Hasil untuk "Naval Science"

T. Fossen

Technical and Research Symposium Society of Naval Architects and Marine EngineersMcGraw-Hill Encyclopedia of Science and TechnologyJane's High-speed Marine Craft and Air Cushion VehiclesNew York Port HandbookUndersea Technology Handbook, Directory无模型自适应控制兵法简述Marine Technology and SNAME News人月神話有限元方法Marine Robotics and Applications周易譯注余欢弹性固体中波的传播Aeroplane and Commercial Aviation NewsBulletin of the Permanent International Association of Navigation Congresses递推辨识的理论与实践Handbook of Marine Craft Hydrodynamics and Motion ControlTransactionsTransactions The Society of Naval Architects and Marine EngineersNaval Engineers JournalJane's High-speed Marine Craft核潜艇之旅生活的暗面食物恋Jane's Surface Skimmers美, 看不见的竞争力Mechanical Engineers' Handbook: Power; J. Kenneth Salisbury, editor离散时间控制系统概率, 随机变量与随机过程Government Reports Announcements & IndexMonthly Catalog of United States Government PublicationsDirectory of Special Libraries and Information Centers一個人住第9年Penny Stock HandbookHandbook on Selling to the U.S. MilitaryUnderwater Science and Technology Information Bulletin一个人流浪, 不必去远方The ProceedingsGovernment Reports Announcements

Senka Gudić, Nikša Čatipović, Marija Ban et al.

This study investigates the efficiency of electrocoagulation (EC) in removing Tartrazine Yellow (TY) azo dye from synthetic wastewater using aluminium electrodes. The effects of current density, <i>i</i> (0.008–0.024 A cm⁻²), initial solution pH (3.0–7.0), and treatment time, <i>t</i> (10–50 min) on key process parameters, including pH, temperature (<i>T</i>), TY dye concentration (<i>c</i>) and removal efficiency (<i>R</i>), anode consumption, and sludge characterisation were studied. The experiments were conducted in a batch reactor according to the experimental plan developed in Design-Expert software, which was also used for the evaluation of the obtained results. As the EC process progresses, the removal efficiency of the TY dye increases, while the removal dynamics and the final value of <i>R</i> (ranging from about 28% to 99%) depend on the experimental conditions (<i>i</i>, initial pH, and <i>t</i>). A high <i>R</i>-value is reached faster with the application of higher current densities and lower initial pH. This is associated with a higher proportion of carbon and sulphur in the sludge (from the TY dye) after the EC process. Additionally, a mathematical model was developed to predict the experimental data. A numerical optimisation method using response surface methodology (RSM) was applied to determine the optimal operating conditions for TY dye removal. This resulted in the following conditions: pH = 3.37, <i>t</i> = 18.74 min, and <i>i</i> = 0.016 A cm⁻², achieving a removal efficiency of ≈70%.

Karine Sartelet, Jules Kerckhoffs, Eleni Athanasopoulou et al.

Mapping urban pollution is essential for assessing population exposure and addressing associated health impacts. High urban concentrations are due to the proximity of sources such as traffic or residential heating, and to urban density with the presence of buildings that reduce street ventilation. This urban complexity makes fine-scale mapping challenging, even for regulated pollutants such as NO2 and PM2.5. In this study we apply state-of-the-art empirical and deterministic modeling approaches to produce high-resolution (<100 m) pollution maps across five European cities (Paris, Athens, Birmingham, Rotterdam, Bucharest). These methodologies enable full-city mapping capturing intra-urban gradients of concentrations. Depending on the methodology, regulated pollutants (NO2, PM2.5) and/or emerging pollutants (black carbon (BC) and ultrafine particles (UFP characterized here by particulate number concentration PNC)) are considered. For deterministic modelling, different approaches are presented: a multi-scale Eulerian modelling chain down to the street scale with chemistry/aerosol dynamics at all scales, multi-scale hybrid models with Eulerian regional dispersion and Gaussian subgrid dispersion, and a Gaussian-based model. Empirical land use regression models were developed based upon mobile monitoring.To compare the relative performance of the methodologies and to evaluate their performance and limitations, the modelling results are compared to fixed measurement stations. We introduce a standardized metric to quantify spatial and seasonal variability and assess each method’s capacity to reproduce fine-scale urban heterogeneity. We also evaluate how data assimilation affects both concentration accuracy and variability representation—particularly relevant for emerging pollutants where measurement data are sparse. We confirm established seasonal and spatial patterns: spatial variability is more pronounced for PNC, NO2 and BC than PM2.5, and concentrations are higher during the winter periods. We also observe reduced spatial variability in winter for PM2. 5 (linked to residential heating) and for BC in cities with significant wood burning emissions. This study adds unique value by evaluating these patterns using fixed measurement stations, and quantifying them across entire urban areas at very fine spatial resolution (<100 m). Furthermore, important methodological strengths and limitations are pointed out, providing practical guidance for the selection and improvement of urban exposure mapping methods, supporting the implementation of the new EU Air Quality Directive.

