bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development of 15MW Floating Offshore Wind Turbine Substructure Customized to Korean Shipyard
Authors: Baquet Aldric , Kim Kyong Hwan , Kim Jang , Park Sewan , Ha Yoon Jin , Lee Hyungtae , Shen Zhirong , Kyoung Johyun 
Published: January 2023

Abstract
As Republic of Korea (Korea, hereafter) moves towards “net zero carbon emission” by 2050, wind power generation is considered as one of the key elements to achieve the goal. Korean government plans to reach 12GW installed offshore wind capacity by 2030. The plan includes the construction of a 6GW floating offshore wind farm in the Dong-Hae area off the coast of Ulsan in water depths ranging from 120 to 180m.
A floating offshore wind turbine substructure is being developed with a focus on the Korean market. It consists of semisubmersible type platform with three columns of pentagonal shapes connected by three pontoons. The design philosophy is to have a construction-friendly structure for smaller Korean shipyards, minimal requirement for offshore installation vessel fleet and a robust and passive design that requires minimum maintenance over the operation period. The column sections are similar to that of small fishing vessels and pontoon section is similar to barge, both of which are familiar to the small Korean shipyards for construction. Hull structures are optimized to support a 15MW wind turbine safely through a thorough time-domain response-based structural analyses and assessment.
This paper presents the key features and functionalities of the design, an overview of the hull and mooring performance and a preliminary construction and installation plan.
Numerical Simulations of Offshore Wind Turbine Component Lifts to Determine Installation Operability
Authors: Sreenivasan Atheendra , Voogt Arjan 
Published: January 2023

Abstract
The U.S. plans to install 30GW of offshore wind by 2030. However, the lack of Jones-Act compliant installation vessels means that alternative installation strategies will have to be used in the near to mid-term. One such strategy is to use Jones-Act classed barges or purpose built ships to transport Wind Turbine Generator (WTG) components from port to the offshore location where a foreign vessel can install them. This paper uses numerical simulations to derive the uptime of such an operation for different feeder solutions.
Numerical Analysis of the Semisubmersible Using Generating-Absorbing Boundary Conditions
Authors: Ramesh Reddy Likhitha , Viré Axelle 
Published: January 2023

Abstract
The present work tests the performance of an efficient boundary condition called generating-absorbing boundary condition (GABC) for free-surface waves and studies its effect on computational efficiency. Numerical simulations of a semisubmersible substructure are performed with two different boundary conditions with low spurious reflection: relaxation zone and GABC. These simulations are conducted for regular sea states in OpenFOAM and have been validated using experimental data obtained from the OC6 campaign. The numerical results with relaxation zone boundary condition and GABC agreed well with the experimental results. Using GABC also resulted in an almost 15% reduction in computational time compared to the conventional approaches.
Design and Analysis of a Floating-Wind Shallow-Water Mooring System Featuring Polymer Springs
Authors: Lozon Ericka , Hall Matthew , McEvoy Paul , Kim Seojin , Ling Bradley 
Published: January 2023

Abstract
This paper presents a mooring system design featuring polymer springs for the VolturnUS-S 15-MW reference floating wind turbine in site conditions for the New York Bight at a 50-m water depth. Polymer springs have a nonlinear stress-strain curve that allows a stiffer response at low loads and a more flexible response at higher loads, potentially reducing peak mooring line tensions. The mooring dynamics model MoorDyn has been extended to model springs with nonlinear tension-strain curves from a user-inputted look-up table. This MoorDyn modeling advancement is verified against OrcaFlex simulation results. Using MoorDyn’s updated capabilities, a spring-equipped catenary mooring system is designed for the 15-MW floating system, along with a baseline catenary mooring system that does not use polymer springs. The floating wind turbine simulator OpenFAST is used to simulate the mooring systems in design-driving load cases to show the effect of polymer springs on key dynamic behaviours. The results show that the spring-equipped design reduces peak tensions by up to 60%, whereas the turbine offsets stay within a maximum of 7.2 m, which is still a reasonable offset limit for cable considerations. The reduction in peak tensions results in a significant decrease in damage equivalent loads — on the order of 50% for upwind lines in fully loaded conditions. These results show that polymer springs can effectively reduce peak tensions and fatigue loads in mooring systems at shallow water depths.
Stochastic Stability of Offshore Wind Turbine Blades Influenced by Rotationally Sampled Turbulence Perturbations
Authors: Caracoglia Luca 
Published: January 2023

