bluuebunny/crossref_metadata_2025_split huggingface dataset:
Physical Testing of a High-Speed Helico-Axial Pump for High-GVF Operation
Authors: Ejim Chidrim Enoch , Xiao Jinjiand , Lee Woon Yung , Zabala Wilson Andres 
Published: November 2021

Abstract
High-speed rotordynamic pump operation for downhole or surface production is required and also beneficial to handle very high gas volume fraction (GVF) flows. Operating speeds of these pumps can be in excess of twice those of conventional pumps. This study presents results showing a high-speed helico-axial pump (HAP) can operate satisfactorily at intake GVFs up to 98%. The findings increase capabilities of field engineers and operators to boost and maximize production from high gas-content wells.
The HAP tested had a housing outer diameter of 4.00-inch and operated at a rotational speed of 6000 revolutions per minutes (RPM). Air and water were the test fluids with the water volume flow rate held constant while the air volume flow rate was varied. The liquid and gas volume flow rates varied from 63 to 143 barrels per day (BPD), and 549 to 3238 BPD, respectively. Intake pressures varied from 14 to 76 psig, with average inlet temperature of 18°C. The corresponding discharge pressures and temperatures were recorded for each test point and observed for stable pump operation.
The results showed that the HAP had stable operation during the tests for intake GVF range from 84% to 98%. Pump discharge pressures for this range of high intake GVF varied from 21 to 89 psig. The corresponding differential pressures across the HAP all had positive magnitudes indicating that at such high-speeds, the HAP was still able to add energy to the fluid even with the high gas content at intake. Analysis at fixed intake pressure with varying GVFs showed that the discharge-to-intake pressure ratio decreased with increasing intake GVF. For instance, at 33psig intake pressure, increasing the intake GVF from 84% to 94% decreased the discharge-to-intake pressure ratio from about 1.27 to 1.20, respectively. It was also observed that tightening the clearance between the impeller and diffuser of the HAP increased the discharge pressure compared to when the clearance was loose. Furthermore, ensuring the upstream flow is properly conditioned also improved the stable operation of the HAP. Overall and in conclusion, running a HAP at high speeds in addition to optimizing certain features of the HAP can result in stable pump operation and enhanced pressure boosting in high-GVF flows.
This study mainly highlights the importance of operating HAPs at high speeds of up to 6000 RPM. Tightening clearances between rotordynamic components as well as tailored inlet flow conditioning are also additional features that enhance pressure boosting. This architecture opens up opportunities for field operators, and engineering personnel to maximize hydrocarbon production from their very high-gas content field assets, thereby increasing the economic bottomline for the stakeholders.
Unlocking Potential in Thinly Laminated Reservoirs Through Cased Hole Pulsed Neutron Logging
Authors: Akashev Yernur , Ahmad Samira , Cavalleri Chiara , Ignatochkina Yulia , Solodkiy Yevgenii 
Published: November 2021

