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

Kashafad Bin Hafiz, Aftab Alam Khan, S.M. Mustafizur Rahman et al.

Saint Martin Island (SMI), the first coral reef ecosystem at the Bay of Bengal is ecologically significant for its unique biological richness. The purpose of this study is to monitor environmental health by measuring hazardous metals in coral reef and evaluating the ecological status. The study assesses environmental health state and identifies the probable source using various indices such as contamination factor and degree, pollution load index, geo accumulation index, enrichment factor, ecological risk index, potential ecological risk index, mean effects range median quotient, modified hazard quotient, toxicity risk indicator, and positive matrix factorization, hieracia cluster analysis. The measured constituents are found in range, for As (0.07–2.30), Pb (18.18–28.53), Hg (0.03–0.08), Fe (30160–65445.21), Cd (0.02–5.73), Mn (511–806), Cu (6.2–22.85), Zn (27.7–87), Cr (14.13–58.73), and Ni (32.25–132.13) mg/kg. All the stations were found with higher concentration of Ni in seabed sediments than the threshold limits. The source of toxins from upstream is an issue to consider when analyzing coral health from an ecotoxicological standpoint. The study area also coincided with the newly established SMI marine protected area (MPA) by Ministry of Forest, Environment and Climate Change, Bangladesh, and the sample sites are distinct within the framework of the methodological study. Thus, this research is expected to help for monitoring and establishing baseline for evaluating the ecological status for management plans.

Carolina M. L. Camargo, Victor Malagón-Santos, Jeemijn Scheen et al.

Sea-level projections are highly anticipated outcomes of climate model simulations, relevant for coastal management worldwide. Ideally, any model simulation needs to be validated against observations, but this is impossible for the most recent sets of future climate model simulations, which start near to the present day (2020). Here, we compare satellite observations of regional sea-level change against projections from the Intergovernmental Panel on Climate Change Fifth Assessment Report for total sea-level change and its individual components over their overlapping period, from 2007–2022. We first test and compare three different methods to reduce the internal variability in the observations, which hampers the comparison with models, in particular for shorter time periods. While all three methods reduce the internal variability, we find the low-frequency component analysis (LFCA) removes most of the internal variability. We find that the regional projections are in good agreement with the LFCA-filtered observations, for 96% of ocean area within the 90% confidence interval. For the total sea level and sterodynamic component, the projections both under- and overestimate the observations, depending on the region. For mass-driven sea-level change, the regional projections tend to overestimate the observations. Our analysis gives confidence in sea-level projections for the instrumental era.

Mohsen Pourmohammad Shahvar, Giovanni Marsella

This study enhances coastal vulnerability assessment by introducing a Modified Fuzzy DEMATEL technique that incorporates socio-ecological and power dynamic considerations. The novelty of the study lies in integrating causal analysis with a participatory vulnerability framework tailored to coastal management. By analyzing multiple natural and socio-economic parameters, the results identify land use and land cover as the most influential factors, highlighting a shift toward socio-economic prioritization. The model also acknowledges limitations due to reliance on expert judgment and the absence of ground-truth validation. Our findings emphasize the need for location-specific vulnerability models rather than universal frameworks, offering insights for future participatory and evidence-based coastal management strategies.

Jorge Mongil-Manso, Carmen Patino-Alonso, José Nespereira-Jato et al.