Abstract
This study examines the stochastic stability of large, offshore wind turbine blades in the presence of flow turbulence. The problem is relevant to the design and reliability of future offshore wind energy structures. The formulation exploits a previously derived model to investigate mean square flutter stability. The formulation uses rotationally sampled turbulence to describe incoming flow perturbations. Although some assumptions are introduced in this preliminary study, the paper demonstrates the suitability of the proposed methodology; the solution is enabled via stochastic differential equations, numerically solved. A benchmark blade of a horizontal-axis offshore wind turbine is employed as the first application example.
Empire Wind Model Tests - Wave Loads and Responses
Authors: Alsos Hagbart S. , Ravinthrakumar Senthuran , Økland Ole David , Pâkozdi Csaba , Rolland Louise Ankerstjerne , Nordheim Knut 
Published: January 2023

Abstract
The following paper presents key findings from the Empire Wind monopile model tests. The tests were performed at SINTEF Oceans test facilities in late 2021 and early 2022 as part of FEED and early Detailed Engineering. Due to harsh weather conditions, with the potential for tropical hurricanes, combined with large monopile WTGs (Wind Turbine Generator), an extensive hydrodynamic test campaign has been conducted. The reason for this has been to test and validate the loads and response modes during extreme wave conditions over a variation of highly nonlinear sea states. This further allows for comparison with numerical analyses applied in both FEED and detailed design. As the Empire Wind WTGs are in waters occasionally visited by hurricanes, the attention to extreme events in a low damping configuration has been of particular interest. This implies global response due to ringing and slamming, as well as local effects from runup. Providing an accurate and representative description of such responses, is important. This is particularly true when dynamic properties of the WTGs are moved closer to the wave frequent area. This has also called for more advanced numerical methods to capture both the nonlinear irregular waves and the global structural response. The paper gives and introduction to both the model tests, and comparisons with the numerical analyses. Focus is placed on wave responses from selected sea states along the 50 and 500 year contours.
Semi-Supervised CNN-Based SVDD Anomaly Detection for Condition Monitoring of Wind Turbines
Authors: Peng Dandan , Liu Chenyu , Desmet Wim , Gryllias Konstantinos 
Published: January 2023

Abstract
Anomaly Detection (AD) can play a key role in condition monitoring of wind turbines, aiming to detect faults as early as possible, improving their reliability and the efficiency of power generation, while reducing downtime and maintenance costs. Supervisory Control and Data Acquisition (SCADA) systems are widely used, capturing large amounts of status data. Effectively extracting useful information from these massive data is critical. At present, AD methods are mainly divided into shallow kernel and deep-based methods. Deep AD methods show more promising results when applied on large datasets. Typically, AD methods are based on a large number of unlabeled data. However, sometimes in addition to the large number of unlabeled samples, a small number of labeled samples is also available, which can be used to build an improved model. In this paper a semi-supervised AD method based on Convolutional Neural Networks (CNN) and Support Vector Data Description (SVDD) is proposed to automatically monitor the status of wind turbines. The proposed method is validated on a wind turbines’ SCADA dataset, including ice blade events. Extensive experimental results demonstrate that the proposed method achieve better AD performance compared to a state-of-the-art unsupervised Deep SVDD AD method.
Hydrodynamic Effects of Surface Roughness on Cylinders: Literature Review and Research Gaps
Authors: Maduka Maduka , Coughlan Katherine , Schoefs Franck , Thiagarajan Krish , Arwade Sanjay , Bates Alison 
Published: January 2023

