bluuebunny/crossref_metadata_2025_split huggingface dataset:
Influence of the Semi-Submersible Platform Flexibility on the Dynamic Response of the Wind Turbine
Authors: Sizova Sofya , Maillot Elise , Moreau Suzanne , Féron Marie 
Published: October 2022

Abstract
As the offshore wind turbines increase in size, the structural flexibility of the tower, the blades and the platforms are becoming more significant and may impact the dynamic response of the wind turbine. To assess properly the design life of a floating wind turbine, the modal analysis needs then to consider the interaction between all elements.
In this study, a modal analysis of the IEA 15 MW wind turbine on a floating NereWind™ semi-submersible support is performed with finite element modelling and the hydro-structure calculation software HOMER. The natural modes of the tower are calculated in several configurations to study the impact of the local stiffness variations, the global platform flexibility, the added mass and the hydrostatic stiffness.
This work will allow floating offshore wind farms developers and floating concept designers to understand better the impact of the structural and geometrical arrangement on the modal turbine behaviour.
Wave Record Gap-Filling Using a Low-Rank Tensor Completion Model
Authors: Chen Jiaxin , Ashton Ian G. C. , Pillai Ajit C. 
Published: October 2022

Abstract
The offshore wind farm industry has identified further refinement of marine operations as necessary to realize the lower strike prices seen in recent subsidy auctions. This requires extending working times by taking advantage of weather windows, even when operating in more remote sites. Key to this will be increasing the accuracy of forecasts and live metocean data from site for effective and safe operation scheduling. In recent years, statistical models or deep learning-based models, by learning spatial and temporal patterns in observations have shown their potential to support or even partly replace numerical weather modelling to provide accurate forecasts.
The present paper implements a machine learning approach utilizing a nonconvex low-rank tensor completion considering truncated nuclear norm algorithm (LRTC-TNN) which can characterize the spatial and temporal dependencies rooted in the data to fill gaps in measurement data from wave buoys. The performance is assessed by manually masking valid data thereby introducing three types of missing entries. The proposed method is shown to successfully fill datasets missing up to 20% of the data with R2 values exceeding 0.9. The results are also compared against other intuitive methods including linear interpolation, cubic spline interpolation, and mean historical. Compared to these, the present method is shown to have more reasonable trends. Finally, sensitivity considering the ratio of missing data, the type of missing data, and two hyper-parameters of the algorithm were compared to characterize their impacts on the results and advise potential improvements.
This work demonstrates multivariate time series gap-filling with arbitrary missing ratio and sensor availability that can be extended and deployed to sensor networks for possible forecasting applications.
Hydrodynamic Performance of a Vertical-Axis Tidal Current Turbine in Surge Motion Using a 2-D Vortex Panel Model
Authors: Perassi Federica , Jarquin Laguna Antonio , Simao Ferreira Carlos 
Published: October 2022

Abstract
Under real sea conditions, floating vertical-axis tidal current turbines experience motions in six degrees of freedom which impact the hydrodynamic performance. In this paper, a 2D free-wake vortex panel method (U2DiVA) is adopted as a time effective tool to assess the hydrodynamic response of an existing floating vertical-axis tidal turbine under platform’s surge motion in uniform tidal current. The results of the numerical simulations show that the surge motion modifies the flow field perceived at the blades, affecting the instantaneous value of the tip speed ratio and the time evolution of the induction field. Consequently, surge motion impacts on the structural strength of the turbine by introducing multiple frequencies of oscillation of the hydrodynamic coefficients and increasing the peak loading on the rotor. Surge motion is found to be beneficial to the average power extraction while only marginally affecting the mean loading. The preliminary findings of this research provide insights about critical aspects for the design and evaluation of floating vertical-axis tidal turbine systems.
Dynamic Analysis of an Integrated Structure Consists of Jacket Offshore Wind Turbine and Aquaculture Cage
Authors: Li Na , Zhao Haisheng , Shi Wei , Wang Wenhua , Li Xin 
Published: October 2022

