bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unusual coccolithophore blooms in Scottish waters
Authors: Renshaw Richard , Bresnan Eileen , Kay Susan , McEwan Robert , Miller Peter I. , Tett Paul 
Published: September 2023

Abstract. Two unusual blooms were observed in Scottish waters during summer 2021: one in the Clyde Sea and the other by the east coast of the Shetland Islands. Both had the appearance of coccolithophore blooms. Transmission electron microscopy of a sample from the Clyde Sea confirmed the presence there of the coccolithophore Emiliania huxleyi. We examine the conditions that led to these unusual blooms. In situ data are scarce, and so we draw inference from satellite data and reanalysis. For Shetland, the bloom can be seen to originate further north on the edge of the continental shelf. It is advected south and then west towards the Shetland coast by surface currents. For the Clyde Sea region, April 2021 was the coldest April of the last 30 years (National Climate Information Centre). We hypothesise that this cold weather restricted the usual spring bloom of diatoms. A restricted spring bloom would mean higher-than-usual concentrations of nutrients in the summer. It might also mean reduced numbers of grazers. These factors would provide ideal conditions for coccolithophores to flourish as temperatures and sunlight increase.
Intense wind-driven coastal upwelling in the Balearic Islands in response to Storm Blas (November 2021)
Authors: Mourre Baptiste , Reyes Emma , Lorente Pablo , Santana Alex , Hernández-Lasheras Jaime , Hernández-Carrasco Ismael , García-Jove Maximo , Zarokanellos Nikolaos D. 
Published: September 2023

Abstract. This article analyses the Balearic Islands wind-driven coastal upwelling in
response to the intense and long-lasting Storm Blas, which affected the
western Mediterranean Sea in November 2021. The storm was associated with a
pronounced pressure low, generating heavy rains and intense winds and
showing some characteristics of a tropical cyclone. The Balearic Islands
area was particularly affected since the core of the storm was moving over a 1-week-long period from the south-west of this area to just above the islands of Menorca
and Mallorca. High-resolution regional forecast models indicated
that the intense north-easterly winds blowing over the region during the
first days of the storm led to the development of intense upwellings along
the north-western coasts of the islands of Mallorca and Ibiza together with a
reversal of the surface current. While the clouds associated with the storm
prevented the radiometers on board satellites to precisely observe the
evolution of the sea surface signature of the upwelling, signals of enhanced
chlorophyll concentration were still detected in the upwelling region. The
high-resolution Western Mediterranean Operational Prediction System model, which downscales
the Copernicus Marine Service Mediterranean predictions, is used to describe
the characteristics of this intense coastal-upwelling event, as well as to
analyse its singularity over the past-9-year time series through the
comparison of different coastal-upwelling indices. The upwelling event is
found to have a duration of 3 d (considering its effects on the sea
surface temperature) with a spatial offshore extension of around 20 km. It
was characterized by intense cold coastal sea surface anomalies of around
6 ∘C. While it was the most intense event over the past 9 years in terms
of local cross-shore sea surface temperature gradients, it is ranked second
in terms of the intensity of cross-shelf transports, behind the Storm Gloria
upwelling event in January 2020. This study demonstrates the benefits of
operational oceanography for the characterization of extreme events through
the provision of time series of high-resolution modelling results in the
coastal area.
High-frequency radar-derived coastal upwelling index
Authors: Lorente Pablo , Rubio Anna , Reyes Emma , Solabarrieta Lohitzune , Piedracoba Silvia , Tintoré Joaquín , Mader Julien 
Published: September 2023