Abstract
In recent years, several studies have been performed to assess the damages caused by marine biofouling. Marine biofouling (or marine growth) generally refers to the settlement and growth of unwanted aquatic organisms on human-made structures situated in marine and estuarine environments. Regarding the continued demonstration of energy resource potential and a promising area of research by offshore wind turbines (OWTs), this paper provides a review of biofouling phenomena in the context of underwater cylindrical components of offshore/marine structures. Most floating wind turbine installations are located in moderate water depths between 50 m to 100 m. At these depths, biofouling can be seen on a large section of cylindrical structures, including mooring lines or power cables, with considerable roughness. The proposed review will specifically highlight various marine fouling parameters and laboratory approaches employed by researchers in modelling biofouling, and its effects on hydrodynamic loading due to wave and current excitation. Most previous experimental research assumed that biofouling effects are a function of surface roughness that is either uniform or nearly uniform and that the stationary roughened cylinder is fully covered. Some other studies, however, have proven that the surface roughness alone cannot precisely characterize marine growth; other marine fouling parameters such as roughness geometry, surface coverage ratio, facility testing set-up, biofouling species, and colonization pattern can all have a significant impact on the hydrodynamic force coefficients. To highlight knowledge gaps and research trends on collective influential aspects of biofouling to date. This report went on to explore the challenges in modelling biofouling due to its intrinsic randomness and uncertainty, as well as suggestions for many studies on marine fouling that are currently absent.
A Highly Efficient and Rapid Cost-Optimization Framework for Offshore Wind Turbine Foundations for an Entire Windfarm Site
Authors: Nielsen Martin Bjerre , Hindhede Dennis , Palmer Matthew , Leblanc Thilsted Christian 
Published: January 2023

Abstract
The present paper presents a framework for holistic optimization of monopile foundations for a full offshore windfarm. The framework has been developed in-house at Wood Thilsted Partners and has been used for design of several full-scale wind farms including Vineyard Wind, the first commercial scale wind farm in the US. The backbone of the code is a beam finite-element based structural analysis code that performs all necessary design checks according to the governing standards. A direct coupling to various 3D FE-software packages is in place for detailed analyses of non-trivial structural details such as bolted flange connections and welded attachments. Three dimensional FE is also used for position-specific 3D soil analysis for calibration and validation of the non-linear soil-structure interaction springs serving as boundary conditions for the model. Installation and drivability assessments are similarly included in the design flow based on the beam model and validated subsequently via parameterized 3D models. Everything is integrated in a fully automated optimization framework that can perform a full optimization sweep including automatic generation of design reports and drawing material ready for certification for a full windfarm of several hundred individual positions in a few hours. Representative examples illustrate how the framework provides the possibility for conducting large scale sensitivity studies, such as diameter and clustering studies allowing for holistic cost-optimization across all positions in a windfarm.
Trojan-horse silk fibroin nanocarriers loaded with a re-call antigen to redirect immunity against cancer
Authors: Bari Elia , Ferrera Francesca , Altosole Tiziana , Perteghella Sara , Mauri Pierluigi , Rossi Rossana , Passignani Giulia , Mastracci Luca , Galati Martina , Astone Giuseppina Iliana , Mastrogiacomo Maddalena , Castagnola Patrizio , Fenoglio Daniela , Di Silvestre Dario , Torre Maria Luisa , Filaci Gilberto 
Published: January 2023