The CMS collaboration, A. Tumasyan, W. Adam et al.

Yunyun Gai, Longsheng Li, Zikang Li et al.

When the XBeach model is used to simulate beach profiles, the selection of four sensitive parameters—facua, gammax, eps, and gamma—is crucial. Among these, the two key parameters, facua and gamma, are particularly sensitive. However, the XBeach model does not specify the exact choice of these four key parameters, offering only a broad range for each one. In this paper, we investigate the applicability of tuning these four parameters within the XBeach model. We employ Generalized Likelihood Uncertainty Estimation (GLUE) to optimize the model settings. The Brier Skill Score (<i>BSS</i>) for each parameter combination is calculated to quantify the likelihood probability distribution of each parameter. The optimal parameter set (facua = 0.20, gamma = 0.50) was ultimately determined. Here, the facua parameter represents the degree of influence of wave skewness and asymmetry on the direction of sediment transport, while the gamma parameter represents the equivalent random wave in the wave dissipation model and is used to calculate the probability of wave breaking. Six profiles of the southern beach on Chudao Island are selected to validate the results, establishing the XBeach model based on profile measurement data before and after Typhoon “Lekima”. The results indicate that after parameter optimization, the simulation accuracy of XBeach is significantly improved, with the <i>BSS</i> increasing from 0.3 and 0.17 to 0.68 and 0.79 in P1 and P6 profiles, respectively. This paper provides a recommended range for parameter values for future research.

Guoqiang Liang, Jingqian Gong, Mengxuan Li et al.

Large language models (LLMs) have exhibited exceptional capabilities in natural language understanding and generation, image recognition, and multimodal tasks, charting a course towards AGI and emerging as a central issue in the global technological race. This manuscript conducts a comprehensive review of the core technologies that support LLMs from a user standpoint, including prompt engineering, knowledge-enhanced retrieval augmented generation, fine tuning, pretraining, and tool learning. Additionally, it traces the historical development of Science of Science (SciSci) and presents a forward looking perspective on the potential applications of LLMs within the scientometric domain. Furthermore, it discusses the prospect of an AI agent based model for scientific evaluation, and presents new research fronts detection and knowledge graph building methods with LLMs.

Shi Yan Sun, Ruili Gao, Yueyang Li et al.

Introduction: The study presents an integrated system comprising a central platform and four wave-energy converters, with a focus on investigating their coupled motions induced by ocean waves. The interaction between the buoys and the central platform is achieved through the implementation of spring components. The power take-off system is simulated by incorporating damping coefficients and stiffness into these spring components, enabling a detailed analysis of the energy conversion of such system.Methods: Numerical simulations based on the continuity equation and the Reynolds-Averaged Navier-Stokes (RANS) equations, coupled with the realizable k−ε turbulence model, are conducted. The two-phase flow model employs the Volume of Fluid (VOF) method to accurately capture free surface elevations. Additionally, frequency-domain predictions, based on the linearized velocity potential flow theory, are provided for a single central platform and buoy for comparative purposes.Results: Detailed results regarding the effects of wave frequency and the damping coefficient of the power take-off system are presented.Discussion: The results reveal that while both the platform’s motion and the relative motions between buoys and the platform are suppressed, the absolute motion of buoys varies depending on their respective locations within the system and ocean waves. This variation is deeply influenced by the interaction between incident, reflected and diffracted waves within the system.

Xintao Liu, Min Chen, Christophe Claramunt et al.