Abstract
Field A is located in the center of the Dnieper-Donets basin (DDB), producing gas from clastic reservoirs from several deep horizons in the Upper Visean sediments. The case study highlights the application of advanced pulsed neutron logging technologies and high-resolution data processing to unlock the sedimentary layers’ characteristics and the gas potential behind the casing. Multiple rock measurements are simultaneously recorded for continuous lithology identification, porosity quantification, and differentiating gas-filled porosity from low porosity formations. Dedicated log data acquisition and processing techniques enable investigating the effect of thin laminations on reservoir quality and producibility.
The use of advanced pulsed neutron logging and interpretation method reduces the operational risks while securing critical reservoir parameters. A pulsed neutron spectroscopy tool provided a rich dataset including a self-compensated sigma and neutron porosity logs, fast neutron cross section (FNXS) together with capture and inelastic elemental spectroscopy. The logs interpretation was performed integrating FNXS and very high-resolution neutron porosity with mineral dry weight fractions and matrix properties from elemental spectroscopy processing. The comparison between the pulsed neutron measurements with standard open hole logs highlights the critical role of advanced fit-to-purpose logging techniques to accurately describe the underlying complexity of the formation and obtain improved net reservoir and net pay thicknesses in laminated and heterogeneous sequences.
The logging objectives were successfully met, and additional valuable information related to the reservoir were determined in an efficient manner. The study also shows the critical value of FNXS as confident gas measurement.
The FNXS measures the ability of the formation interacting with fast neutrons which are highly dependent on atomic density and not dominated by particular isotopes such as traditional sigma and porosity measurements. It is highly sensitive to gas-filled porosity, but it is independent of hydrogen index, acting like a cased-hole density measurement. Moreover, it demonstrates the importance of accurate knowledge of the mineralogy and matrix as well as the ability to measure at very high resolution to unravel the highly layered nature of the formation and its implication on completion and production strategy.
Pulsed neutron logging has evolved over a half century, but the intrinsic physical measurements remain unchanged. With the advent and introduction of the new FNXS measurement and a high-quality spectroscopy elemental concentration, a higher quality measurement and interpretation can be obtained from standalone pulsed neutron logging. The advanced technology and log data analysis interpretation module can be considered as an effective and comprehensive methodology for robust formation evaluation in similar and complex setting.
Open- and Cased- Hole Formation Evaluation Workflows Running Together for Reducing Uncertainties in Gas Reservoirs: An Acquisition Data and Evaluation Strategy in Dnieper-Donets Basin, Ukraine
Authors: Zambrano Rafael , Sadivnyk Michael , Makar Yevhen , Cavalleri Chiara , Rose David 
Published: November 2021

Abstract
Formation evaluation using cased-hole logs is a primary option for re-evaluating old wells in brownfields or contingency logging in new wells. Its consistency with a robust open hole evaluation is vital for its future implementation in field development. This work describes detailed open- and cased- hole evaluation workflows integrating different advanced subsurface measurements and alternative interpretation techniques to reduce the uncertainties of deriving the main petrophysical properties across the conventional and tight gas reservoirs in the Dnieper-Donets basin.
Since not all open-hole measurements can be recorded behind casing and some of the cased hole logs are not characterized for open hole conditions, it is not always possible to implement the same evaluation techniques for measurements done in open hole and cased hole. Nevertheless, different measurements provide different formation responses that supplement their gaps from one another. A wireline data acquisition strategy has been elaborated to carry out formation evaluation workflows using open- and cased-hole data independently but learning from each other. The methodology is based on novel and non-standard evaluation techniques that use measurements from advanced wireline technology such as nuclear magnetic resonance (NMR) and advanced pulsed neutron spectroscopy logs.
The methodology was applied to log data recorded on the Visean and Serpukhovian (Lower Carboniferous) productive gas zones, characterized by porosity (5-15pu) and permeability (0.1-100mD). The principal challenge for the formation evaluation of these reservoirs is deriving an accurate estimation of porosity, which requires removing the gas and matrix effects on the log responses. An inaccurate porosity estimation will result in an inaccurate permeability and water saturation, and the problem worsens in low-porosity rocks. In the open hole, the porosity computation from the Density-Magnetic Resonance (DMR) technique has proven to be more accurate in comparison with common single porosity methods. The same problem is addressed in cased hole conditions with the advanced pulsed neutron spectroscopy logs and a novel technique that combines the thermal neutron elastic scattering and fast neutron cross sections to obtain a gas-free and matrix-corrected porosity, as well as a resistivity independent gas saturation. The consistency of petrophysical properties independently estimated from the two separate workflows add confidence to the approach, and this is reflected in the gas production obtained from the perforated intervals.
This script describes in detail the open- and cased- hole formation evaluation workflows and the wireline technology and methodologies applied. Actual examples illustrate the effectiveness of these quantitative approaches in the Dnieper-Donets basin.
Using Supervised Machine Learning Algorithms for Automated Lithology Prediction from Wireline Log Data
Authors: Popescu Marian , Head Rebecca , Ferriday Tim , Evans Kate , Montero Jose , Zhang Jiazuo , Jones Gwynfor , Kaeng Geovani Christopher 
Published: November 2021

