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

Jonathan Dale, Michelle Farrell

Saltmarsh habitat provides important ecosystem services, such as water quality regulation, carbon sequestration and flood defence, but is experiencing losses globally. Historically, this has been caused by land claim, and more recently by rising sea levels. Several methods have been implemented to compensate for saltmarsh habitat loss, including realigning defences, transplanting vegetation, and building structures such as sedimentation fields to enclose areas of mudflat and encourage sediment deposition. It has been suggested that sedimentation fields may offer saltmarsh restoration without the limitations identified in other restoration approaches, such as poor drainage and anoxia caused by changes to the sediment structure due to prior human activity. In this article, we argue that restoration through sedimentation fields should be viewed as a continuation of human activity influencing natural processes, rather than a method that overcomes the influence of prior human activity on saltmarsh ecosystem functioning. This opinion is evidenced by a critical review of the (pre-)historic human activity and saltmarsh restoration attempts at Rumney Great Wharf, Severn Estuary, Wales, where sedimentation fields were constructed between 1989 and 2005 and extended in 2024. We then evaluate the research requirements that need to be addressed to ensure the successful implementation of future schemes, including further understanding of the interactions between physical and biological processes, to enhance ecosystem functioning in sites restored using sedimentation fields.

Chinomnso C. Onwubiko, Denis W. Aheto

Nature-based Solutions (NbS) have emerged as transformative approaches to address societal challenges, support biodiversity, and enhance human well-being. Globally, NbS are recognized for their potential to mitigate climate change impacts such as coastal flooding. Despite growing policy interest, limited empirical evidence exists on their real-world effectiveness, particularly in Africa. The core objective of this study was to evaluate how community perceptions, awareness, and demographic factors influence the acceptance and effectiveness of NbS for flood risk reduction in selected coastal communities of Rivers State, Nigeria. Specifically, it aimed to assess community perceptions and awareness of NbS, identify demographic, geographic, and psychosocial factors influencing these perceptions, and analyze how risk perception and local knowledge affect acceptance. The study addressed three key questions: (1) How do community perceptions affect NbS acceptance and implementation? (2) What factors shape awareness and understanding of NbS in Kula, Oyorokoto, and Bonny? (3) How do perceptions vary across demographic groups? To answer these, a structured survey of 1224 respondents was conducted: 61% were male and 39% female, with most aged 31–50 years (80%). Education emerged as a key factor—about 49% of respondents had at least secondary or post-secondary education, which showed a significant link with positive perceptions of NbS (χ² = 460.98, <i>p</i> < 0.001, Cramer’s V = 0.434). Occupation also shaped views: traders (36.8%) and fishers (24.5%) formed the majority, with occupational patterns showing moderate influence (χ² = 112.68, <i>p</i> < 0.001, Cramer’s V = 0.215). Overall, awareness was the strongest predictor, with communities reporting higher NbS awareness demonstrating significantly greater acceptance (OR = 0.06, <i>p</i> < 0.001). These findings highlight that targeted awareness-raising, education, and community engagement are critical to promoting mangrove conservation, afforestation, and ecosystem restoration, ultimately strengthening resilience to climate-induced risks in coastal communities.

Gerasimos A. Kolokythas, Athanassios A. Dimas

The three-dimensional, turbulent, free-surface flow developing in the surf zone over a constant-slope beach as a result of the interaction between the longshore current and the undertow, induced by spilling wave breaking oblique to the shoreline, is numerically simulated. The simulations are performed by implementing the large-wave simulation (LWS) method in a numerical solver of the three-dimensional Navier–Stokes equations. According to the LWS method, large velocity and free-surface elevation scales are fully resolved, while the effect of the corresponding subgrid scales is modeled by eddy-viscosity stresses. The model validation is based on the comparison between the present numerical results and existing experimental measurements for a case of incident regular waves propagating normal to the shoreline over a bed of constant slope 1/35. It is found that the LWS model adequately predicts the wave-breaking parameters—breaking height and depth—and the undertow vertical profiles in the surf zone. Then, two cases of oblique waves, with wave incidence angles of 20° and 30°, and all other parameters identical to those of the validation case, are considered. The numerical results include the gradual breaking process of the refracted waves, as well as the three-dimensional structure of the longshore current and the undertow in the surf zone. In the outer surf zone, the undertow has a larger velocity magnitude than the longshore current, while in the inner surf zone, the opposite occurs.