BackgroundThe current challenge for immunotherapies is to generate effective antitumor immunity. Since tumor immune escape mechanisms do not impact pre-existing and consolidated immune responses, we tested the hypothesis of redirecting a pregenerated immunity to cancer: to recall a non-tumor antigen response against the tumor, silk fibroin nanoparticles (SFNs) have been selected as ‘Trojan-horse’ carriers, promoting the antigen uptake by the tumor cells.MethodsSFNs have been loaded with either ovalbumin (OVA) or CpG oligonucleotide (CpG) as antigen or adjuvant, respectively. In vitro uptake of SFNs by tumor (B16/F10 melanoma and MB49 bladder cancer) or dendritic cells, as well as the presence of OVA-specific T cells in splenic and tumor-infiltrating lymphocytes, were assessed by cytometric analyses. Proof-of-concept of in vivo efficacy was achieved in an OVA-hyperimmune B16/F10 murine melanoma model: SFNs-OVA or SFNs-CpG were injected, separately or in association, into the subcutaneous peritumoral area. Cancer dimensions/survival time were monitored, while, at the molecular level, system biology approaches based on graph theory and experimental proteomic data were performed.ResultsSFNs were efficiently in vitro uptaken by cancer and dendritic cells. In vivo peritumor administration of SFNs-OVA redirected OVA-specific cytotoxic T cells intratumorally. Proteomics and systems biology showed that peritumoral treatment with either SFNs-OVA or SFNs-CpG dramatically modified tumor microenvironment with respect to the control (CTR), mainly involving functional modules and hubs related to angiogenesis, inflammatory mediators, immune function, T complex and serpins expression, redox homeostasis, and energetic metabolism. Both SFNs-OVA and SFNs-CpG significantly delayed melanoma growth/survival time, and their effect was additive.ConclusionsBoth SFNs-OVA and SFNs-CpG induce effective anticancer response through complementary mechanisms and show the efficacy of an innovative active immunotherapy approach based on the redirection of pre-existing immunity against cancer cells. This approach could be universally applied for solid cancer treatments if translated into the clinic using re-call antigens of childhood vaccination.
Palliative care and COVID-19: a bibliometric analysis
Authors: Bernardis Alessandra , Gonzalez-Jaramillo Valentina , Ebneter Andreas S , Eychmüller Steffen 
Published: January 2023

ObjectiveTo assess the impact of COVID-19 on the palliative care (PC) publication trend in the last 10 years and the collaboration between countries and main topics that were discussed in the papers.MethodsWe used Scopus to identify publications on PC between 2012 and 2021 and publications about PC and COVID-19 between 2020 and 2021. We used VOSviewer to assess the main topics using the keywords from the papers and to assess country collaboration.Results1937 publications resulted. An increase in publications about PC was observed during the pandemic, only partially explained by OVID-19-related publications. Cancer-related PC publications were the ones with the most marked increase. We identified six clusters in the distribution of the keywords: bioethics, cancer, nursing home/telemedicine, public health, caring and PC following the WHO definition. The countries with higher number of publications were the United States and England.ConclusionWe showed an increase in the number of PC publications in the last 2 years that was only partially explained by COVID-19-related publications. Most of the publications increase was due to cancer-related publications, since, during the time of the pandemic, publications on cancer and PC increased markedly, while those on heart failure, lung disease and dementia, remained constant.
Testing and improving the performance of protein thermostability predictors for the engineering of cellulases
Authors: Dotsenko Anna , Denisenko Jury , Osipov Dmitrii , Rozhkova Aleksandra , Zorov Ivan , Sinitsyn Arkady 
Published: January 2023

Thermostability of cellulases can be increased through amino acid substitutions and by protein engineering with predictors of protein thermostability. We have carried out a systematic analysis of the performance of 18 predictors for the engineering of cellulases. The predictors were PoPMuSiC, HoTMuSiC, I-Mutant 2.0, I-Mutant Suite, PremPS, Hotspot, Maestroweb, DynaMut, ENCoM ([Formula: see text] and [Formula: see text], mCSM, SDM, DUET, RosettaDesign, Cupsat (thermal and denaturant approaches), ConSurf, and Voronoia. The highest values of accuracy, F-measure, and MCC were obtained for DynaMut, SDM, RosettaDesign, and PremPS. A combination of the predictors provided an improvement in the performance. F-measure and MCC were improved by 14% and 28%, respectively. Accuracy and sensitivity were also improved by 9% and 20%, respectively, compared to the maximal values of single predictors. The reported values of the performance of the predictors and their combination may aid research in the engineering of thermostable cellulases as well as the further development of thermostability predictors.
Performance of Statistical and Machine Learning Risk Prediction Models for Surveillance Benefits and Failures in Breast Cancer Survivors
Authors: Su Yu-Ru , Buist Diana S.M. , Lee Janie M. , Ichikawa Laura , Miglioretti Diana L. , Bowles Erin J. Aiello , Wernli Karen J. , Kerlikowske Karla , Tosteson Anna , Lowry Kathryn P. , Henderson Louise M. , Sprague Brian L. , Hubbard Rebecca A. 
Published: January 2023