Abstract
The intensive exploration of marine resources is announced by Chinese government. The abundant offshore wind energy in the water depth of 30–50 m shall be developed in the offshore regions in China during the next decades, which are also feasible areas for marine farming. In view of structural safety and economic benefits, a multi-utility offshore structure is proposed in the study with the intension of realizing the ocean resources with high quality exploration. The recommended offshore structure is consists of a jacket offshore wind turbine and a steel cage (JOWT-SC). In order to investigate the influence of the additional aquaculture cage, the finite element models of JOWT-SC is established in SACS, the hydrodynamic model of the aquaculture cage is equivalented as Morison model. The comparisons of the dynamic characteristics and structural responses with the original jacket offshore wind turbine (JOWT) are carried out. There are the remarkable discrepancies between the natural modes of JOWT-SC and JOWT due to the added aquaculture cage. Meanwhile, the remarkably increased hydrodynamic forces on JOWT-SC under wave and current loads are observed. The influence of aquaculture cage on the dynamic characteristics and hydrodynamics of JOWT-SC are discussed.
The late Mesoproterozoic to early Neoproterozoic Grenvillian orogeny and the assembly of Rodinia: Turning point in the tectonic evolution of Laurentia
Authors: Swanson-Hysell* Nicholas L. , Rivers Toby , van der Lee Suzan 
Published: October 2022

ABSTRACT
The amalgamation of Laurentia’s Archean provinces ca. 1830 Ma was followed by ~700 m.y. of accretionary orogenesis along its active southeastern margin, marked by subduction of oceanic lithosphere, formation of arcs and back-arcs, and episodic accretion. This prolonged period of active-margin tectonic processes, spanning the late Paleoproterozoic and Mesoproterozoic eras, resulted in major accretionary crustal growth and was terminated by closure of the Unimos Ocean (new name). Ocean closure was associated with rapid motion of Laurentia toward the equator and resulted in continental collision that led to profound reworking of much of the accreted Proterozoic crust during the ca. 1090–980 Ma Grenvillian orogeny. The Grenvillian orogeny resulted in formation of a large, hot, long-duration orogen with a substantial orogenic plateau that underwent extensional orogenic collapse before rejuvenation and formation of the Grenville Front tectonic zone. The Grenvillian orogeny also caused the termination and inversion of the Midcontinent Rift, which, had it continued, would likely have split Laurentia into distinct continental blocks. Voluminous mafic magmatic activity in the Midcontinent Rift ca. 1108–1090 Ma was contemporaneous with magmatism in the Southwestern Laurentia large igneous province. We discuss a potential link between prolonged subduction of oceanic lithosphere beneath southeast Laurentia in the Mesoproterozoic and the initiation of this voluminous mafic magmatism. In this hypothesis, subducted water in dense, hydrous Mg-silicates transported to the bottom of the upper mantle led to hydration and increased buoyancy, resulting in upwelling, decompression melting, and intraplate magmatism. Coeval collisional orogenesis in several continents, including Amazonia and Kalahari, ties the Grenvillian orogeny to the amalgamation of multiple Proterozoic continents in the supercontinent Rodinia. These orogenic events collectively constituted a major turning point in both Laurentian and global tectonics. The ensuing paleogeographic configuration, and that which followed during Rodinia’s extended breakup, set the stage for Earth system evolution through the Neoproterozoic Era.
Treatment outcomes and their determinants of IgG4-related ophthalmic disease: a territory-wide cohort study
Authors: Lai Kenneth K H , Li Emmy Y M , Chan Regine Y C , Chu Winnie C W , Cheng Andy C O , Chan Karen K W , Chin Joyce K Y , Kwok Jeremy S W , Io Ida Y F , Yip Nelson K F , Li Kenneth K W , Chan Wai Ho , Lam Nai Man , Yip Wilson W K , Young Alvin L , Chan Edwin , Ko Callie K L , Ko Simon T C , Yuen Hunter K L , Tham Clement C Y , Pang Chi-Pui , Chong Kelvin K L 
Published: October 2022

Background
Oral corticosteroid remains the first-line treatment of IgG4-related ophthalmic disease, but steroid-dependence is common and serious. Factors associated with steroid dependence and relapse have to be further explored.

Study population
A city-wide, biopsy-proven, Chinese cohort.

Methods
Retrospective, masked review of medical records, orbital images and histopathology reports.

