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Wet walls in ancient masonry buildings are common due to rainfall, groundwater and other environmental factors, and usually accompanied by the degradation phenomena such as powdering, shedding, salting out, which threatens the value and safety of ancient buildings. It is found that the bottom of Dayan Pagoda has been damp and gloomy for a long time. In recent 40 years, the waterline at the bottom has risen by about 0.5 ∼1m, and the wet area on the north wall is as high as 5m. This paper aims to clarify the cause of the rising waterline and the water source of Dayan Pagoda. The correlation between degradation of Dayan pagoda and environmental factors such as temperature, rainfall and solar radiation was established. Firstly, there was a field measurement on the waterline height in different orientations of Dayan Tower; Then, a coupled heat and moisture transfer model was developed to obtain the hygrothermal distribution in the brick wall, and the impact of rainfall and groundwater was evaluated, and the rising trend of waterline in the future was predicted. The field measured results show that during 2018-2020, the waterline of the west wall of Dayan Pagoda has the largest rise (about 20cm), while the south wall has the smallest rise (about 10cm). The simulation results show that the water source of rising waterline is mainly wind-driven rain rather than groundwater rising. The result contributes to propose environmental control measures to alleviate the degradation of masonry buildings caused by water.

Watershed is a widely used image segmentation algorithm. Most researchers understand just an idea of this method: a grayscale image is considered as topographic relief, which is flooded from initial basins. However, frequently they are not aware of the options of the algorithm and the peculiarities of its realizations. There are many watershed implementations in software packages and products. Even if these packages are based on the identical algorithm–watershed, by flooding their outcomes, processing speed, and consumed memory, vary greatly. In particular, the difference among various implementations is noticeable for huge volumetric images; for instance, tomographic 3D images, for which low performance and high memory requirements of watershed might be bottlenecks. In our review, we discuss the peculiarities of algorithms with and without waterline generation, the impact of connectivity type and relief quantization level on the result, approaches for parallelization, as well as other method options. We present detailed benchmarking of seven open-source and three commercial software implementations of marker-controlled watershed for semantic or instance segmentation. We compare those software packages for one synthetic and two natural volumetric images. The aim of the review is to provide information and advice for practitioners to select the appropriate version of watershed for their problem solving. In addition, we forecast future directions of software development for 3D image segmentation by watershed.

Hydrogen (2H/1H) and oxygen (18O/16O) isotopic ratios were measured in precipitation (900 samples) collected from several locations in India during the period 2003–2006 (12 locations in 2003 and 18 locations in 2004–2006). The amount of rainfall along with air temperature and humidity were also measured. The meteoric water line developed for India using isotopic data of precipitation samples, namely, δ2H = 7.93(±0.06) × δ18O + 9.94(±0.51) (n = 272, r2 = 0.98), differs slightly from the global meteoric water line. Regional meteoric water lines were developed for several Indian regions (i.e., northern and southern regions of India, western Himalayas) and found to be different from each other (southern Indian meteoric water line, slope is 7.82, intercept or D excess is 10.23; northern Indian meteoric water line, slope is 8.15, intercept is 9.55) which is attributed to differences in their geographic and meteorological conditions and their associated atmospheric processes (i.e., ambient temperature, humidity, organ, and source of vapor masses). The local meteoric water lines developed for a number of locations show wide variations in the slope and intercept. These variations are due to different vapor sources such as the northeast (NE) monsoon that originates in the Bay of Bengal; the southwest monsoon (SW) that originates in the Arabian Sea; a mixture of NE and SW monsoons; retreat of NE and SW monsoons and western disturbances that originate in the Mediterranean Sea. The altitude effect in the isotopic composition of precipitation estimated for western Himalayan region also varies from month to month.

In the maritime industry, accurately detecting a ship’s draft line is crucial for ensuring transaction fairness and navigational safety. Existing deep learning-based methods for draft line detection primarily use segmentation techniques to segment the entire body of water before determining the waterline. These approaches incur high computational costs and often face challenges under varying environmental conditions, such as lighting changes and different hull colors. To address these issues, we propose multi-scale feature fusion keypoint detection network (MFFKD) for precise and efficient ship draft line detection. Our network integrates four stages of Dilated Residual-Channel Recalibration Module (DR-CRM) blocks to extract multi-scale features. Meanwhile, the Feature Enhancement Extraction Modules (FEEM) are employed to enhance these extracted features, and the Multi-scale Feature Weighted Integration (MFWI) module efficiently fuses the enhanced multi-scale features. Furthermore, a task head for keypoint prediction is designed to ensure accurate localization of keypoints. By integrating the predicted keypoint data with mark information detected by the character recognition head through a mathematical model, we achieve precise predictions of waterline readings. To enhance the model’s adaptability to various environmental conditions, we adopt a dual-phase training strategy: an initial pre-training phase for learning general ship features and waterline characteristics, followed by a fine-tuning phase using data from diverse scenes. Extensive experimental results show that our method surpasses the baseline models in waterline detection accuracy. In terms of model execution speed, our method exceeds the advanced segmentation-based approaches. These demonstrate the effectiveness of integrating keypoint detection with dual-phase training in ship waterline detection.