The land use and vegetation type of a reservoir's catchment substantially affect the hydrological processes of soil infiltration and runoff. They also act as drivers or constraints for erosive processes. All the previous processes influence the amount of water and sediment that reach the reservoir and affect its functioning. This study is mainly aimed to improve the knowledge of these processes in southeastern Spain by means of experimental rainfall simulation and multivariate statistical analysis. The results show that the mean infiltration rate is 1.06 times higher in forests than in shrublands and 1.07 times higher than in olive crops (280.52, 265.02, and 262.08 mm/h, respectively), with mean surface runoff consequently 1.57 times lower in forests than in shrublands and 2.41 times lower than in olive crops (20.81, 32.58, and 50.24 mm/h). Likewise, the sediment concentration in the runs is 5.48 times higher in olive groves (518.43 g/L) than in forests (94.61 g/L) and 2.94 times higher than in shrublands (176.48 g/L). Soil properties and parent material might have a more important effect on the studied variables than the different vegetation types. Furthermore, root systems and the use of tillage on crops could favor infiltration, which would tend to equalize the values of the variables analyzed; but this needs to be demonstrated in future research. The results obtained are of interest for vegetation cover and soils management in reservoirs’ catchments in Mediterranean areas. Furthermore, the current research provides an opportunity to study more specifically the origin of the sediment that reaches the reservoirs, beyond sheet and rill erosion.

Thuong V. Tran, Ruth Reef, Xuan Zhu et al.

Mangrove restoration efforts have been ongoing, but with varying levels of success, requiring spatial and temporal monitoring to better understand the stocks and drivers of success. Here, we used multi-spectral remote sensing and spatial regression techniques to examine mangrove distribution and restoration potential in the Vietnamese Southern Coastal (VSC) region from 1988 to 2023, an area where multiple episodes of mangrove restoration have been attempted over the past decades. Our results show that 51.5% of the mangrove area has recovered from previous losses, while 48.5% has been lost during the 1988–2023 period. Significant gains were observed between 2018 and 2023, accounting for 77.8% of the total restoration. However, over 40,000 ha of mangroves were lost during each decade between 1988 and 2018, primarily due to land-use changes. Regression analyses estimated a sustainable mangrove cover increase of 9.9% (23,407 ha) and persistence of 22.5% (52,936 ha), mainly in protected areas and low-impact zones. Conversely, 9.8% (23,056 ha) of mangroves in erosion-prone and human-disturbed regions face continued decline. Our study demonstrated the effectiveness of integrating long-term Normalised Difference Vegetation Index time-series analysis with spatial regression to monitor mangrove ecosystems. These techniques offered a scalable framework for global mangrove monitoring and restoration planning, supporting evidence-based conservation policies.

S. Valery Ávila-Mosqueda, Brigitta I. van Tussenbroek, Joaquín Rodrigo Garza-Pérez

Since 2011, recurring Sargassum Brown Tides (SBTs), caused by periodic massive influxes of holopelagic <i>Sargassum</i> spp., have impacted seagrass meadows in the 50–200 m wide nearshore fringes of Mexican Caribbean reef lagoons. The present study aimed to assess the cumulative effects of SBTs in 2015 and 2018–2019 through a spatial–temporal analysis of seagrass meadows in the Puerto Morelos reef lagoon. We hypothesized that the impacts of the SBTs likely extended beyond the near-shore fringe and were detectable across the seagrass landscape throughout the entire reef lagoon. Through time, the spatial configuration of the seagrass meadows presented a new self-organized configuration linked to spatial fragmentation, an increase in the number of patches but a decrease in size, and changes in vegetation communities, indicating a shift in ecosystem state. This shift may serve as an early warning signal of reef system deterioration. Monitoring seagrass meadow status using this approach provides a deeper understanding of their dynamics, shifts and resilience, and will facilitate the development of timely management strategies.

Hongli Mu, Yifan Zhuo, Yanjuan Wu et al.

