Hasil untuk "Harbors and coast protective works. Coastal engineering. Lighthouses"

Yaoshen Fan, Guangzhou Wang, Shentang Dou et al.

Sediment coarsening in submerged deltas is commonly attributed to seabed erosion because of insufficient sediment input. The Yellow River subaqueous delta (YRSD) has exhibited distinct coarsening patterns following both accretion and erosion events. To investigate these contrasting mechanisms, grain size distributions, elevation changes, and bottom shear stress patterns were analyzed across the delta from 1992 to 2022. The results revealed distinct sedimentary patterns among the abandoned YRSD, active YRSD, southern Laizhou Bay, and adjacent Bohai Sea, with average median grain size (D50) increases of 17, 17, 6, and 0 μm, respectively. Sediment coarsening occurred primarily from 1992 to 2000, when the river mouth position was artificially altered and fluvial sediment grain size increased from 16 to 29 μm. From 1992 to 2015, the active YRSD experienced accretion at a rate of 7.8 mm/yr. Moreover, the abandoned YRSD and southern Laizhou Bay experienced significant erosion. The erosion rates were −5.1 and −1.0 mm/yr, respectively. This led to the identification of two mechanisms of sediment coarsening: erosion-driven coarsening in sediment-deficient areas and accretion-driven coarsening where the input sediment grain size increased. Although marine processes did not intensify during this period, the bottom shear stress distribution changed substantially due to morphological evolution, with correlation coefficients between grain size and shear stress showing increasing trends in littoral zones. This strengthening relationship, coupled with the declining fluvial sediment load, demonstrates the YRSD transition from river-dominated to wave-dominated processes, providing important insight into delta evolution under changing sediment regimes. The insights gained can guide Yellow River Delta management through targeted strategies and provide essential evidence for predicting delta evolution.

Tushar Khankhoje, Samrat Boro, Parthasarathi Choudhury

Continuous water and sediment flow monitoring across river cross sections is essential for the management of flood- and sediment-related problems in watersheds. The sediment rating curve (SRC) estimates missing or uncertain sediment flow via its corresponding water discharge. Generally, a power form of relationship correlates the two quantities. The log-transformed water discharge and sediment discharge data were used to depict the SRCs developed in the present study. SRC parameter estimation via least squares regression using at-site dataset pairs can be found in the literature. However, the availability of reliable datasets at the site limits model applicability. This method does not describe the SRC on the basis of the continuity aspects of river system flow characteristics. Therefore, the current study proposes integrated SRC estimation models (Model 2 and Model 3) using modified Muskingum equations abiding by the spatial and temporal continuity of the entire river system state. These models are derived from streamflow storage balance criteria and ensure flow continuity norms. Moreover, Model 3 considers an inverse power form of the relationship depicting the water flow characteristics that govern the sediment transport phenomena through the river system. Standalone models for SRC parameter estimation (Model 1) were also developed for comparison among all three models via the root mean square error (RMSE), NRMSE (normalized root mean square error) and coefficient of determination (R2). The Mahanadi River system within Chhattisgarh state, India comprises five sections at tributaries, and the main channel was considered for the study. The improved NRMSE by Model 2 (7.53%) and Model 3 (7.14%) at Rajim and Model 3 (3.44%) at Bamnidhi in comparison to Model 1 at Rajim (9.19%) and Bamnidhi (4.80%) encouraged the application of integrated models for SRC estimation in river systems. Moreover, Model 3 outperformed Model 2 in some cases where the sediment transport process may be governed by water flow characteristics.