Accurately mapping the boundary between land and water (the ‘waterline’) is critical for tracking change in vulnerable coastal zones, and managing increasingly threatened water resources. Previous studies have largely relied on mapping waterlines at the pixel scale, or employed computationally intensive sub-pixel waterline extraction methods that are impractical to implement at scale. There is a pressing need for operational methods for extracting information from freely available medium resolution satellite imagery at spatial scales relevant to coastal and environmental management. In this study, we present a comprehensive evaluation of a promising method for mapping waterlines at sub-pixel accuracy from satellite remote sensing data. By combining a synthetic landscape approach with high resolution WorldView-2 satellite imagery, it was possible to rapidly assess the performance of the method across multiple coastal environments with contrasting spectral characteristics (sandy beaches, artificial shorelines, rocky shorelines, wetland vegetation and tidal mudflats), and under a range of water indices (Normalised Difference Water Index, Modified Normalised Difference Water Index, and the Automated Water Extraction Index) and thresholding approaches (optimal, zero and automated Otsu’s method). The sub-pixel extraction method shows a strong ability to reproduce both absolute waterline positions and relative shape at a resolution that far exceeds that of traditional whole-pixel methods, particularly in environments without extreme contrast between the water and land (e.g., accuracies of up to 1.50–3.28 m at 30 m Landsat resolution using optimal water index thresholds). We discuss key challenges and limitations associated with selecting appropriate water indices and thresholds for sub-pixel waterline extraction, and suggest future directions for improving the accuracy and reliability of extracted waterlines. The sub-pixel waterline extraction method has a low computational overhead and is made available as an open-source tool, making it suitable for operational continental-scale or full time-depth analyses aimed at accurately mapping and monitoring dynamic waterlines through time and space.

Backgrounds Numerous studies have shown that dental unit water lines (DUWLs) are often contaminated by a wide range of micro-organisms (bacteria, fungi, protozoa) and various prevalence have been reported for it in previous studies. Therefore, this review study aims to describe the prevalence of bacterial biofilm contamination of DUWLs. Methods This is a systematic review and meta-analysis in which the related keywords in different international databases, including Medline (via PubMed ) and Scopus were searched. The retrieved studies were screened and the required data were extracted from the included studies. Three standard methods including American Dental Association (ADA), The Center for Disease Control and Prevention (CDC) and contaminated > 100 CFU/ml( C-100 ) standards were used to assess the bacterial biofilm contamination of DUWLs. All studies that calculated the prevalence of bacterial biofilm contamination of DUWLs, and English full-text studies were included in the meta-analysis. Studies that did not have relevant data or used unusual laboratory methods were excluded. Methodological risk of bias was assessed by a related checklist and finally, the data were pooled by fixed or random-effect models. Results Seven hundred and thirty-six studies were identified and screened and 26 related studies were included in the meta-analysis. The oldest included study was published in 1976 and the most recent study was published in 2020. According to the ADA, CDC and C-100 standards, the prevalence of bacterial contamination was estimated to be 85.0% ( 95% confidence interval (CI) : 66.0–94.0%), 77.0% ( 95%CI : 66.0–85.0%) and 69.0% ( 95%CI : 67.0–71.0%), respectively. The prevalence of Legionella Pneumophila and Pseudomonas Aeruginosa in DUWLs was estimated to be 12.0% ( 95%CI : 10.0–14.0%) and 8.0% ( 95%CI : 2.0–24.0%), respectively. Conclusion The results of this review study suggested a high prevalence of bacterial biofilm in DUWLs; therefore, the use of appropriate disinfecting protocol is recommended to reduce the prevalence of contamination and reduce the probable cross-infection.

For many remote sensing applications, the instantaneous waterline on the image is critical boundary information to separate land and water and for other purposes. Accurate waterline extraction from satellite images is a desirable feature in such applications. Due to the complex topography of low tidal flats and their indistinct spatial and spectral characteristics on satellite imagery, the waterline extraction for tidal flats (especially at low tides) from remote sensing images has always been a technically challenging problem. We developed a novel method to extract waterline from satellite images, assuming that the waterline’s elevation is level. This paper explores the utilization of bathymetry during waterline extraction and presents a novel approach to tackle the waterline extraction issue, especially for low tidal flats, using remote sensing images at mid/high tide, when most of the tidal flat area is filled with seawater. Repeated optical satellite images are easily accessible in the current days; the proposed approach first generates the bathymetry map using the mid/high-tide satellite image, and then the initial waterline is extracted using traditional methods from the low-tide satellite image; the isobath (depth contour lines of bathymetry), which corresponds to the initial waterline is robustly estimated, and finally an area-based optimization algorithm is proposed and applied to both isobath and initial waterline to obtain the final optimized waterline. A series of experiments using Sentinel-2 multispectral images are conducted on Jibei Island of Penghu Archipelago and Chongming Island to demonstrate this proposed strategy. The results from the proposed approach are compared with the Normalized Difference Water Index (NDWI) and Support Vector Machine (SVM) methods. The results indicate that more accurate waterlines can be extracted using the proposed approach, and it is very suitable for waterline extraction for tidal flats, especially at low tides.