Grass, shrubs and tree stems can increase flow depth and resistance and prevent soil erosion, and it is necessary to quantify the relationship between flow depth and hydraulic parameters for high surface roughness of vegetation stem. Therefore, the experimental design included flow depth, velocity and transport capacity, which were measured for different stem covers (bare flume to cover 30%), diameters (2, 10, and 36 mm), and arrangements (bead, tessellation, stagger, random, and stripe) to clarify the relationship between flow depth and the hydraulic radius, Reynolds number Re, Manning coefficient nm, Darcy-Weisbach resistance f and transport capacity Tc. The result shows that flow depth could be effectively predicted by stem cover and stem diameter; the greater the surface roughness was, the more the difference between flow depth and hydraulic radius; and flow depth could not be used as the hydraulic radius to calculate hydraulic parameters for high surface roughness. Re, nm, and f were significantly impacted by flow depth. The linear relationship between flow depth and Re, nm, and f became stronger as stem cover decreased and stem diameter increased, and they were more affected by stem cover than by diameter. The relationship between flow depth and f was less impacted by high surface roughness of vegetation stem. Tc was not significantly impacted by flow depth; the Manning coefficient and Darcy-Weisbach resistance were not appropriate for predicting transport capacity; and the Reynolds number could illustrate the mechanism of sediment transport capacity affected by vegetation stem cover from the perspective of flow resistance.

Yichao Tong, Yuqing Sun, Jing Xia et al.

The abnormal proliferation of <i>Ulva</i> in the Yellow Sea has instigated the notorious green tide phenomenon. Mitigating this ecological challenge necessitates a holistic comprehension of <i>Ulva</i>’s nitrogen and phosphorus uptake behaviors. Investigating the mechanisms governing nutrient absorption, encompassing factors like concentration, form, and input dynamics, has unveiled their profound influence on nutrient assimilation rates. The nutrient absorption characteristics of <i>Ulva prolifera</i>, including its preference for abundant nutrients, a high nitrogen-to-phosphorus (N/P) ratio, and its ability to efficiently absorb nutrients during pulse nutrient input events, determine its dominant role in the green tide events in the Yellow Sea. Although source control and preemptive salvaging are effective methods for managing green tides, addressing the root causes of these coastal ecological disasters requires the implementation of long-term pollution control strategies that align with sustainable development goals, with a priority on reducing marine eutrophication. This is crucial for the effective management and restoration of the coastal ecosystem in the Yellow Sea.

Francisco Uribe, Mauricio Fuentes, Jaime Campos

This study presents numerical simulations of the Aysén tsunami, which occurred on 21 April 2007. The tsunami was triggered by hundreds of landslides caused by a magnitude 6.2 earthquake. With an estimated wave height of 50 m at the northern tip of the Mentirosa Island, the event resulted in 10 fatalities and the destruction of multiple salmon farms along the fjord. We employed the NHWAVE and FUNWAVE-TVD numerical software to conduct a series of simulations using various landslide configurations and two approaches to model landslide motion: a viscous flow and a solid slide governed by Coulomb friction. The numerical results indicate that the solid landslide model without basal friction provides the most accurate representation of the measured in situ run-up heights and generates the largest inundation areas. Furthermore, the simulation results show that the arrival time of the tsunami waves was approximately 600 s. Our findings indicate that the volume of the landslide is the most critical factor in determining tsunami wave heights. Additionally, the Coulomb friction angle is another significant parameter to consider in the modeling process.

Wenhong Cao, Xu Geng, Chunjing Liu et al.

Bedforms are formed by sediment particles under the action of flow, which in turn affect flow and sediment movement. However, the fundamental mechanisms behind the formation and development of bedforms under varying flow conditions, the interconnection between sediment particle movement and bed morphology development, as well as the influence of bedforms on flow and sediment transport, are still not well understood. In the current study, the moveable bed load transport flume experiments under different flow conditions were done, and the development processes of two-dimensional and three-dimensional dunes formed under different flow intensities were simulated. The detailed structure of the bedforms was measured by laser scanning technology, the characteristics of the bedforms were analyzed in detail, and the relations between the formation of the bedforms and the movement of sediment particles are discussed. The results found that the correlation coefficients of the longitudinal profile curve can serve as a quick reference for distinguishing two-dimensional and three-dimensional dunes. When the crestline ratio is used as the criterion for two-dimensional and three-dimensional dunes, the relative amplitude of the dune crestline and the included angle with the flow direction should also be comprehensively considered. The lee side angles of dunes calculated using the triangle generalization method and local section tangent method are compared and show that the values obtained by the former method are only about half of that of the latter. It is also found that the lee side angles of dunes are related to the dune height. The superimposed dunes generally exist in the downstream area of the stoss side of the dunes. The local bed slopes on the stoss side of the dunes show reverse slopes. The superimposed dunes improve the local bed height and further increase the reverse slope degree of the stoss side. A streamwise ridge is an important form located in the upstream area of the dunes stoss side, and they are symmetrically distributed on both sides. Multiple streamwise ridges divide the stoss side of the dunes into relatively independent movement areas, restricting the movement of sediment particles to specific regions. According to the distribution characteristics of bed morphologies, the effects of dunes on sediment particle movement and flow energy consumption are analyzed.