Daisuke Kikuta, Hiroki Ikeuchi, Kengo Tajiri

Chaos Engineering (CE) is an engineering technique aimed at improving the resilience of distributed systems. It involves intentionally injecting faults into a system to test its resilience, uncover weaknesses, and address them before they cause failures in production. Recent CE tools automate the execution of predefined CE experiments. However, planning such experiments and improving the system based on the experimental results still remain manual. These processes are labor-intensive and require multi-domain expertise. To address these challenges and enable anyone to build resilient systems at low cost, this paper proposes ChaosEater, a system that automates the entire CE cycle with Large Language Models (LLMs). It predefines an agentic workflow according to a systematic CE cycle and assigns subdivided processes within the workflow to LLMs. ChaosEater targets CE for software systems built on Kubernetes. Therefore, the LLMs in ChaosEater complete CE cycles through software engineering tasks, including requirement definition, code generation, testing, and debugging. We evaluate ChaosEater through case studies on small- and large-scale Kubernetes systems. The results demonstrate that it consistently completes reasonable CE cycles with significantly low time and monetary costs. Its cycles are also qualitatively validated by human engineers and LLMs.

I. V. Florinsky, S. O. Zharnova

Geomorphometric modeling and mapping of ice-free Antarctic areas is promising for obtaining new quantitative knowledge about the topography of these unique landscapes and for the further use of morphometric information in Antarctic research. Within the framework of a project of creating a physical geographical thematic scientific reference geomorphometric atlas of ice-free terrains of Antarctica, we performed geomorphometric modeling and mapping of key ice-free areas of the Sôya Coast (the east coast of Lützow-Holm Bay, Queen Maud Land, East Antarctica). These include the Flatvaer (Ongul) Islands, Langhovde Hills, Breidvågnipa, Skarvsnes Foreland, Skallen Hills, and Skallevikhalsen Hills. As input data for geomorphometric modeling and mapping, we used five fragments of the Reference Elevation Model of Antarctica. For the six ice-free areas and adjacent glaciers, we derived models and maps of eleven most scientifically important morphometric variables (i.e., slope, aspect, horizontal curvature, vertical curvature, minimal curvature, maximal curvature, catchment area, topographic wetness index, stream power index, total insolation, and wind exposition index). The obtained models and maps describe the ice-free topography of the Sôya Coast in a rigorous, quantitative, and reproducible manner. New morphometric data can be useful for further geological, geomorphological, glaciological, ecological, and hydrological studies of this region.

Luiz Drude de Lacerda, Alexander Cesar Ferreira, Rebecca Borges et al.

The semiarid northeast coast of Brazil harbours just less than 44,300 ha of mangroves, 4% of Brazilian total. Notwithstanding this relatively small area, these forests have high ecological and economic importance, sustaining traditional fisheries and protecting biodiversity, including many threatened species. They present unique biogeochemical characteristics resulting in distinct ecosystem functioning compared to mangroves located in humid areas. Semiarid mangroves present lower aboveground biomass compared to humid region mangroves but show similar belowground biomass. Whereas mangrove soils in humid areas are strongly influenced by sulphate reduction, iron geochemistry is a primary driver of soil characteristics in semiarid mangrove soils, suggesting different responses to climate change drivers between them. Although legally protected, they have incurred continuous degradation due to regional drivers, mostly aquaculture and river damming, which differs from those in humid coast mangroves. Semiarid mangroves are also particularly sensitive to drivers associated with global climate change (high temperatures, reduced rainfall and sea level rise). These conditions occur at a global scale; however, the impacts are worsened by the natural conditions of semiarid coastlines, which already provide biologically stressful conditions for mangroves. This article compares the impacts of such drivers in semiarid mangroves with those of humid mangroves, focusing on their biogeochemical response and eventual rehabilitation.