Results
There were 101 patients with at least 24-month follow-up. Up to 82% (82/101) received oral corticosteroid as first-line treatments, and 7 of them received also concomitant steroid-sparing agents (SSA)/biological agents as primary treatment. There was 61% (50/82) of patients required long-term corticosteroid (alone=23, with SSA=27) after 1.9±0.7 (range 1–5) relapses. When compared with the 21% (17/82) of patients who tapered corticosteroid successfully for 24 months, steroid dependence was associated with elevated baseline serum IgG4 level (94% vs 65%, p<0.01) and Mikulicz syndrome (46% vs 18%, p<0.05). Up to 13% (11/82) of patients tolerated residual disease after tapering off corticosteroid. There was 17% (17/101) of patients did not require any medications after biopsies. They were more likely to have debulking surgeries (71% vs 40%, p<0.05), discrete orbital lesions (65% vs 26%, p<0.05), normal baseline serum IgG4 level (24% vs 6%, p<0.05) and no Mikulicz syndrome (94% vs 61%, p<0.05).

Conclusion
In this cohort, 60% of patients required long-term maintenance oral corticosteroid. Elevated pretreatment serum IgG4 level and Mikulicz syndrome were associated with steroid dependence. Debulking surgery is an alternative for a subgroup of patients with discrete orbital lesions, normal baseline IgG4 level and no Mikulicz syndrome.
Mid-Term Results of the First Dorsal Metacarpal Artery Flap for Thumb Defects
Authors: GÜNAY Ali E. , TATLISU Kadir , ÇAVUŞ Mehmet , KAHRAMAN Murat 
Published: October 2022

Background: The thumb plays an important role in the function of the hand. Sensate reconstruction of the pulp is important in restoring function to the thumb. The aim of this study is to present outcomes of a sensate islanded first dorsal metacarpal artery (FDMA) flap used for the reconstruction of skin defects of the thumb. Methods: Patients who had a FDMA flap reconstruction of thumb pulp defects in the orthopaedics and traumatology clinic of affiliated hospital were included in the study. This included eight thumbs of eight patients. The range of motion, sensation and cosmetic satisfaction in the donor and recipient areas were evaluated at a minimum of 2-year follow-up. Results: There were no partial or complete flap failures. The mean static 2-point discrimination was 15 mm and the mean Semmes–Weinstein monofilament test score was 3.93. Cortical re-orientation was observed in 65% of patients. Near normal range of motion and Kapandji scores were regained in all patients. Conclusions: Excellent motion and sensory outcomes were obtained following the FDMA flap reconstruction of thumb pulp defects. The donor morbidity of the flap is minimal, and this flap can be considered as one of the primary options for reconstruction of thumb pulp defects. Level of Evidence: Level V (Therapeutic)
A shallow-water dunefield in a microtidal, wind-dominated strait (Stintino, NW Sardinia, Italy)
Authors: Andreucci S. , Santonastaso A. , De Luca M. , Cappucci S. , Cucco A. , Quattrocchi G. , Pascucci V. 
Published: October 2022

Abstract
This study focuses on the recent evolution of a shallow-water dunefield system off La Pelosa Beach, located in the wind-dominated, microtidal, asymmetrical Asinelli Strait (Stintino, NW Sardinia, Italy). The system comprises four zones defined by a gradual decrease in current strength from the strait centre to more distal parts that form different bedforms. The strait centre (Zone A) is a substrate-carved channel passing downcurrent to a vast sandy shoal characterized by a subaqueous dunefield (Zone B) that is surrounded by a sandwave–ripple carpet (Zone C). The strait margin (Zone D) is dominated by a sandy cusp, the so-called La Pelosa Beach system. The extensive seagrass meadow located on the SE side of the strait-end zone feeds the system with bioclastic-rich sands. This carbonate sedimentary load is ensured by natural seagrass meadow retreats that are possibly boosted by human impact. The sediment transport is modulated by the interaction between two opposite currents triggered by the east-incoming Greco/Levante and NW-incoming Mistral winds. These currents experience a great variability due to multi-annual fluctuations (4–6 years) of the prevailing wind activities. Such cyclicity is responsible for periodic dune migration and and transport from La Pelosa Beach out of the system below the active part of the submerged beach (depth of closure). Bedform formations (ripples, dunes and sandwaves) and migrations within the strait are the result of complex physical processes observed in modern environments but rarely described in rock formations. The presented conceptual model can be used to recognize sediment ‘spillover’ processes on other modern, microtidal, wind-dominated straits, as well as proposing the main criteria for recognizing this strait sedimentary succession in the geological record.
CoreScore: a machine learning approach to assess legacy core condition
Authors: Fellgett Mark , Hall Alex , Harris Simon , Damaschke Magret , Kingdon Andrew 
Published: October 2022