Muneera Bano, Hashini Gunatilake, Rashina Hoda

Large language models (LLMs) have rapidly gained popularity and are being embedded into professional applications due to their capabilities in generating human-like content. However, unquestioned reliance on their outputs and recommendations can be problematic as LLMs can reinforce societal biases and stereotypes. This study investigates how LLMs, specifically OpenAI's GPT-4 and Microsoft Copilot, can reinforce gender and racial stereotypes within the software engineering (SE) profession through both textual and graphical outputs. We used each LLM to generate 300 profiles, consisting of 100 gender-based and 50 gender-neutral profiles, for a recruitment scenario in SE roles. Recommendations were generated for each profile and evaluated against the job requirements for four distinct SE positions. Each LLM was asked to select the top 5 candidates and subsequently the best candidate for each role. Each LLM was also asked to generate images for the top 5 candidates, providing a dataset for analysing potential biases in both text-based selections and visual representations. Our analysis reveals that both models preferred male and Caucasian profiles, particularly for senior roles, and favoured images featuring traits such as lighter skin tones, slimmer body types, and younger appearances. These findings highlight underlying societal biases influence the outputs of LLMs, contributing to narrow, exclusionary stereotypes that can further limit diversity and perpetuate inequities in the SE field. As LLMs are increasingly adopted within SE research and professional practices, awareness of these biases is crucial to prevent the reinforcement of discriminatory norms and to ensure that AI tools are leveraged to promote an inclusive and equitable engineering culture rather than hinder it.

Emma Asbridge, Claire Krause, Richard Lucas et al.

Tropical cyclones can significantly impact mangrove forests, with some recovering rapidly, whilst others may change permanently. Inconsistent approaches to quantifying these impacts limit the capacity to identify patterns of damage and recovery across landscapes and cyclone categories. Understanding these patterns is critical as the changing frequency and intensity of cyclones and compounding effects of climate change, particularly sea-level rise, threaten mangroves and their ecosystem services. Improvements in Earth observation data, particularly satellite-based sensors and datacube environments, have enhanced capacity to classify time-series data and advanced landscape monitoring. Using the Landsat archive within Digital Earth Australia to monitor annual changes in canopy cover and extent, this study aims to quantify and classify immediate and long-term impacts of category 3–5 cyclones for mangroves in Australia. Closed canopy mangrove forests experienced the greatest immediate impact (loss of canopy cover). Most immediate impacts were minor, implying limited immediate mortality. Impacts varied spatially, reflecting proximity to exposed coastlines, cyclone track and forest structure (height, density, condition and species). Recovery was evident across all cyclones, although some areas exhibited permanent damage. Understanding the impacts and characteristics of vulnerable and resilient forests is crucial for managers tasked with protecting mangroves and their services as the climate changes.

Dong Li, Min Zhao, Hailong Zhang et al.

The inland water portion of the carbon cycle is an essential component of the global carbon cycle and is a promising direction to seek missing carbon sinks. Inland waters fix inorganic carbon to form autochthonous organic carbon (Auto-OC) and accept laterally transferred terrestrial OC. Calculating the carbon sink flux of inland water requires a quantitative estimation of the proportion of the aforementioned processes. In the current study, n-alkanes are used as biomarkers and the dual carbon isotope method (Bayesian mixing model) is applied to estimate the proportions of Auto-OC in the sediment of a simulation site comprising five shallow submerged macrophyte-dominated subsystems. The study results showed that a high proportion of Auto-OC was present in all sediment, regardless of the season or subsystem. However, the proportions were higher in the warm-humid season than in the cold-dry season. Results from a correlation analysis showed that temperature-controlled seasonal variations in the photosynthetic strength of aquatic organisms are the most likely cause of this difference. The average deposition rates of total organic carbon and Auto-OC were high (66.7 and 58.2 g C/m2/yr, respectively). Throughout the year, the weighted average percentage of Auto-OC ranged from 76% to 90%, with a mean value of 86% in the five aquatic subsystems. Establishing and maintaining submerged macrophyte-dominated systems have a potential of decreasing carbon dioxide (CO2) evasion and sequestrating more carbon (C) in headwaters. Working for clear submerged macrophyte-dominated lakes is beneficial for increasing carbon sinks.