Alexander Metelkin, Bernhard Vowinckel

The settling behavior of sediment aggregates is a critical factor influencing the transport of fine-grained sediment in riverine and marine environments. Due to the small size and fragile structure of cohesive sediment aggregates, direct measurement of their porosity and permeability is challenging. While porosity often is estimated using settling velocity relations, permeability is frequently overlooked. The current study examines the impact of considering non-negligible permeability on the properties of flocs. Aggregate properties are compared by calibrating experimental data to two settling models in a dilute regime: one assumes a fractal structure of aggregates and neglects permeability, while the other assumes constant porosity and permeability. The current results demonstrate that both models describe the experimental data of highly porous aggregates with similar accuracy. Aggregate dynamics are further investigated in more complex flow conditions using numerical simulations applying a volume penalization method to geometrically resolve flocs. The behavior of permeable and impermeable flocs is compared in dense suspension regimes and during dilute settling in density-stratified environments. The current findings reveal that permeability significantly influences settling dynamics in complex scenarios and should be considered when determining aggregate properties.

Bryan A. Oakley, Emily Watling, Nina Musco et al.

Eelgrass (<i>Zostera marina</i>) is a native perennial marine angiosperm found in shallow bays and estuaries. Eelgrass beds are considered essential fish habitats and provide an important food source for marine organisms and waterfowl. This study examines changes in extent of the eelgrass beds in the southern portion of the Little Narragansett Bay Estuary, Rhode Island/Connecticut, USA, between 2019 and 2022. The primary dataset used to delineate eelgrass beds was side-scan sonar coupled with underwater video imagery. Previous studies showed a decline in the extent of eelgrass here between 2012 and 2016. Our results show an increase in eelgrass coverage from 0.52 km² in 2019 to 0.75 km² in 2022. This increase in the extent of eelgrass occurred against the trends of declining eelgrass coverage both globally and regionally.

Yiqing Guo, Nagur Cherukuru, Eric Lehmann et al.

Sea surface temperature (SST) is a fundamental physical parameter characterising the thermal state of sea surface. Due to the intricate thermal interactions between land, sea, and atmosphere, the spatial gradients of SST in coastal waters often appear at finer spatial scales than those in open ocean waters. The Thermal Infrared Sensor (TIRS) onboard Landsat-8, with its 100-meter spatial resolution, offers a unique opportunity to uncover fine-scale coastal SST patterns that would otherwise be overlooked by coarser-resolution thermal sensors. In this study, we first analysed the spatiotemporal patterns of SST in South Australia's temperate coastal waters from 2014 to 2023 by developing an operational approach for SST retrieval from the Landsat-8 TIRS sensor. A buoy was deployed off the coast of Port Lincoln, South Australia, to validate the quality of SST retrievals. Then the daily baseline climatology of SST with 100 m resolution was constructed, which allowed for the detection and analysis of anomalous SST events. Our results suggest the following: (1) the satellite-derived SST data aligned well with the in-situ measured SST values; (2) the semi-enclosed, shallow regions of Upper Spencer Gulf and Upper St Vincent Gulf showed higher temperatures during summer and cooler temperatures during winter than waters closer to the open ocean, resulting in a higher seasonal variation in SST; (3) the near-shore shallow areas in Spencer Gulf and St Vincent Gulf, and regions surrounding Kangaroo Island, were identified to have a higher probability of SST anomalies compared to the rest of the study area; and (4) anomalous SST events were more likely to happen during the warm months than the cool months. We hope these findings would be helpful in supporting the fishing and aquaculture industries in the coastal waters of South Australia.