Abstract
Today's geoscience challenges often require repurposing of data and samples from legacy boreholes. Collection of new deep core is expensive; maximizing this investment is vital. However, the condition of legacy cores varies due to factors including recovery, sampling, lithology, and storage.
Rock Quality Designation analysis is often undertaken on new core but this only provides a snapshot of core condition and will not be indicative of subsequent condition. Poor core condition can make destructive analytical techniques impossible and also impacts non-destructive techniques including core scanning.
Since 2011, BGS have systematically collected 125 000 core images. This study investigates if core condition of this archive can be assessed using automated analysis by machine learning. A neural network-based approach was used to segment these images. By differentiating imaged core from their background, properties such as number of fragments and total rock area were determined and used to assess core condition. Analysis of outputs demonstrates that with minimal input data, core condition can be rapidly assessed. This allows users to better understand and visualize core. This can be used to qualitatively assess non-destructive data, improve success of destructive sampling through targeted sampling and reduce the time and effort spent interacting with physical material.
Auditing YouTube’s Recommendation Algorithm for Misinformation Filter Bubbles
Authors: Srba Ivan , Moro Robert , Tomlein Matus , Pecher Branislav , Simko Jakub , Stefancova Elena , Kompan Michal , Hrckova Andrea , Podrouzek Juraj , Gavornik Adrian , Bielikova Maria 
Published: October 2022

In this article, we present results of an auditing study performed over YouTube aimed at investigating how fast a user can get into a misinformation filter bubble, but also what it takes to “burst the bubble,” i.e., revert the bubble enclosure. We employ a sock puppet audit methodology, in which pre-programmed agents (acting as YouTube users) delve into misinformation filter bubbles by watching misinformation-promoting content. Then they try to burst the bubbles and reach more balanced recommendations by watching misinformation-debunking content. We record search results, home page results, and recommendations for the watched videos. Overall, we recorded 17,405 unique videos, out of which we manually annotated 2,914 for the presence of misinformation. The labeled data was used to train a machine learning model classifying videos into three classes (promoting, debunking, neutral) with the accuracy of 0.82. We use the trained model to classify the remaining videos that would not be feasible to annotate manually.
Using both the manually and automatically annotated data, we observe the misinformation bubble dynamics for a range of audited topics. Our key finding is that even though filter bubbles do not appear in some situations, when they do, it is possible to burst them by watching misinformation-debunking content (albeit it manifests differently from topic to topic). We also observe a sudden decrease of misinformation filter bubble effect when misinformation-debunking videos are watched after misinformation-promoting videos, suggesting a strong contextuality of recommendations. Finally, when comparing our results with a previous similar study, we do not observe significant improvements in the overall quantity of recommended misinformation content.
Toward an Understanding of Long-tailed Runtimes of SLS Algorithms
Authors: Lorenz Jan-Hendrik , Wörz Florian 
Published: October 2022

The satisfiability problem (SAT) is one of the most famous problems in computer science. Traditionally, its NP-completeness has been used to argue that SAT is intractable. However, there have been tremendous practical advances in recent years that allow modern SAT solvers to solve instances with millions of variables and clauses. A particularly successful paradigm in this context is stochastic local search (SLS).

In most cases, there are different ways of formulating the underlying SAT problem. While it is known that the precise formulation of the problem has a significant impact on the runtime of solvers, finding a helpful formulation is generally non-trivial. The recently introduced
GapSAT
solver [Lorenz and Wörz 2020] demonstrated a successful way to improve the performance of an SLS solver on average by learning additional information, which logically entails from the original problem. Still, there were also cases in which the performance slightly deteriorated. This justifies in-depth investigations into how learning logical implications affects runtimes for SLS algorithms.

In this work, we propose a method for generating logically equivalent problem formulations, generalizing the ideas of
GapSAT
. This method allows a rigorous mathematical study of the effect on the runtime of SLS SAT solvers. Initially, we conduct empirical investigations. If the modification process is treated as random, then Johnson SB distributions provide a perfect characterization of the hardness. Since the observed Johnson SB distributions approach lognormal distributions, our analysis also suggests that the hardness is long-tailed.