Dawid Aleksander Szatten, Oleksandr Obodovskyi, Marta Brzezińska

The stability of river channels results from the impact of spatially diversified natural characteristics of the catchment, which are additionally intensified by the pressure of human activities. The aim of the current study was the overall assessment of the riverbed stability in the Brda River catchment (Poland) in the two periods 1980–1989 and 1991–2018. The study area is characterized by a high discharge regularity, resulting from the river-lake system in the upper part of the catchment, and strong human pressure caused by the presence of hydrotechnical structures in the middle and lower parts of the catchment. The hydrological, sedimentological, and land cover archival data were used in the current study. Also, in the field campaign, the characteristics of the river channel were delimited. Finally, the erosive stability channel factor is proposed, reflecting the level of pressure on the fluvial system from the catchment. The results show that in the first period, there was a tendency to accumulate sediment on the riverbed, resulting in its instability. However, in the second period, the river channel was stable, and erosion and sedimentation processes did not occur. The links between the stability of the bed of the Brda River, and the temporal and area-related pressures made it possible to identify long-term trends in the degradation of the fluvial environment as a result of human activities and to indicate the directions for sustainable sediment management in the catchment.

Wayne Carpenter, Bradley Goodwiller, Daniel Wren

The National Center for Physical Acoustics (NCPA) at The University of Mississippi has developed a single-frequency acoustic attenuation system (SFAAS) to monitor the concentration of suspended fine sediments in rivers and streams. The system was operated in the Goodwin Creek Watershed in Panola County, Mississippi, USA, from November 2019 to February 2023. The system collected data when the stream stage was above 0.3 m, and physical pump samples were collected concomitantly to provide calibration data. A subset of the data, comprising 14 storm events recorded over the multiyear deployment, will be presented here to demonstrate the operation of the SFAAS and its potential to aid in hydrologic research. SFAAS was able to provide high-resolution fine sediment concentration data with a stable calibration relationship for a given hardware configuration. The data were used to investigate the behavior of fine sediment concentrations in the watershed, including hysteresis in the relationship between flow rates and sediment concentrations during streamflow hydrographs and sediment rating curves that relate stream depth to transport rates.

Diwen Huang, Yong You, Hao Sun et al.

Self-cleaning is a crucial feature of slit dams, which not only enhances upstream and downstream hydraulic connections but also automatically restores a dam's debris flow storage capacity. In this work, a series of specially designed flume tests are performed to simulate the self-cleaning process. The flow rate, relative opening, bed-slope angle, and number of openings are considered. The erosion process, topographic characteristics, and relative erosion depth are analyzed to gain insight into the self-cleaning details. The current results reveal that when the boulders jamming the openings are removed, erosion occurs in three stages (downcutting, headward erosion, and lateral erosion). Conversely, when the blockage remains stable, only surface armoring occurs. Furthermore, after the self-cleaning process reaches a quasiequilibrium state, the topographic features are summarized, and the differences in the maximum erosion depth at the opening are analyzed for different experimental conditions. This paper proposes a critical criterion (F) for the self-cleaning of slit dams on the basis of dimensional analysis. The critical criterion takes into account the interactions of three parameters (Froude number, relative opening, and opening rate) and can be conveniently applied to existing slit dams. When F is less than 0.25, the opening remains blocked, and only surface armoring occurs; when F is between 0.25 and 0.38, the blockage may be removed; and when F is greater than 0.38, self-cleaning leads to massive erosion, and the blockage is removed. Therefore, the proposed critical criterion can help design the opening dimensions of a slit dam, restoring its storage capacity. Finally, the positive effect of self-cleaning on restoring the storage capacity of slit dams is discussed.

André M. Esteves, Verônica S. Oliveira, Paulo J. P. dos Santos et al.

