities and paths. Whether the marine meteorological data includes events of strong typhoon centers passing through will affect the results of frequency analysis. The development of offshore wind power in Taiwan is closely related to the unique marine meteorological conditions throughout the lifecycle stages, including wind farm site selection, feasibility studies, planning and design, construction and installation, operation and maintenance, and decommissioning. This study references relevant research and analyzes sixty-three scenarios using nine types of maximum storm wind speed radii and seven Holland-B parameters. The data from Japan Meteorological Agency Best Track Data (JMA BTD) is utilized, explicitly selecting 20 typhoon events after 2000 for wind speed simulation using a typhoon wind speed model. After validating the typhoon wind speeds with observation data from the Central Weather Bureau (CWB) in Hsinchu and the Longdong buoy, the technique of Monte Carlo simulation is utilized to generate synthetic typhoons randomly. The average of the relative absolute errors for the simulated maximum wind speeds is calculated, and through comprehensive evaluation, optimal parameter combinations (Rm, B) are obtained.

In this study, the U-oedometer, a novel modified oedometer cell equipped with tailor-made needle probes, is developed to easily and accurately measure the excess pore water pressure (Δu) during 1D consolidation tests and to determine the coefficient of consolidation (cv). The 3D printing technique is applied to make simple yet robust modifications to the conventional oedometer cell for facilitating the installation of the needle probes. The tailor-made needle probes are designed in such a way that the volumetric compliance is lowered to avoid measurement bias. Subsequently, the Δu-based method is proposed to determine cv, with the target of avoiding the intervention of human judgement and therefore minimizing the degree of subjectivity. The experimental results demonstrate that the measured Δu matches the theoretical values of the Terzaghi 1D consolidation theory, showing that the estimated cv is sufficiently reliable. In addition to the determination of cv, the U-oedometer allows additional measurements of other soil properties during consolidation, including the coefficient of permeability (k) and the coefficient of earth pressure at rest (K0). It is observed that k decreases with the reduction in void volume, due to the increase in the effective vertical stress (σv′). Further, the secondary compression seems to be a continuation of the primary consolidation, where the soil sample continues to deform at a relatively slower rate, associated with the slight decrease in k. A constant value of K0 is observed at any value of σv′ in the loading path, while during secondary compression, K0 slightly increases with time.

In this study, a hydrodynamic model was used that includes the effects of wave–current interactions to simulate the wave and current patterns before and after offshore wind turbine installation in western Taiwan. By simulating the waves and currents after the offshore wind turbine was established, the waves and currents caused by the wind turbine were seen to have a limited range of influence, which is probably within an area about four to five times the size of the diameter (12–15 m) of the foundation structure. Overall, the analysis of the simulation results of the wave and current patterns after the offshore wind turbines were established shows that the underwater foundation only affected the local area near the pile structure. The wind farm (code E) of the research case can be equipped with about 720 cage cultures; if this is extended to other wind farms in the western sea area, it should be possible to produce economic-scale farming operations such as offshore wind power and fisheries. However, this study did not consider the future operation of the entire offshore wind farm. If the operation and maintenance of offshore wind farms are not affected, and if the consent of the developer is obtained, it should be possible to use this method to provide economically large-scale farming areas as a mutually beneficial method for offshore wind power generation and fisheries.

Porous silicon is of current interest for cardiac tissue engineering applications. While porous silicon is considered to be a biocompatible material, it is important to assess whether post-etching surface treatments can further improve biocompatibility and perhaps modify cellular behavior in desirable ways. In this work, porous silicon was formed by electrochemically etching with hydrofluoric acid, and was then treated with oxygen plasma or supercritical carbon dioxide (scCO2). These processes yielded porous silicon with a thickness of around 4 μm. The different post-etch treatments gave surfaces that differed greatly in hydrophilicity: oxygen plasma-treated porous silicon had a highly hydrophilic surface, while scCO2 gave a more hydrophobic surface. The viabilities of H9c2 cardiomyocytes grown on etched surfaces with and without these two post-etch treatments was examined; viability was found to be highest on porous silicon treated with scCO2. Most significantly, the expression of some key genes in the angiogenesis pathway was strongly elevated in cells grown on the scCO2-treated porous silicon, compared to cells grown on the untreated or plasma-treated porous silicon. In addition, the expression of several apoptosis genes were suppressed, relative to the untreated or plasma-treated surfaces.

The objective of this study was to adequately examine potential wave fields, flow fields, and suspended load changes in different wind turbine foundations. Accordingly, this study applied the hydrodynamic model to simulate waves, currents, and suspended load in the study area. The simulation results are based on the assumption that dredging and rubble bed trimming were performed for 8 h and that the per foundation setting operation was completed in 2 h. The influence on the tripile and jacket was larger than that on the monopile, and the influence time was longer. However, due to the influence of tidal currents on the sea, the suspended load also became more acceptable than the initial concentration. From a macroscopic perspective, the different foundations did not sufficiently affect the study area. From a microscopic perspective, changes in the suspended load were only limited to areas surrounding the piles after the installation of the wind turbines.