Geographic information science in the era of geospatial big data: A cyberspace perspective Xintao Liu,1 Min Chen,2,21,* Christophe Claramunt,3 Michael Batty,4 Mei-Po Kwan,5 Ahmad M. Senousi,6 Tao Cheng,7 Josef Strobl,8 Cöltekin Arzu,9 John Wilson,10 Temenoujka Bandrova,11 Milan Konecny,2,12 Paul M. Torrens,13 Fengyuan Zhang,1 Li He,14 Jinfeng Wang,15 Carlo Ratti,16 Olaf Kolditz,17 Alexander Klippel,18 Songnian Li,19 Hui Lin,20 and Guonian L€ u2,21,* 1Department of Land Surveying and Geo-Informatics, Hong Kong Polytechnic University, Hong Kong SAR, Kowloon, China 2Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing 210023, China 3The French Naval Academy Research Institute, 29240 Lanveoc-Poulmic, France 4The Bartlett Centre for Advanced Spatial Analysis (CASA), University College London (UCL), WC1E 6BT London, UK 5Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong SAR, Shatin 999077, China 6Department of Civil Engineering, Cairo University, 12613 Cairo, Egypt 7SpaceTimeLab, Department of Civil, Environmental and Geomatic Engineering, University College London (UCL), WC1E 6BT London, UK 8Department of Geoinformatics, University of Salzburg, 5020 Salzburg, Austria 9Institute of Interactive Technologies (IIT), University of Applied Sciences and Arts Northwestern Switzerland FHNW, 5210 Windisch, Switzerland 10Spatial Sciences Institute, University of Southern California, Los Angeles, CA 90007, USA 11Laboratory on Cartography, Department of Photogrammetry and Cartography, University of Architecture, Civil Engineering and Geodesy, 1164 Sofia, Bulgaria 12Laboratory on Geoinformatics and Cartography, Department of Geography, Masaryk University, 61137 Brno, Ceská Republika 13Computer Science and Engineering, Tandon School & Center for Urban Science + Progress, New York University, New York, NY 10012, USA 14School of Humanities and Social Science, Xi’an Jiaotong University, Xi’an 710049, China 15Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China 16Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA 02139, USA 17Centre for Environmental Research, Helmholtz Centre for Environmental Research–UFZ, 04318 Leipzig, Germany 18Laboratory of Geo-information Science and Remote Sensing, Wageningen University & Research, Wageningen 6708, the Netherlands 19Department of Civil Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada 20School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China 21Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China *Correspondence: chenmin0902@njnu.edu.cn (M.C.); gnlu@njnu.edu.cn (G.L.) Received: April 20, 2022; Accepted: June 29, 2022; Published Online: July 6, 2022; https://doi.org/10.1016/j.xinn.2022.100279 a 2022 The Author(s). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Citation: Liu X., Chen M., Claramunt C., et al., (2022). Geographic information science in the era of geospatial big data: A cyberspace perspective. The Innovation 3(5), 100279.

Kenneth M. Golden, N. Benjamin Murphy, Daniel Hallman et al.

Abstract. Polar sea ice is a critical component of Earth’s climate system. As a material, it is a multiscale composite of pure ice with temperature-dependent millimeter-scale brine inclusions, and centimeter-scale polycrystalline microstructure which is largely determined by how the ice was formed. The surface layer of the polar oceans can be viewed as a granular composite of ice floes in a sea water host, with floe sizes ranging from centimeters to tens of kilometers. A principal challenge in modeling sea ice and its role in climate is how to use information on smaller-scale structures to find the effective or homogenized properties on larger scales relevant to process studies and coarse-grained climate models. That is, how do you predict macroscopic behavior from microscopic laws, like in statistical mechanics and solid state physics? Also of great interest in climate science is the inverse problem of recovering parameters controlling small-scale processes from large-scale observations. Motivated by sea ice remote sensing, the analytic continuation method for obtaining rigorous bounds on the homogenized coefficients of two-phase composites was applied to the complex permittivity of sea ice, which is a Stieltjes function of the ratio of the permittivities of ice and brine. Integral representations for the effective parameters distill the complexities of the composite microgeometry into the spectral properties of a self-adjoint operator like the Hamiltonian in quantum physics. These techniques have been extended to polycrystalline materials, advection diffusion processes, and ocean waves in the sea ice cover. Here we discuss this powerful approach in homogenization, highlighting the spectral representations and resolvent structure of the fields that are shared by the two-component theory and its extensions. Spectral analysis of sea ice structures leads to a random matrix theory picture of percolation processes in composites, establishing parallels to Anderson localization and semiconductor physics and providing new insights into the physics of sea ice.