Abstract. Coastal upwelling has been extensively studied since it plays a critical role in the connectivity between offshore waters and coastal ecosystems, which has impacts on water quality, fisheries, and aquaculture production. Significant efforts have been devoted to the quantification of the intensity, duration, and variability of this phenomenon
by means of coastal upwelling indexes (CUIs), derived from wind, sea level
pressure, or sea surface temperature data. Although valuable, first-order
descriptors, such classical indexes have been reported to present some
limitations. As one of the major shortcomings is the omission of the direct
influence of ocean circulation, this work introduces a novel CUI, generated
from remotely sensed hourly surface current observations provided by a
high-frequency radar (HFR). The consistency of the proposed index (CUI-HFR)
is assessed in two different oceanographic areas during two distinct time
periods: in the north-western Iberian (NWI) peninsula for 2021 and in the
Bay of Biscay (BOB) for 2014. To this aim, CUI-HFR is compared against a traditional CUI based on hourly wind observations (CUI-WIND)
provided by two buoys. Likewise, the skill of CUI-HFR to identify upwelling
and downwelling processes is also qualitatively evaluated. Complementarily,
the prognostic capabilities of the GLOBAL analysis and forecasting system to accurately reproduce upwelling and downwelling events in the NWI area are also analysed (CUI-GLOBAL). Results obtained in these two pilot areas
revealed the following: (i) noticeable agreement between CUI-HFR and CUI-WIND, with
correlation coefficients above 0.67; and (ii) a proven ability of CUI-HFR and
CUI-GLOBAL to categorize a variety of upwelling and downwelling episodes,
which highlights their potential applicability for direct upwelling
monitoring over any coastal area of the global ocean.
МОНИТОРИНГ ЗАРАЖЕННОСТИ РЫБ СЕМЕЙСТВА КАРПОВЫХ ЛИЧИНКАМИ OPISTHORCHIS FELINEUS НА ТЕРРИТОРИИ КОСТАНАЙСКОЙ ОБЛАСТИ
Authors: Аубакиров Марат , Н. С. Кауменов , Ш. С. Габдуллин , Е. Н. Еренко , А.М. Абдыбекова , В. Н. Домацкий 
Published: September 2023

На сегодняшний день на территории Казахстана одной из наиболее значимых проблем является распространение паразитарных заболеваний среди рыб, в частности описторхоза. В Костанайской области имеется значительное количество рек и озер, что позволет обеспечивать водой многие отрасли сельского хозяйства и промышленности, способствует развитию любительского и промышленного рыболовства. В результате чего огромное значение для рыбоводства и промышленности имеет своевременная диагностика и мониторинг таких заболеваний как лигулидоз, диплостомоз, а наибольшее эпидемиологическое значение имеют в свою очередь печеночные сосальщики трематоды семейства Opisthorchidae. В следствии чего гельминтологическая безопасность рыбных продуктов для человека, а также проблема паразитарных заболеваний влияющих на численность рыб на территории Костанайской области является на данный момент актуальной. В статье приведены сведения по эпизоотологической ситуации на территории Костанайской области по описторхозу, описаны методы осмотра, внешнего и внутреннего обследования рыбы, методы лабораторной диагностики, морфологическая характеристика и биология развития возбудителей данных заболеваний. Проведён анализ источников литературы по изучению описторхоза, распространению его возбудителя. Изучена официальная документация годовых отчетов, представленных на официальных сайтах.

Ключевые слова: описторхоз, Оpisthorchiidae, O. felineus, рыба, эпидемиология, водоемы
Hydrothermal Graphite as a Trigger for High-Temperature Orogenic Gold Mineralization at Haoyaoerhudong, Northern China
Authors: Li Wenbo , Zhang Fanghua , Qiao Xueyuan , Fu Tianyao 
Published: September 2023