Emma I. Brahmey, Karen J. McGlathery, Scott C. Doney

To inform water quality monitoring techniques and modeling at coastal research sites, this study investigated seasonality and trends in coastal lagoons on the eastern shore of Virginia, USA. Seasonality was quantified with harmonic analysis of low-frequency time-series, approximately 30 years of quarterly sampled data at thirteen mainland, lagoon, and ocean inlet sites, along with 4–6 years of high-frequency, 15-min resolution sonde data at two mainland sites. Temperature, dissolved oxygen, and apparent oxygen utilization (AOU) seasonality were dominated by annual harmonics, while salinity and chlorophyll-a exhibited mixed annual and semi-annual harmonics. Mainland sites had larger seasonal amplitudes and higher peak summer values for temperature, chlorophyll-a and AOU, likely from longer water residence times, shallower waters, and proximity to marshes and uplands. Based on the statistical subsampling of high-frequency data, one to several decades of low-frequency data (at quarterly sampling) were needed to quantify the climatological seasonal cycle within specified confidence intervals. Statistically significant decadal warming and increasing chlorophyll-a concentrations were found at a sub-set of mainland sites, with no distinct geographic patterns for other water quality trends. The analysis highlighted challenges in detecting long-term trends in coastal water quality at sites sampled at low frequency with large seasonal and interannual variability.

Daniel R. Clarkson, Lawrence A. Bull, Chandula T. Wickramarachchi et al.

Regression is a fundamental prediction task common in data-centric engineering applications that involves learning mappings between continuous variables. In many engineering applications (e.g.\ structural health monitoring), feature-label pairs used to learn such mappings are of limited availability which hinders the effectiveness of traditional supervised machine learning approaches. The current paper proposes a methodology for overcoming the issue of data scarcity by combining active learning with hierarchical Bayesian modelling. Active learning is an approach for preferentially acquiring feature-label pairs in a resource-efficient manner. In particular, the current work adopts a risk-informed approach that leverages contextual information associated with regression-based engineering decision-making tasks (e.g.\ inspection and maintenance). Hierarchical Bayesian modelling allow multiple related regression tasks to be learned over a population, capturing local and global effects. The information sharing facilitated by this modelling approach means that information acquired for one engineering system can improve predictive performance across the population. The proposed methodology is demonstrated using an experimental case study. Specifically, multiple regressions are performed over a population of machining tools, where the quantity of interest is the surface roughness of the workpieces. An inspection and maintenance decision process is defined using these regression tasks which is in turn used to construct the active-learning algorithm. The novel methodology proposed is benchmarked against an uninformed approach to label acquisition and independent modelling of the regression tasks. It is shown that the proposed approach has superior performance in terms of expected cost -- maintaining predictive performance while reducing the number of inspections required.

Brandon Vu, Toki Migimatsu, Jeannette Bohg

Task and Motion Planning (TAMP) algorithms solve long-horizon robotics tasks by integrating task planning with motion planning; the task planner proposes a sequence of actions towards a goal state and the motion planner verifies whether this action sequence is geometrically feasible for the robot. However, state-of-the-art TAMP algorithms do not scale well with the difficulty of the task and require an impractical amount of time to solve relatively small problems. We propose Constraints and Streams for Task and Motion Planning (COAST), a probabilistically-complete, sampling-based TAMP algorithm that combines stream-based motion planning with an efficient, constrained task planning strategy. We validate COAST on three challenging TAMP domains and demonstrate that our method outperforms baselines in terms of cumulative task planning time by an order of magnitude. You can find more supplementary materials on our project \href{https://branvu.github.io/coast.github.io}{website}.