Maarten Vlaswinkel, Duarte Antunes, Frank Willems

Decarbonization of the transport sector sets increasingly strict demands to maximize thermal efficiency and minimize greenhouse gas emissions of Internal Combustion Engines. This has led to complex engines with a surge in the number of corresponding tunable parameters in actuator set points and control settings. Automated calibration is therefore essential to keep development time and costs at acceptable levels. In this work, an innovative self-learning calibration method is presented based on in-cylinder pressure curve shaping. This method combines Principal Component Decomposition with constrained Bayesian Optimization. To realize maximal thermal engine efficiency, the optimization problem aims at minimizing the difference between the actual in-cylinder pressure curve and an Idealized Thermodynamic Cycle. By continuously updating a Gaussian Process Regression model of the pressure's Principal Components weights using measurements of the actual operating conditions, the mean in-cylinder pressure curve as well as its uncertainty bounds are learned. This information drives the optimization of calibration parameters, which are automatically adapted while dealing with the risks and uncertainties associated with operational safety and combustion stability. This data-driven method does not require prior knowledge of the system. The proposed method is successfully demonstrated in simulation using a Reactivity Controlled Compression Ignition engine model. The difference between the Gross Indicated Efficiency of the optimal solution found and the true optimum is 0.017%. For this complex engine, the optimal solution was found after 64.4s, which is relatively fast compared to conventional calibration methods.

Sebastian Baltes, Florian Angermeir, Chetan Arora et al.

Large Language Models (LLMs) are now ubiquitous in software engineering (SE) research and practice, yet their non-determinism, opaque training data, and rapidly evolving models threaten the reproducibility and replicability of empirical studies. We address this challenge through a collaborative effort of 22 researchers, presenting a taxonomy of seven study types that organizes the landscape of LLM involvement in SE research, together with eight guidelines for designing and reporting such studies. Each guideline distinguishes requirements (must) from recommended practices (should) and is contextualized by the study types it applies to. Our guidelines recommend that researchers: (1) declare LLM usage and role; (2) report model versions, configurations, and customizations; (3) document the tool architecture beyond the model; (4) disclose prompts, their development, and interaction logs; (5) validate LLM outputs with humans; (6) include an open LLM as a baseline; (7) use suitable baselines, benchmarks, and metrics; and (8) articulate limitations and mitigations. We complement the guidelines with an applicability matrix mapping guidelines to study types and a reporting checklist for authors and reviewers. We maintain the study types and guidelines online as a living resource for the community to use and shape (llm-guidelines$.$org).

Shalini Chakraborty, Sebastian Baltes

The IT industry provides supportive pathways such as returnship programs, coding boot camps, and buddy systems for women re-entering their job after a career break. Academia, however, offers limited opportunities to motivate women to return. We propose a diverse multicultural research project investigating the challenges faced by women with software engineering (SE) backgrounds re-entering academia or related research roles after a career break. Career disruptions due to pregnancy, immigration status, or lack of flexible work options can significantly impact women's career progress, creating barriers for returning as lecturers, professors, or senior researchers. Although many companies promote gender diversity policies, such measures are less prominent and often under-recognized within academic institutions. Our goal is to explore the specific challenges women encounter when re-entering academic roles compared to industry roles; to understand the institutional perspective, including a comparative analysis of existing policies and opportunities in different countries for women to return to the field; and finally, to provide recommendations that support transparent hiring practices. The research project will be carried out in multiple universities and in multiple countries to capture the diverse challenges and policies that vary by location.

Daniel A. Friess, Zoë I. Shribman, Milica Stankovic et al.