As a second contribution, we theoretically prove that restarts are useful for long-tailed distributions. This implies that incorporating additional restarts can further refine
all
algorithms employing above mentioned modification technique.

Since the empirical studies compellingly suggest that the runtime distributions follow Johnson SB distributions, we also investigate this property on a theoretical basis. We succeed in proving that the runtimes for the special case of Schöning’s random walk algorithm [Schöning 2002] are approximately Johnson SB distributed.
Doubly Robust Estimation for Correcting Position Bias in Click Feedback for Unbiased Learning to Rank
Authors: Oosterhuis Harrie 
Published: October 2022

Clicks on rankings suffer from position bias: generally items on lower ranks are less likely to be examined—and thus clicked—by users, in spite of their actual preferences between items. The prevalent approach to unbiased click-based learning-to-rank (LTR) is based on counterfactual inverse-propensity-scoring (IPS) estimation. In contrast with general reinforcement learning, counterfactual doubly robust (DR) estimation has not been applied to click-based LTR in previous literature.
In this article, we introduce a novel DR estimator that is the first DR approach specifically designed for position bias. The difficulty with position bias is that the treatment—user examination—is not directly observable in click data. As a solution, our estimator uses the expected treatment per rank, instead of the actual treatment that existing DR estimators use. Our novel DR estimator has more robust unbiasedness conditions than the existing IPS approach, and in addition, provides enormous decreases in variance: our experimental results indicate it requires several orders of magnitude fewer datapoints to converge at optimal performance. For the unbiased LTR field, our DR estimator contributes both increases in state-of-the-art performance and the most robust theoretical guarantees of all known LTR estimators.
Antidiabetic Activity of Mung Bean or Vigna radiata (L.) Wilczek Seeds in Alloxan-Induced Diabetic Mice
Authors: Amare Yosef Eshetie , Dires Kassahun , Asfaw Tsegahun 
Published: October 2022

Introduction. Despite the development of oral hypoglycemic medications, diabetes and its associated complications continue to be significant clinical issues. The purpose of this study was to examine the antidiabetic effects of Vigna radiata (L.) Wilczek seeds in mice that had been given alloxan to cause diabetes. Methods. In Swiss albino mice, diabetes was brought on by a single intraperitoneal injection of the drug alloxan (150 mg/kg). For 14 days, glibenclamide (5 mg/kg) and methanol extract of V. radiata seeds (100, 200, and 400 mg/kg) were given orally. Following oral administration of V. radiata to mice, the blood glucose levels (BGL) and body weight were measured at 7 and 14 days. The mice were sacrificed at the end of the trial, and blood samples were taken for the evaluation of insulin, glycated hemoglobin, aspartate aminotransferase (AST), alanine aminotransferase (ALT), high-density lipoprotein (HDL), total cholesterol (TC), and triglyceride (TG) levels. It was determined how much glycogen was present in the liver. Additionally, the total phenolic and flavonoid contents of V. radiata were determined, along with the in vitro DPPH (2, 2 diphenyl-1-picrylhrazyl) free radical-scavenging activity.

P

< 0.05 was chosen as the cutoff for statistical significance. Results. Following oral administration of V. radiata for 14 days, diabetic mice’s BGL and bad cholesterol (TC and TG) levels significantly decreased, while HDL levels increased. Treatment with V. radiata significantly decreased the levels of AST, ALT, and glycated hemoglobin when compared with diabetes control. On the other hand, it raised insulin levels and the amount of liver glycogen. V. radiata underwent phytochemical analysis, which identified the presence of tannins, saponins, phenols, alkaloids, terpenoids, steroids, flavonoids, and glycosides. Per gram of V. radiata seed extract, the total phenolic content was 43.12 ± 3.14 mg of gallic acid equivalents, while the total flavonoid content was 38.35 ± 2.6 mg of quercetin equivalents. Ascorbic acid was shown to have an IC50 value of 18.64 µg/ml during a DPPH-scavenging assay, while V. radiata had an IC50 value of 73.35 µg/ml. Conclusion. According to the findings of the current study, the methanolic extract of the seeds from the plant V. radiata possesses significant antidiabetic characteristics that are on par with those of the commonly used drug glibenclamide. Hence, V. radiata seems to be effective as a natural antidiabetic.