Arnaud Debussche, Étienne Mémin, Antoine Moneyron

In this paper, we consider a stochastic nonlinear formulation of classical coastal waves models under location uncertainty (LU). In the formal setting investigated here, stochastic versions of the Serre-Green- Nagdi, Boussinesq and classical shallow water wave models are obtained through an asymptotic expansion, which is similar to the one operated in the deterministic setting. However, modified advection terms emerge, together with advection noise terms. These terms are well-known features arising from the LU formalism, based on momentum conservation principle.

Nicola C. James, Alan K. Whitfield

One of the most critical ecosystem functions provided by shallow coastal habitats is as nurseries for the juveniles of fish. Many of the studies that have assessed the nursery function of structurally complex coastal habitats have compared seagrass with unstructured sand and mud and as such, seagrass has emerged as the most important coastal nursery habitat for juvenile fishes. Although considerably less work has focussed on the nursery provision of structurally complex macroalgae within coastal nursery seascapes, recent work has started to highlight that the nursery provision of canopy-forming macroalgae may in fact be comparable with that of seagrass. This review collates research published on the important nursery role of macroalgae within both tropical and temperate coastal seascapes and highlights the importance of smaller canopy-forming brown algae from the Fucalean genera (particularly Sargassum spp.) as core nursery areas for juvenile fishes, particularly emperors (Lethrinidae), rabbitfishes (Siganidae), wrasse and parrotfishes (Labridae), goatfishes (Mullidae), groupers (Serranidae), surgeonfish (Acanthuridae) and damselfish (Pomacentridae) within tropical back-reef systems. Similarly, in temperate nursery seascapes, fucoid (Cystoseira spp.) and macroalgae-dominated reefs were important nursery habitats for damselfish (Chromis chromis), groupers and numerous species of wrasse and sparids (Sparidae). Although the overall density of juvenile fish was not shown to be higher in kelp relative to other temperate nursery habitats, kelp was important in the recruitment of Notolabrus celiodotus (wrasse), Paralabrax clathrus (Serranidae), Brachyistius frenatus (Embiotocidae), Heterostichus rostratus (Clinidae) and Sebastes spp. (Scorpaenidae). Although not interchangeable (fish communities were often different), the nursery function of structurally complex macroalgae was found to be similar to that of seagrass in both temperate and tropical seascapes.

Ana Paula de Castro Rodrigues, Matheus Marinho Pereira, Aline Campos et al.

Bioavailability assessment is important for evaluating the risks to the local biota, and the combined use of several ecological risk indices in eutrophic environments allows the best analysis of the local reality for decision-making. The relationship between acid volatile sulfide (AVS) concentrations and simultaneously extracted metals (SEM) allows us to infer the metal holding capacity of sediment, with the objective of evaluating the potential bioavailability of trace metals (Cd, Cu, Ni, Pb, and Zn) using ecological risk indices, such as the ΣSEM/AVS model and Adverse Effect Index (AEI), in surface sediments from Guanabara Bay and Sepetiba Bay, Brazil. AVS was determined using a colorimetric method and SEM with ICP-OES. In general, almost all sampling in Sepetiba Bay showed ΣSEM/AVS ratio values above 1. However, all results for the ΣSEM/AVS ratio found for the Guanabara Bay sediments were <1 for both estuaries. After normalization by organic carbon content, a possible toxicity risk for biota was found in Sepetiba Bay. However, the AEI in Guanabara Bay was above 1 for all metals in most samples, also indicating a risk to the biota.