Abstract
Carbonaceous materials are a key factor controlling mineralization processes in many world-class gold deposits. Haoyaoerhudong is the largest carbonaceous metasediment-hosted gold deposit on the north margin of the North China craton. Gold-bearing orebodies are hosted in carbonaceous slates and schists belonging to Mesoproterozoic rift-related successions. Typical hydrothermal minerals are pyrrhotite, quartz, biotite, graphite, apatite, titanite, and native gold. The ore mineralogy, combined with microthermometry and Raman spectra on fluid inclusions, has demonstrated three stages of hydrothermal activity: (I) quartz-biotite ± sulfide stage associated with gold mineralization (315°–510°C; ~4.8 wt % NaCl equiv; H2O-NaCl-CO2 ± CH4 ± N2 system); (II) quartz-sulfide stage, including quartz-sulfide stringers (IIa, 250°–334°C; ~5.4 wt % NaCl equiv; H2O-NaCl-CH4 ± CO2 ± N2 system) and fractured quartz-sulfide ores (IIb, 234°–308°C; ~4.1 wt % NaCl equiv; H2O-NaCl-N2 ± CH4 system); and (III) post-ore quartz-calcite stage (70°–219°C; ~4.8 wt % NaCl equiv; H2O-NaCl system). The molar ratios of CO2 and CH4 progressively decreased from stage I to II, consistent with the occurrence of graphite in alteration zones. Microscopic observation and Raman spectra suggest that the fine-grained graphite from altered schist (Gr-1/2) and coarse-sized graphite from gold-bearing veins (Gr-3/4) are of high crystallinity and exhibit characteristics indicating a hydrothermal origin. The δ13C values of graphite, varying from −27.1 to −26.0‰ Vienna-Pee Dee Belemnite (V-PDB), suggest that the carbon was of biogenetic origin. Apatite Sr isotopes (87Sr/86Sr: 0.708293–0.708842) and titanite Nd isotopes (εNdt: –11.76 to –14.84) also indicate contributions from carbonaceous rocks during mineralization.
Thermodynamic modeling demonstrates that graphite may have precipitated at Haoyaoerhudong due to cooling and reduction of the H2O-CO2-CH4 fluids at high temperatures. Graphite precipitation would significantly consume CO2 and effectively destabilize Au bisulfide complexes, facilitating the codeposition of pyrrhotite, graphite, and native gold at high temperatures (≥379°C). We infer that deposition of hydrothermal graphite is a crucial process for mesothermal-hypothermal mineralization in sediment-hosted orogenic gold deposits.
Unsupervised Ship Detection in SAR Imagery Based on Energy Density-Induced Clustering
Authors: Yuan Zifeng , Li Yu , Liu Yu , Liang Jiale , Zhang Yuanzhi 
Published: September 2023

Article
Unsupervised Ship Detection in SAR Imagery Based on Energy Density-Induced Clustering

Zifeng Yuan 1, Yu Li 1,*, Yu Liu 1, Jiale Liang 1, and Yuanzhi Zhang 2,3

1 Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2 School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 100049, China
3 Key Laboratory of Lunar and Deep Space Exploration, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, China
* Correspondence: yuli@bjut.edu.cn

Received: 6 March 2023
Accepted: 24 April 2023
Published: 26 September 2023

Abstract: Intelligent recognition of maritime ship targets from synthetic aperture radar (SAR) imagery is a hot research issue. However, interferences such as the strong sea clutter, sidelobe, small ship size and weak backscattered signal continually affect the detection results. To address this problem, a novel unsupervised machine learning-based ship detection algorithm, named energy density-induced clustering (EDIC), is proposed in this paper. It is discovered that the singular values between ship targets and interference signals are significantly different in a local region because of their various concentration degrees of signal energy intensity. Accordingly, in this study, two novel energy density features are proposed based on the singular value decomposition in order to effectively highlight the ship targets and suppress the interference. The proposed novel energy density features have the advantage of clearly distinguishing ship targets from sea surfaces regardless of the effects of interferences. To test the performance of the proposed features, unsupervised K-means clustering is conducted for obtaining ship detection results. Compared with the classical and state-of-the-art SAR ship detectors, the proposed EDIC method generally yields the best performance in almost all tested sea sample areas with different kinds of interferences, in terms of both detection accuracy and processing efficiency. The proposed energy density-based feature extraction method also has great potential for supervised classification using neural networks, random forests, etc.
Cross-Subject EEG Channel Selection Method for Lower Limb Brain-Computer Interface
Authors: Wei Mingnan , Huang Mengjie , Ni Jiaying 
Published: September 2023

Article
Cross-Subject EEG Channel Selection Method for Lower Limb Brain-Computer Interface

Mingnan Wei 1,2, Mengjie Huang 3,*, and Jiaying Ni 3

1 School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China
2 Department of Computer Science, University of Liverpool, Liverpool, L69 3BX, United Kingdom
3 Design School, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China
* Correspondence: Mengjie.Huang@xjtlu.edu.cn