Mangroves, tidal marshes and seagrasses have experienced extensive historical reduction in extent due to direct and indirect effects of anthropogenic land use change. Habitat loss has contributed carbon emissions and led to foregone opportunities for carbon sequestration, which are disproportionately large due to high ‘blue carbon’ stocks and sequestration rates in these coastal ecosystems. As such, there has been a rapid increase in interest in using coastal habitat restoration as a climate change mitigation tool. This review shows that restoration efforts are able to substantially increase blue carbon stocks, while also having a positive impact on various gaseous fluxes. However, blue carbon increases are spatially variable, due to biophysical factors such as climate and geomorphic setting. While there are potentially hundreds of thousands of hectares of land that may be biophysically suitable for restoration, these activities are still often conducted at small scales and with mixed success. Maximizing potential carbon gains through blue carbon restoration will require managers and coastal planners to overcome the myriad socioeconomic and governance constraints related to land tenure, legislation, target setting and cost, which often push restoration projects into locations that are biophysically unsuitable for plant colonization.

Taichi Nishimura, Shota Nakada, Hokuto Munakata et al.

We propose Lighthouse, a user-friendly library for reproducible video moment retrieval and highlight detection (MR-HD). Although researchers proposed various MR-HD approaches, the research community holds two main issues. The first is a lack of comprehensive and reproducible experiments across various methods, datasets, and video-text features. This is because no unified training and evaluation codebase covers multiple settings. The second is user-unfriendly design. Because previous works use different libraries, researchers set up individual environments. In addition, most works release only the training codes, requiring users to implement the whole inference process of MR-HD. Lighthouse addresses these issues by implementing a unified reproducible codebase that includes six models, three features, and five datasets. In addition, it provides an inference API and web demo to make these methods easily accessible for researchers and developers. Our experiments demonstrate that Lighthouse generally reproduces the reported scores in the reference papers. The code is available at https://github.com/line/lighthouse.

Mehrdad Sabetzadeh, Chetan Arora

Natural Language Processing (NLP) is now a cornerstone of requirements automation. One compelling factor behind the growing adoption of NLP in Requirements Engineering (RE) is the prevalent use of natural language (NL) for specifying requirements in industry. NLP techniques are commonly used for automatically classifying requirements, extracting important information, e.g., domain models and glossary terms, and performing quality assurance tasks, such as ambiguity handling and completeness checking. With so many different NLP solution strategies available and the possibility of applying machine learning alongside, it can be challenging to choose the right strategy for a specific RE task and to evaluate the resulting solution in an empirically rigorous manner. In this chapter, we present guidelines for the selection of NLP techniques as well as for their evaluation in the context of RE. In particular, we discuss how to choose among different strategies such as traditional NLP, feature-based machine learning, and language-model-based methods. Our ultimate hope for this chapter is to serve as a stepping stone, assisting newcomers to NLP4RE in quickly initiating themselves into the NLP technologies most pertinent to the RE field.

Hao Wu, Likun Zhang, Shucheng Li et al.

In the federated learning (FL) process, since the data held by each participant is different, it is necessary to figure out which participant has a higher contribution to the model performance. Effective contribution assessment can help motivate data owners to participate in the FL training. Research works in this field can be divided into two directions based on whether a validation dataset is required. Validation-based methods need to use representative validation data to measure the model accuracy, which is difficult to obtain in practical FL scenarios. Existing validation-free methods assess the contribution based on the parameters and gradients of local models and the global model in a single training round, which is easily compromised by the stochasticity of model training. In this work, we propose CoAst, a practical method to assess the FL participants' contribution without access to any validation data. The core idea of CoAst involves two aspects: one is to only count the most important part of model parameters through a weights quantization, and the other is a cross-round valuation based on the similarity between the current local parameters and the global parameter updates in several subsequent communication rounds. Extensive experiments show that CoAst has comparable assessment reliability to existing validation-based methods and outperforms existing validation-free methods.