AbstractBackground:Machine learning (ML) approaches facilitate risk prediction model development using high-dimensional predictors and higher-order interactions at the cost of model interpretability and transparency. We compared the relative predictive performance of statistical and ML models to guide modeling strategy selection for surveillance mammography outcomes in women with a personal history of breast cancer (PHBC).Methods:We cross-validated seven risk prediction models for two surveillance outcomes, failure (breast cancer within 12 months of a negative surveillance mammogram) and benefit (surveillance-detected breast cancer). We included 9,447 mammograms (495 failures, 1,414 benefits, and 7,538 nonevents) from years 1996 to 2017 using a 1:4 matched case–control samples of women with PHBC in the Breast Cancer Surveillance Consortium. We assessed model performance of conventional regression, regularized regressions (LASSO and elastic-net), and ML methods (random forests and gradient boosting machines) by evaluating their calibration and, among well-calibrated models, comparing the area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CI).Results:LASSO and elastic-net consistently provided well-calibrated predicted risks for surveillance failure and benefit. The AUCs of LASSO and elastic-net were both 0.63 (95% CI, 0.60–0.66) for surveillance failure and 0.66 (95% CI, 0.64–0.68) for surveillance benefit, the highest among well-calibrated models.Conclusions:For predicting breast cancer surveillance mammography outcomes, regularized regression outperformed other modeling approaches and balanced the trade-off between model flexibility and interpretability.Impact:Regularized regression may be preferred for developing risk prediction models in other contexts with rare outcomes, similar training sample sizes, and low-dimensional features.
Blocking Genomic Instability Prevents Acquired Resistance to MAPK Inhibitor Therapy in Melanoma
Authors: Dharanipragada Prashanthi , Zhang Xiao , Liu Sixue , Lomeli Shirley H. , Hong Aayoung , Wang Yan , Yang Zhentao , Lo Kara Z. , Vega-Crespo Agustin , Ribas Antoni , Moschos Stergios J. , Moriceau Gatien , Lo Roger S. 
Published: January 2023

AbstractBlocking cancer genomic instability may prevent tumor diversification and escape from therapies. We show that, after MAPK inhibitor (MAPKi) therapy in patients and mice bearing patient-derived xenografts (PDX), acquired resistant genomes of metastatic cutaneous melanoma specifically amplify resistance-driver, nonhomologous end-joining (NHEJ), and homologous recombination repair (HRR) genes via complex genomic rearrangements (CGR) and extrachromosomal DNAs (ecDNA). Almost all sensitive and acquired-resistant genomes harbor pervasive chromothriptic regions with disproportionately high mutational burdens and significant overlaps with ecDNA and CGR spans. Recurrently, somatic mutations within ecDNA and CGR amplicons enrich for HRR signatures, particularly within acquired resistant tumors. Regardless of sensitivity or resistance, breakpoint–junctional sequence analysis suggests NHEJ as critical to double-stranded DNA break repair underlying CGR and ecDNA formation. In human melanoma cell lines and PDXs, NHEJ targeting by a DNA-PKCS inhibitor prevents/delays acquired MAPKi resistance by reducing the size of ecDNAs and CGRs early on combination treatment. Thus, targeting the causes of genomic instability prevents acquired resistance.Significance:Acquired resistance often results in heterogeneous, redundant survival mechanisms, which challenge strategies aimed at reversing resistance. Acquired-resistant melanomas recurrently evolve resistance-driving and resistance-specific amplicons via ecDNAs and CGRs, thereby nominating chromothripsis–ecDNA–CGR biogenesis as a resistance-preventive target. Specifically, targeting DNA-PKCS/NHEJ prevents resistance by suppressing ecDNA/CGR rearrangements in MAPKi-treated melanomas.This article is highlighted in the In This Issue feature, p. 799