The patterns of meiofaunal distribution in a submarine canyon and adjacent open-slope habitats at Campos Basin, southwest Atlantic, were investigated. A total of eight stations was sampled, four inside the Canyon Almirante Câmara and four on the adjacent open slope. These stations represented four isobaths (400, 700, 1000, 1300 m) and were sampled during two distinct periods (2008, 2009). At each station, three replicates were obtained and sectioned into layers of 0–2, 2–5 and 5–10 cm. Nematoda was the most abundant group in both habitats, comprising more than 85% of the total meiofauna in both sampling periods. The density and assemblage structure of the meiofauna showed high variability between the 400 m isobath and the other three isobaths in the canyon habitat. These results reinforce the roles of habitat heterogeneity and the availability of food sources as key factors strongly influencing the deep-sea meiofauna in the southwest Atlantic Ocean. Phytopigments were significantly correlated with the two major meiofaunal groups (Nematoda and Copepoda), as well as with total meiofaunal density, only in the canyon habitat. On the adjacent open slope, only copepods showed a significant correlation with sediment characteristics (mean grain size and carbonates), suggesting that distinct environmental factors influence the distribution of meiofauna in the two habitats.

Marcus Kessel, Colin Atkinson

The ability of Generative AI (GAI) technology to automatically check, synthesize and modify software engineering artifacts promises to revolutionize all aspects of software engineering. Using GAI for software engineering tasks is consequently one of the most rapidly expanding fields of software engineering research, with over a hundred LLM-based code models having been published since 2021. However, the overwhelming majority of existing code models share a major weakness - they are exclusively trained on the syntactic facet of software, significantly lowering their trustworthiness in tasks dependent on software semantics. To address this problem, a new class of "Morescient" GAI is needed that is "aware" of (i.e., trained on) both the semantic and static facets of software. This, in turn, will require a new generation of software observation platforms capable of generating large quantities of execution observations in a structured and readily analyzable way. In this paper, we present a vision and roadmap for how such "Morescient" GAI models can be engineered, evolved and disseminated according to the principles of open science.

Yi Zheng

For controlling pollution of the marine environment while developing coastal economy, the coastal environmental performance was proposed and measured in static and dynamic methods combined with DEA and efficiency theory in this paper. With the two methods, 16 harbor cities were evaluated. The results showed the index designed in this paper can better reflect the effect to the marine environment for economy of the coastal cities.

Roberto Casadei, Gianluca Aguzzi, Giorgio Audrito et al.

Today's distributed and pervasive computing addresses large-scale cyber-physical ecosystems, characterised by dense and large networks of devices capable of computation, communication and interaction with the environment and people. While most research focusses on treating these systems as "composites" (i.e., heterogeneous functional complexes), recent developments in fields such as self-organising systems and swarm robotics have opened up a complementary perspective: treating systems as "collectives" (i.e., uniform, collaborative, and self-organising groups of entities). This article explores the motivations, state of the art, and implications of this "collective computing paradigm" in software engineering, discusses its peculiar challenges, and outlines a path for future research, touching on aspects such as macroprogramming, collective intelligence, self-adaptive middleware, learning, synthesis, and experimentation of collective behaviour.

Valerio Terragni, Annie Vella, Partha Roop et al.

A paradigm shift is underway in Software Engineering, with AI systems such as LLMs playing an increasingly important role in boosting software development productivity. This trend is anticipated to persist. In the next years, we expect a growing symbiotic partnership between human software developers and AI. The Software Engineering research community cannot afford to overlook this trend; we must address the key research challenges posed by the integration of AI into the software development process. In this paper, we present our vision of the future of software development in an AI-driven world and explore the key challenges that our research community should address to realize this vision.

Arthur Pilone, Paulo Meirelles, Fabio Kon et al.

A central challenge for ensuring the success of software projects is to assure the convergence of developers' and users' views. While the availability of large amounts of user data from social media, app store reviews, and support channels bears many benefits, it still remains unclear how software development teams can effectively use this data. We present an LLM-powered approach called DeeperMatcher that helps agile teams use crowd-based requirements engineering (CrowdRE) in their issue and task management. We are currently implementing a command-line tool that enables developers to match issues with relevant user reviews. We validated our approach on an existing English dataset from a well-known open-source project. Additionally, to check how well DeeperMatcher works for other languages, we conducted a single-case mechanism experiment alongside developers of a local project that has issues and user feedback in Brazilian Portuguese. Our preliminary analysis indicates that the accuracy of our approach is highly dependent on the text embedding method used. We discuss further refinements needed for reliable crowd-based requirements engineering with multilingual support.