Received: 27 April 2023
Accepted: 30 June 2023
Published: 26 September 2023

Abstract: Lower limb motor imagery (MI) classification is a challenging research topic in the area of brain-computer interfaces (BCIs), and entails numerous signal channels to provide sufficient information about the background neural activity. However, practical applications often lack the environment to accommodate excessive channels due to the time-consuming setup process, inconvenient movement, and restricted application scenarios. The existing channel selection algorithms (designed for the individual subject) place a great deal of focus on the classified performance comparisons, whereas the significance of actual locations and neural functions of brain regions is disregarded. Although these algorithms require significant computation resources, their selected solutions cannot be re-used for other subjects to realize the cross-subject channel selection and improve the reusability of model due to poor interpretability and inapplicability. To date, there have been no investigations about the cross-subject channel selection problem for the lower limb MI stepping tasks. This study proposes an optimal cross-subject lower limb channel selection that selectively retains significant channels, narrows the computation scope of the selection, and obtains the optimal selection solutions. Through stepping-based MI experiments, the proposed optimal channel selection enables effective recognition in low-channel settings, thereby contributing a lot to the development of generic and convenient lower limb BCI systems. Additionally, statistical analysis reveals a significant difference in energy spectrum between left and right stepping-based MI tasks in the and bands of the frontal lobe channels, providing new evidence that the frontal lobe dramatically affects lower limb MI tasks.
Improved Computer-Aided Diagnosis System for Nonerosive Reflux Disease Using Contrastive Self-Supervised Learning with Transfer Learning
Authors: Liao Junkai , Lam Hak-Keung , Gulati Shraddha , Hayee Bu 
Published: September 2023

Article
Improved Computer-Aided Diagnosis System for Nonerosive Reflux Disease Using Contrastive Self-Supervised Learning with Transfer Learning

Junkai Liao 1, Hak-Keung Lam 1,*, Shraddha Gulati 2, and Bu Hayee 2

1 Department of Engineering, King’s College London, London, United Kingdom
2 King’s Institute of Therapeutic Endoscopy, King’s College Hospital NHS Foundation Trust, London, United Kingdom
* Correspondence: hak-keung.lam@kcl.ac.uk

Received: 5 February 2023
Accepted: 21 July 2023
Published: 26 September 2023

Abstract: The nonerosive reflux disease (NERD) is a common condition, the symptoms of which mainly include heartburn, regurgitation, dysphagia and odynophagia. The conventional diagnosis of NERD needs the endoscopic examination, biopsy of the lining of the esophagus (mucosa), and ambulatory pH testing over 24 to 96 hours, which is complex and time-consuming. To address this problem, a computer-aided diagnosis system for NERD (named NERD-CADS) has been proposed in our previous paper. The NERD-CADS offers a more convenient and efficient approach to diagnosing NERD, which only requires the input of endoscopic images into the computer to produce a nearly instant diagnostic result. The NERD-CADS uses a convolutional neural network (CNN) as a classifier and can classify the endoscopic images captured in the esophagus of both healthy people and NERD patients. This is, in fact, a classification problem of two classes: non-NERD and NERD. We conduct ten-fold cross-validation to verify the classification accuracy of the NERD-CADS. The experiment shows that the mean of ten-fold classification accuracy of the NERD-CADS test reaches 77.8%. In this paper, we aim to improve the classification accuracy of the NERD-CADS. We add the contrastive self-supervised learning as an additional component to the NERD-CADS (named NERD-CADS-CSSL), and investigate whether it can learn the capability of extracting representations to improve the classification accuracy. This paper combines the contrastive self-supervised learning with transfer learning, which first employs massive public image data to train the CNN by the contrastive self-supervised learning, and then uses the endoscopic images to fine-tune the CNN. In this way, the capability of extracting representations (learned by the contrastive self-supervised learning) can be transferred into the downstream task (NERD diagnosis). The experiment shows that the NERD-CADS-CSSL can obtain a higher mean (80.6%) in tests than the NERD-CADS (77.8%).
Real-Time Traffic Flow Statistics Based on Dual-Granularity Classification
Authors: Bi Yanchao , Yin Yuyan , Liu Xinfeng , Nie Xiushan , Zou Chenxi , Du Junbiao 
Published: September 2023