Antonella Peresan, Hany M. Hassan

Significant tsunamis in Northern Adriatic are rare and only a few historical events were reported in the literature, with sources mostly located along with central and southern parts of the Adriatic coasts. Recently, a tsunami alert system has been established for the whole Mediterranean area; however, a detailed description of the potential impact of tsunami waves on coastal areas is still missing for several sites. This study aims at modelling the hazard associated with possible tsunamis, generated by offshore earthquakes, with the purpose of contributing to tsunami risk assessment for selected urban areas located along the Northeastern Adriatic coasts. Tsunami modelling is performed by the NAMI DANCE software, which allows accounting for seismic source properties, variable bathymetry, and non-linear effects in waves propagation. Preliminary hazard scenarios at the shoreline are developed for the coastal areas of Northeastern Italy and at selected cities (namely Trieste, Monfalcone, Lignano and Grado). A wide set of potential tsunamigenic sources of tectonic origin, located in three distance ranges (namely at Adriatic-wide, regional and local scales), are considered for the modelling; sources are defined according to available literature, which includes catalogues of historical tsunami and existing active faults databases. Accordingly, a preliminary set of tsunami-related parameters and maps are obtained (e.g. maximum run-up, arrival times, synthetic mareograms), relevant towards planning mitigation actions at the selected sites.

Tao Chen, Miqing Li

In presence of multiple objectives to be optimized in Search-Based Software Engineering (SBSE), Pareto search has been commonly adopted. It searches for a good approximation of the problem's Pareto optimal solutions, from which the stakeholders choose the most preferred solution according to their preferences. However, when clear preferences of the stakeholders (e.g., a set of weights which reflect relative importance between objectives) are available prior to the search, weighted search is believed to be the first choice since it simplifies the search via converting the original multi-objective problem into a single-objective one and enable the search to focus on what only the stakeholders are interested in. This paper questions such a "weighted search first" belief. We show that the weights can, in fact, be harmful to the search process even in the presence of clear preferences. Specifically, we conduct a large scale empirical study which consists of 38 systems/projects from three representative SBSE problems, together with two types of search budget and nine sets of weights, leading to 604 cases of comparisons. Our key finding is that weighted search reaches a certain level of solution quality by consuming relatively less resources at the early stage of the search; however, Pareto search is at the majority of the time (up to 77% of the cases) significantly better than its weighted counterpart, as long as we allow a sufficient, but not unrealistic search budget. This, together with other findings and actionable suggestions in the paper, allows us to codify pragmatic and comprehensive guidance on choosing weighted and Pareto search for SBSE under the circumstance that clear preferences are available. All code and data can be accessed at: https://github.com/ideas-labo/pareto-vs-weight-for-sbse.

Angelika Kaplan, Thomas Kühn, Ralf Reussner

Background: Classifications in meta-research enable researchers to cope with an increasing body of scientific knowledge. They provide a framework for, e.g., distinguishing methods, reports, reproducibility, and evaluation in a knowledge field as well as a common terminology. Both eases sharing, understanding and evolution of knowledge. In software engineering (SE), there are several classifications that describe the nature of SE research. Regarding the consolidation of the large body of classified knowledge in SE research, a generally applicable classification scheme is crucial. Moreover, the commonalities and differences among different classification schemes have rarely been studied. Due to the fact that classifications are documented textual, it is hard to catalog, reuse, and compare them. To the best of our knowledge, there is no research work so far that addresses documentation and systematic investigation of classifications in SE meta-research. Objective: We aim to construct a unified, generally applicable classification scheme for SE meta-research by collecting and documenting existing classification schemes and unifying their classes and categories. Method: Our execution plan is divided into three phases: construction, validation, and evaluation phase. For the construction phase, we perform a literature review to identify, collect, and analyze a set of established SE research classifications. In the validation phase, we analyze individual categories and classes of included papers. We use quantitative metrics from literature to conduct and assess the unification process to build a generally applicable classification scheme for SE research. Lastly, we investigate the applicability of the unified scheme. Therefore, we perform a workshop session followed by user studies w.r.t. investigations about reliability, correctness, and ease of use.

Xuanzhe Liu, Diandian Gu, Zhenpeng Chen et al.