Marina Potapova, Daiana Markarian, Abigail King et al.

Microscopic eukaryotes are important components of coastal wetland ecosystems. The goal of this study was to investigate the diversity of microeukaryotes in the tidal pools of a New Jersey salt marsh and to compare the assemblages of natural and artificial pools excavated for controlling mosquito populations. We evaluated microeukaryotic assemblages using the amplicon sequencing of 18S and <i>rbc</i>L DNA markers and the microscopic identification of diatoms in water and sediment samples. 18S unique amplicon sequence variants (ASV) representing ciliates, dinoflagellates, diatoms, and cercozoans were the most diverse, while the reads of dinoflagellates, diatoms, ciliates, and nematodes were the most abundant. The dominant ASVs were attributed to organisms that are characteristic of coastal plankton and sediments or those known for their resistance to salinity, desiccation, hypoxia, and UV stress. The sediment assemblages were more diverse compared to those from the water column and contained a larger portion of ASVs that were not assigned to any low-rank taxa, reflecting the current gaps in understanding the diversity of microeukaryotes. Most taxonomic groups were significantly different in their abundance and composition between natural and artificial pools. Dinoflagellates, haptophytes, chrysophytes, pelagophytes, and raphidophytes—the groups that include a large proportion of mixotrophic taxa and species known for forming harmful algal blooms—were more abundant in the artificial than in the natural pools. Fungi, labyrinthulomycetes, and peronosporomycetes were also more abundant in artificial pools, which may be related to organic matter enrichment. Diatoms and foraminifera showed an opposite trend of higher abundance in natural pools.

Siegmund Nuyts, Eugene J. Farrell, Sheena Fennell et al.

Remote video imagery using shoreline edge detection is widely used in coastal monitoring in order to acquire measurements of nearshore and swash features. Some of these systems are constrained by their long setup time, positioning requirements and considerable hardware costs. As such, there is a need for an autonomous low-cost system (~EUR 500), such as Timex cameras, that can be rapidly deployed in the field, while still producing the outcomes required for coastal monitoring. This research presents an assessment of the effect of the sampling strategy (time-lapse intervals) on the precision of shoreline detection for two low-cost cameras located in a remote coastal area in western Ireland, overlooking a dissipative beach–dune system. The analysis shows that RMSD in the detected shoreline is similar to other studies for sampling intervals ranging between 1 s and 30 s (i.e., RMSD_mean for Camera 1 = 1.4 m and Camera 2 = 0.9 m), and an increase in the sampling interval from 1 s to 30 s had no significant adverse effect on the precision of shoreline detection. The research shows that depending on the intended use of the detected shorelines, the current standard of 1 s image sampling interval when using Timex cameras can be increased up to 30 s without any significant loss of accuracy. This positively impacts battery life and memory storage, making the systems more autonomous; for example, the battery life increased from ~10 days to ~100 days when the sampling interval was increased from 1 to 5 s.

Enzo Pranzini, Irene Cinelli, Giorgio Anfuso

This study investigated the shoreline evolution of the Tuscany coast (Italy) from 1878–1883 to 2019. The 205 km sandy coastline, divided into 821 sectors, each one 250 m long, was analyzed to understand how human activities have altered this once-pristine coast. Sub-period analyses highlighted the impacts, both positive and negative, of various shore-protection projects. Initially, regional beaches were undeveloped and accreting, except for a few river deltas where alternating phases of erosion and accretion were observed. Coastal erosion began at deltas’ areas due to the reduction in sediment inputs and, at other areas, enhanced by the development of human settlements and tourism activities. This triggered the construction of protection structures that shifted erosion processes downdrift, a process that induced the downdrift extension of the structures (according to the “domino” effect), determining the transformation of a completely natural and resilient environment into a largely rigid one. Beach nourishment projects, mostly using inland quarries, added about 1 million cubic meters of sediment from the 1980s to 2019. Currently, 57.8% of beaches are larger than in the 1880s, 9.4% did not change and 32.8% are narrower. Overall, the Tuscan coast gained 6.5 km² of beach surface with an average shoreline advancement of 32 m. Recent trends (2005–2019) show that 37.7% of the coast is eroding, 21.1% is stable, and 41.2% is accreting, with a total surface area increase of about 200,000 m². The beach surface area is still increasing despite the existing reduced sediment input due to the limited sediment loss resulting from the presence of morphological cells enclosed by very prominent headlands and the absence of submarine canyons that would otherwise direct sediments to the continental shelf.