Ryan W. Pfeifle, Barry Rothberg, Kimberly A. Weaver et al.

Theoretical studies predict that the most significant growth of supermassive black holes (SMBHs) occurs in late-stage mergers, coinciding with the manifestation of dual active galactic nuclei (AGNs), and both major and minor mergers are expected to be important for dual AGN growth. In fact, dual AGNs in minor mergers should be signposts for efficient minor-merger-induced SMBH growth for both the more and less massive progenitor. We identified two candidate dual AGNs residing in apparent minor mergers with mass ratios of ∼1:7 and ∼1:30. Sloan Digital Sky Survey (SDSS) fiber spectra show broad and narrow emission lines in the primary nuclei of each merger while only a narrow [O iii ] emission line and a broad and prominent H α /[N ii ] complex is observed in the secondary nuclei. The FWHMs of the broad H α lines in the primary and secondary nuclei are inconsistent in each merger, suggesting that each nucleus in each merger hosts a Type 1 AGN. However, spatially resolved Large Binocular Telescope optical spectroscopy reveals rest-frame stellar absorption features, indicating the secondary sources are foreground stars and that the previously detected broad lines are likely the result of fiber spillover effects induced by the atmospheric seeing at the time of the SDSS observations. This study demonstrates for the first time that optical spectroscopic searches for Type 1/Type 1 pairs similarly suffer from fiber spillover effects as has been observed previously for Seyfert 2 dual AGN candidates. The presence of foreground stars may not have been clear if an instrument with more limited wavelength range or limited sensitivity had been used.

Selçuk Nas

Kyeong-Tae Lee, Garance Perrois, Hyun-Sung Yang et al.

This study was carried out to determine the levels of resistance and resilience of kelp forests to large-scale physical disturbances. Our study site, Seongsan, Jeju Island, was impacted by super typhoon ‘Hinnamnor’. Before the typhoon, Seongsan had shown high ecosystem stability. Our results indicated that the ecological stability of a kelp forest facing a severe typhoon is strongly linked to the prevailing environmental conditions. Although typhoon impact resulted in a significant loss of brown macroalgae canopy, <i>Ecklonia cava</i> remained dominant within the kelp forest community. Resistance and resilience levels strongly depended on water temperature and movement and presence of turf-forming algae. Hence, hydrodynamic and biological factors strongly influence the overall stability of a kelp forest. We also report the first occurrences of a scleractinian coral species (i.e., <i>Montipora millepora</i>) at Seongsan, which became visible after canopy loss following the typhoon. Our findings provide valuable ecological information about the benthic community of kelp-dominated ecosystems and are essential to mitigate the impacts of expected climate change-driven rises in seawater temperature and the frequency of super typhoons.

Nuria Navarro, Inmaculada Rodríguez-Santalla

Coastal wetlands are valuable and sensitive environments that are among the most productive yet highly threatened systems in the world [...]

Sung Youn Boo, Steffen Allan Shelley, Seung-Ho Shin et al.

There has been growing interest recently in hybrid installations integrating the offshore wind farm and aquaculture farm as co-existence while optimizing ocean space use. The offshore marine farms beyond coastal or sheltered areas will require mooring to ensure the station-keeping of the farm system during the storms. In the present work, a sub-surface longline farm is installed in a fixed offshore wind farm at a distance from the wind foundations. The farm is designed to cultivate oysters in multi-compartment bags attached to the longlines vertically. The farm with a cultivating area of 200 m × 200 m is supported by the various farm lines made of polypropylene and buoys that is moored with catenary mooring arrangements. Drag coefficients of a full-scale oyster bag in wave and current are determined using the results of wave basin tests. A lumped model is developed and validated with a complete model for a partial farm. The lumped model is used to simulate the coupled responses of the whole farm in the site extreme waves and currents of a 50-year return period. The strength and fatigue designs of the mooring and farm lines are evaluated against the industry standards and confirmed to comply with the design requirements.

Chenfang Wang, Longxi Han, Yi Zhang et al.