Deep learning (DL) has become a key component of modern software. In the "big model" era, the rich features of DL-based software substantially rely on powerful DL models, e.g., BERT, GPT-3, and the recently emerging GPT-4, which are trained on the powerful cloud with large datasets. Hence, training effective DL models has become a vital stage in the whole software lifecycle. When training deep learning models, especially those big models, developers need to parallelize and distribute the computation and memory resources amongst multiple devices in the training process, which is known as distributed deep learning training, or distributed training for short. However, the unique challenges that developers encounter in distributed training process have not been studied in the software engineering community. Given the increasingly heavy dependence of current DL-based software on distributed training, this paper aims to fill in the knowledge gap and presents the first comprehensive study on developers' issues in distributed training. To this end, we analyze 1,131 real-world developers' issues about using these frameworks reported on Stack Overflow and GitHub. We construct a fine-grained taxonomy consisting of 30 categories regarding the fault symptoms and summarize common fix patterns for different symptoms. Based on the results, we suggest actionable implications on research avenues that can potentially facilitate the distributed training to develop DL-based software, such as focusing on the frequent and common fix patterns when designing testing or debugging tools, developing efficient testing and debugging techniques for communication configuration along with the synthesis of network configuration analysis, designing new multi-device checkpoint-and-replay techniques to help reproduction, and designing serverless APIs for cloud platforms.

Maria Augusta Nelson, Lesandro Ponciano

This work presents an approach for using GitHub classroom as a shared, structured, and persistent repository to support project-based courses at the Software Engineering Undergraduate program at PUC Minas, in Brazil. We discuss the needs of the different stakeholders that guided the development of the approach. Results on the perceptions of professors and students show that the approach brings benefits. Besides the lessons learned, we present insights on improving the education of the next generation of software engineers by employing metrics to monitor skill development, verifying student work portfolios, and employing tooling support in project-based courses.

Feras A. Batarseh, Rasika Mohod, Abhinav Kumar et al.

The field of artificial intelligence (AI) is witnessing a recent upsurge in research, tools development, and deployment of applications. Multiple software companies are shifting their focus to developing intelligent systems; and many others are deploying AI paradigms to their existing processes. In parallel, the academic research community is injecting AI paradigms to provide solutions to traditional engineering problems. Similarly, AI has evidently been proved useful to software engineering (SE). When one observes the SE phases (requirements, design, development, testing, release, and maintenance), it becomes clear that multiple AI paradigms (such as neural networks, machine learning, knowledge-based systems, natural language processing) could be applied to improve the process and eliminate many of the major challenges that the SE field has been facing. This survey chapter is a review of the most commonplace methods of AI applied to SE. The review covers methods between years 1975-2017, for the requirements phase, 46 major AI-driven methods are found, 19 for design, 15 for development, 68 for testing, and 15 for release and maintenance. Furthermore, the purpose of this chapter is threefold; firstly, to answer the following questions: is there sufficient intelligence in the SE lifecycle? What does applying AI to SE entail? Secondly, to measure, formulize, and evaluate the overlap of SE phases and AI disciplines. Lastly, this chapter aims to provide serious questions to challenging the current conventional wisdom (i.e., status quo) of the state-of-the-art, craft a call for action, and to redefine the path forward.

Nasir U. Eisty, Jeffrey C. Carver

Background: Research software is software developed by and/or used by researchers, across a wide variety of domains, to perform their research. Because of the complexity of research software, developers cannot conduct exhaustive testing. As a result, researchers have lower confidence in the correctness of the output of the software. Peer code review, a standard software engineering practice, has helped address this problem in other types of software. Aims: Peer code review is less prevalent in research software than it is in other types of software. In addition, the literature does not contain any studies about the use of peer code review in research software. Therefore, through analyzing developers perceptions, the goal of this work is to understand the current practice of peer code review in the development of research software, identify challenges and barriers associated with peer code review in research software, and present approaches to improve the peer code review in research software. Method: We conducted interviews and a community survey of research software developers to collect information about their current peer code review practices, difficulties they face, and how they address those difficulties. Results: We received 84 unique responses from the interviews and surveys. The results show that while research software teams review a large amount of their code, they lack formal process, proper organization, and adequate people to perform the reviews. Conclusions: Use of peer code review is promising for improving the quality of research software and thereby improving the trustworthiness of the underlying research results. In addition, by using peer code review, research software developers produce more readable and understandable code, which will be easier to maintain.