Article
Real-Time Traffic Flow Statistics Based on Dual-Granularity Classification

Yanchao Bi, Yuyan Yin, Xinfeng Liu *, Xiushan Nie, Chenxi Zou, and Junbiao Du

1 School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101, China
* Correspondence: liuxinfeng18@sdjzu.edu.cn

Received: 17 June 2023
Accepted: 13 September 2023
Published: 26 September 2023

Abstract: Traffic detection devices can cause accuracy degradation over time. Considering problems such as time-consuming and laborious manual statistics, high misdetection probabilities, and model tracking failures, there is an urgent need to develop a deep learning model (which can stably achieve detection accuracy over 90%) to evaluate whether the device accuracy still satisfies the requirements or not. In this study, based on dual-granularity classification, a real-time traffic flow statistics method is proposed to address the above problems. The method is divided into two stages. The first stage uses YOLOv5 to acquire all the motorized and non-motorized vehicles appearing in the scene. The second stage uses EfficientNet to acquire the motorized vehicles obtained in the previous stage and classify such vehicles into six categories. Through this dual-granularity classification, the considered problem is simplified and the probability of false detection is reduced significantly. To correlate the front and back frames of the video, vehicle tracking is implemented using DeepSORT, and vehicle re-identification is implemented in conjunction with the ResNet50 model to improve the tracking accuracy. The experimental results show that the method used in this study solves the problems of misdetection and tracking effectively. Moreover, the proposed method achieves 98.7% real-time statistical accuracy by combining the two-lane counting method.
Dedicated Early Morning Orthopaedic Trauma Operating Room Is Associated With Shorter Time to Surgery and Decreased Length of Hospital Stay for Children Undergoing Surgical Treatment of Supracondylar Humerus Fractures: A Retrospective Cohort Study
Authors: Chen Vivian , Hara Reiko , Siddiqui Ali A. , Omar Hanna N. , Nazareth Alexander , Lightdale-Miric Nina , Phan Alex , Wren Tishya A. L. , Goldstein Rachel Y. 
Published: September 2023

Introduction:
The purpose of this study was to investigate whether the use of a dedicated early morning orthopaedic trauma operating room (OR) resulted in shorter wait times, decreased surgical times, decreased length of stay (LOS), and decreased complications in children treated with urgent surgical intervention for supracondylar humerus fractures.

Methods:
This retrospective comparative cohort study at a level I pediatric trauma center included patients younger than 12 years with supracondylar humerus fractures urgently treated with closed or open reduction and percutaneous pinning. Index surgical cases from April 28, 2013, to February 26, 2020, were included. Patients with prior humerus fracture, concomitant injuries, open fracture, pulseless supracondylar fracture, or missing data were excluded. Patients were analyzed based on the type of OR: dedicated early morning orthopaedic trauma OR or typical daytime orthopaedic OR. The primary outcome was time from presentation to surgery. Secondary outcomes included surgical time, complications, and LOS.

Results:
A total of 401 patients with a mean age of 5 ± 2 (range: 1 to 11) years and a mean follow-up of 2.0 ± 2.1 (range: 0.5 to 25.0) months were included, of whom 137 patients (34%) underwent surgery in the early morning dedicated orthopaedic trauma OR. The dedicated early morning orthopaedic OR group had significantly less time from presentation to surgery (7.5 versus 9.4 hours; P = 0.0002) and shorter LOS (21.0 versus 24.0 hours; P = 0.004) compared with children treated in the typical daytime orthopaedic OR. Surgical time (31.1 versus 32.6 minutes; P = 0.40) and complication rates (5.8% versus 4.9%; P = 0.65) were similar between the groups. No revision surgery was required in either group.

Discussion:
Surgical wait times were diminished with use of the dedicated early morning OR, as was LOS. Surgical times and complication rates were similar between groups. Institutions may consider adopting a dedicated early morning orthopaedic trauma OR to improve surgical wait times and decrease LOS.

Level of evidence:
III