The natural dispersion of oil depends on the oil types, wave-mixing energy, and the temperature and salinity of water. Laboratory experiments were conducted to investigate the effects of these factors on oil dispersion. The results demonstrated that the increase in temperature significantly enhanced the oil dispersion efficiency, particularly for low-viscosity oils. At 30 °C, the dispersion efficiency is 2 times higher than that at 15 °C, while salinity has no significant effect on dispersion efficiency. Nonlinear fitting results revealed an exponential increase in dispersion efficiency with the energy dissipation rate. Furthermore, partial correlation analysis was employed to examine the effects of oil density, viscosity, and surface tension on dispersion efficiency. The results indicated a high correlation between density, viscosity, and dispersion efficiency (<i>r</i> = −0.801, <i>r</i> = −0.812), whereas the correlation coefficient of surface tension was low (<i>r</i> = −0.286). Based on these findings, linear and nonlinear regression models were established between dispersion efficiency and density and viscosity, enabling a rough estimation of oil spill dispersion efficiency under low sea state conditions.

Tai Le Quy, Gunnar Friege, Eirini Ntoutsi

Ensuring fairness is essential for every education system. Machine learning is increasingly supporting the education system and educational data science (EDS) domain, from decision support to educational activities and learning analytics. However, the machine learning-based decisions can be biased because the algorithms may generate the results based on students' protected attributes such as race or gender. Clustering is an important machine learning technique to explore student data in order to support the decision-maker, as well as support educational activities, such as group assignments. Therefore, ensuring high-quality clustering models along with satisfying fairness constraints are important requirements. This chapter comprehensively surveys clustering models and their fairness in EDS. We especially focus on investigating the fair clustering models applied in educational activities. These models are believed to be practical tools for analyzing students' data and ensuring fairness in EDS.

Balaram Panda

Data Science is a modern Data Intelligence practice, which is the core of many businesses and helps businesses build smart strategies around to deal with businesses challenges more efficiently. Data Science practice also helps in automating business processes using the algorithm, and it has several other benefits, which also deliver in a non-profitable framework. In regards to data science, three key components primarily influence the effective outcome of a data science project. Those are 1.Availability of Data 2.Algorithm 3.Processing power or infrastructure

The AXIS Time-Domain, Multi-Messenger Science Working Group, : et al.

The Advanced X-ray Imaging Satellite (AXIS) promises revolutionary science in the X-ray and multi-messenger time domain. AXIS will leverage excellent spatial resolution (<1.5 arcsec), sensitivity (80x that of Swift), and a large collecting area (5-10x that of Chandra) across a 24-arcmin diameter field of view to discover and characterize a wide range of X-ray transients from supernova-shock breakouts to tidal disruption events to highly variable supermassive black holes. The observatory's ability to localize and monitor faint X-ray sources opens up new opportunities to hunt for counterparts to distant binary neutron star mergers, fast radio bursts, and exotic phenomena like fast X-ray transients. AXIS will offer a response time of <2 hours to community alerts, enabling studies of gravitational wave sources, high-energy neutrino emitters, X-ray binaries, magnetars, and other targets of opportunity. This white paper highlights some of the discovery science that will be driven by AXIS in this burgeoning field of time domain and multi-messenger astrophysics.

Qiang Zhang, Lei Xu, Xiangyu Zhang et al.

R. Licata, P. Mehta, D. Weimer et al.

Machine learning (ML) models are universal function approximators and—if used correctly—can summarize the information content of observational data sets in a functional form for scientific and engineering applications. A benefit to ML over parametric models is that there are no a priori assumptions about particular basis functions which can potentially limit the phenomena that can be modeled. In this work, we develop ML models on three data sets: the Space Environment Technologies High Accuracy Satellite Drag Model (HASDM) density database, a spatiotemporally matched data set of outputs from the Jacchia‐Bowman 2008 Empirical Thermospheric Density Model (JB2008), and an accelerometer‐derived density data set from CHAllenging Minisatellite Payload (CHAMP). These ML models are compared to the Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar (NRLMSIS 2.0) model to study the presence of post‐storm cooling in the middle‐thermosphere. We find that both NRLMSIS 2.0 and JB2008‐ML do not account for post‐storm cooling and consequently perform poorly in periods following strong geomagnetic storms (e.g., the 2003 Halloween storms). Conversely, HASDM‐ML and CHAMP‐ML do show evidence of post‐storm cooling indicating that this phenomenon is present in the original data sets. Results show that density reductions up to 40% can occur 1–3 days post‐storm depending on the location and strength of the storm.