Jie JW Wu

Machine learning (ML) components are being added to more and more critical and impactful software systems, but the software development process of real-world production systems from prototyped ML models remains challenging with additional complexity and interdisciplinary collaboration challenges. This poses difficulties in using traditional software lifecycle models such as waterfall, spiral, or agile models when building ML-enabled systems. In this research, we apply a Systems Engineering lens to investigate the use of V-Model in addressing the interdisciplinary collaboration challenges when building ML-enabled systems. By interviewing practitioners from software companies, we established a set of 8 propositions for using V-Model to manage interdisciplinary collaborations when building products with ML components. Based on the propositions, we found that despite requiring additional efforts, the characteristics of V-Model align effectively with several collaboration challenges encountered by practitioners when building ML-enabled systems. We recommend future research to investigate new process models, frameworks and tools that leverage the characteristics of V-Model such as the system decomposition, clear system boundary, and consistency of Validation & Verification (V&V) for building ML-enabled systems.

Wanchanok Umprasoet, Yongtong Mu, Supannee Somrup et al.

According to the Thailand’s National Strategy (2017-2036) and National Reform Plans, various tools, techniques, or methods are necessary to collect and investigate data for the effective preservation and protection of the country’s natural resources. We aimed to apply various tools and methods for integrated coastal management in Thailand. This study used the InVEST models, including the habitat quality (HQ) and habitat risk assessment (HRA) models, to evaluate the natural habitat quality and cumulative human activity risk in the Sriracha district and Sichang Islands, Thailand. The HQ model revealed the presence of abundant ecological services and high quality natural habitats. We observed habitat degradation in the mooring zone, city area, forests, and coral reefs, with moderate risk to distinct habitats. Our findings identified two potential scenarios. Conservation scenarios exhibited a lower HRA ratio compared to current and development scenarios. Overall, the results showed the effectiveness of the InVEST model in evaluating habitat risk under both present and simulated conditions. Our study highlights the importance of informed management plans and policy-making processes to achieve planned coastal management goals.

John W. Day, Charles A. Hall, Kent Klitgaard et al.

We review impacts of climate change, energy scarcity, and economic frameworks on sustainability of natural and human systems in coastal zones, areas of high biodiversity, productivity, population density, and economic activity. More than 50% of the global population lives within 200 km of a coast, mostly in tropical developing countries. These systems developed during stable Holocene conditions. Changes in global forcings are threatening sustainability of coastal ecosystems and populations. During the Holocene, the earth warmed and became wetter and more productive. Climate changes are impacting coastal systems via sea level rise, stronger tropical cyclones, changes in basin inputs, and extreme weather events. These impacts are passing tipping points as the fossil fuel-powered industrial-technological-agricultural revolution has overwhelmed the source–sink functions of the biosphere and degraded natural systems. The current status of industrialized society is primarily the result of fossil fuel (FF) use. FFs provided more than 80% of global primary energy and are projected to decline to 50% by mid-century. This has profound implications for societal energy requirements, including the transition to a renewable economy. The development of the industrial economy allowed coastal social systems to become spatially separated from their dominant energy and food sources. This will become more difficult to maintain with the fading of cheap energy. It seems inevitable that past growth in energy use, resource consumption, and economic growth cannot be sustained, and coastal areas are in the forefront of these challenges. Rapid planning and cooperation are necessary to minimize impacts of the changes associated with the coming transition. There is an urgent need for a new economic framework to guide society through the transition as mainstream neoclassical economics is not based on natural sciences and does not adequately consider either the importance of energy or the work of nature.