Beach morphology can be observed over large spatio-temporal scales, and future shoreline positions can be predicted and coastal risk indicators can be derived by measuring satellite-derived instantaneous waterlines. Long-term satellite missions, such as Landsat and Sentinel-2, provide decades of freely available, high-resolution optical measurement datasets, enabling large-scale data collection and relatively high-frequency monitoring of sandy beaches. Satellite-Derived Shoreline (SDS) extraction methods are emerging and are increasingly being applied over large spatio-temporal scales. SDS generally consists of two steps: a mathematical relationship is applied to obtain a ratio index or pixel classification by machine-learning algorithms, and the land/sea boundary is then determined by edge detection. Indexes from lake waterline detection, such as AWEI or NDWI, are often transferred towards the shore without taking into account that these indexes are inherently affected by wave breaking. This can be overcome by using pixel classification to filter the indices, but this comes at a computational cost. In this paper, we carry out a thorough evaluation of the relationship between scene-dependent variables and waterline extraction accuracy, as well as a robust and efficient thresholding method for coastal land–water classification that optimises the index to satellite radiometry. The method developed for sandy beaches combines a new purpose-built multispectral index (SCoWI) with a refinement method of Otsu’s threshold to derive sub-pixel waterline positions. Secondly, we present a waterline extraction pipeline, called Shoreliner, which combines the SCoWI index and the extraction steps to produce standardised outputs. Implemented on the CNES High Performance Cluster (HPC), Shoreliner has been quantitatively validated at Duck, NC, USA, using simultaneous Sentinel-2 acquisitions and in situ beach surveys over a 3-year period. Out of six dates that have a satellite acquisition and an in situ survey, five dates have a sub-pixel RMS error of less than 10 m. This sub-pixel performance of the extraction processing demonstrates the ability of the proposed SDS extraction method to extract reliable, instantaneous and stable waterlines. In addition, preliminary work demonstrates the transferability of the method, initially developed for Sentinel-2 Level1C imagery, to Landsat imagery. When evaluated at Duck on the same day, Sentinel-2 and Landsat imagery several minutes apart provide similar results for the detected waterline, within the method’s precision. Future work includes global validation using Landsat’s 40 years of data in combination with the higher resolution Sentinel-2 data at different locations around the world.

Purpose The aim of this paper is to construct a device that simulated the seawater splash zone, dynamic waterline zone (splash zone) and full immersion zone. Localized corrosion of 2A12 aluminum alloy long-scale specimen was studied. Design/methodology/approach Corrosion morphologies before and after the corrosion product removal were used to identify corrosion intensity at full seawater immersion zone, dynamic waterline zone (splash zone) and atmospheric zone. The average depth and diameter of corrosion pits in the three zones were evaluated by three-dimensional optical microscopy. The impact of wetting time of the atmospheric zone on the localized corrosion was investigated. Findings Corrosion pits were observed on the surface on day 4 for the wetted atmospheric zone (Case 1), and on the surface on day 8 for the alternant wet/dry atmospheric zone (Case 2). The corrosion product layer on the surface for Case 1 was partially broken down while the layer on the surface for Case 2 was intact. Average pitting depth and pitting diameters for Case 1 were more serious than that for Case 2. Originality/value The above findings revealed that the humidity of the atmospheric zone had great impact on the localized corrosion of aluminum alloy at the seawater splash zone.

Sandy coastlines are very dynamic spaces affected by a variety of natural and human factors. In Central Chile, changes in oceanographic and wave conditions, modes of inter-annual climate variability such as El Niño Southern Oscillation (ENSO), and extreme events such as earthquakes and tsunamis condition the beach morphology. At the same time, direct human actions alter the arrival of sediments to the coast and their alongshore distribution. Despite the relevance of the beaches for this coastal region and the interesting relationship their morphology has with the aforementioned factors, there is a lack of robust morphological datasets to provide a deep characterization and understanding of the dynamism of the Chilean coast. Based on the information provided by satellite-derived shorelines (SDSs) defined by using the SHOREX algorithm, this paper characterizes the morphological changes of Playa Grande in Cartagena Bay (Central Chile) during the period 1985–2019. The shoreline position data are analyzed in the context of changing beach transforming elements, allowing for a better understanding of the changes according to multiple drivers. While some of these factors, such as earthquakes or coastal storms, have a punctual character, changes in wave patterns vary at different time scales, from seasonal to multi-annual, linked to climate phases such as ENSO. Its effects are translated into shoreline erosion and accretion conditioned by the morphology and orientation of the coast while influenced by the availability of sediment in the coastal system. According to that, a conceptual model of the dynamism and redistribution of sediment in the Bay of Cartagena is proposed. The work proves the high utility that the systematic analysis of multi-decadal SDS datasets obtained from the images acquired in the optical by the Landsat and Sentinel-2 offer for beach monitoring and understanding the coastal dynamism.