Abstract
This paper presents advancements in machine learning and cloud deployment that enable rapid and accurate automated lithology interpretation. A supervised machine learning technique is described that enables rapid, consistent, and accurate lithology prediction alongside quantitative uncertainty from large wireline or logging-while-drilling (LWD) datasets.
To leverage supervised machine learning, a team of geoscientists and petrophysicists made detailed lithology interpretations of wells to generate a comprehensive training dataset. Lithology interpretations were based on applying determinist cross-plotting by utilizing and combining various raw logs. This training dataset was used to develop a model and test a machine learning pipeline. The pipeline was applied to a dataset previously unseen by the algorithm, to predict lithology. A quality checking process was performed by a petrophysicist to validate new predictions delivered by the pipeline against human interpretations.
Confidence in the interpretations was assessed in two ways. The prior probability was calculated, a measure of confidence in the input data being recognized by the model. Posterior probability was calculated, which quantifies the likelihood that a specified depth interval comprises a given lithology.
The supervised machine learning algorithm ensured that the wells were interpreted consistently by removing interpreter biases and inconsistencies. The scalability of cloud computing enabled a large log dataset to be interpreted rapidly; &gt;100 wells were interpreted consistently in five minutes, yielding &gt;70% lithological match to the human petrophysical interpretation.
Supervised machine learning methods have strong potential for classifying lithology from log data because: 1) they can automatically define complex, non-parametric, multi-variate relationships across several input logs; and 2) they allow classifications to be quantified confidently. Furthermore, this approach captured the knowledge and nuances of an interpreter's decisions by training the algorithm using human-interpreted labels.
In the hydrocarbon industry, the quantity of generated data is predicted to increase by &gt;300% between 2018 and 2023 (IDC, Worldwide Global DataSphere Forecast, 2019–2023). Additionally, the industry holds vast legacy data. This supervised machine learning approach can unlock the potential of some of these datasets by providing consistent lithology interpretations rapidly, allowing resources to be used more effectively.
Room-temperature printing of 2D GaN semiconductor via liquid metal gallium surface confined nitridation reaction
Authors: Liu Jing , Li Qian , Du Bang-Deng , Gao Jian-Ye , Xing Bao-Yu , Wang Dian-Kai , Ye Ji-Fei 
Published: November 2021

Abstract
Outstanding wide-bandgap semiconductor materials like gallium nitride (GaN) have been extensively utilized in power electronics, radiofrequency power amplifiers, and harsh environment adaptability. Due to its quantum confinement impact in enabling desired deep-ultraviolet emission, excitonic impact, and electronic transport features, two-dimensional (2D) GaN has been one of the most remarkable areas for the future growth of microelectronic devices. Here, for the first time, we report a large area, wide bandgap, and room-temperature 2D GaN synthesis and printing strategy via liquid metal gallium surface-confined nitridation reaction. The developed low-temperature synthesis and printing process is consistent with various electronic device manufacturing methods and thus opens a way for the cost-effective growth of the third-generation semiconductor. In particular, the fully printed field-effect transistors relying on the GaN show p-type switching with an on/off ratio greater than 105, maximum field-effect hole mobility of 53 cm2 V−1 s−1, and a small sub-threshold swing at room temperature. The current study establishes a room temperature way to produce the GaN, which can be further verified, generalized, and realized for various upcoming electronic and photoelectronic applications.
MCGNet+: An Improved Motor Imagery Classication Based on Cosine Similarity
Authors: Li Yan , Zhong Ning , Taniar David , Zhang Haolan 
Published: November 2021

Abstract
It has been a challenge for solving the motor imagery classification problem in the brain informatics area. Accuracy and efficiency are the major obstacles for motor imagery analysis in the past decades since the computational capability and algorithmic availability cannot satisfy complex brain signal analysis. In recent years, the rapid development of Machine Learning (ML) methods has empowered people to tackle the motor imagery classification problem with more efficient methods. Among various ML methods, the Graph neural networks(GNNs) method has shown its efficiency and accuracy in dealing with inter-related complex networks. The use of GNN provides new possibilities for feature extraction from brain structure connection. In this paper, we proposed a new model called MCGNet+, which improves the performance of our previous model MutualGraphNet. In this latest model, the mutual information of the input columns forms the initial adjacency matrix for the cosine similarity calculation between columns to generate a new adjacency matrix in each iteration. The dynamic adjacency matrix combined with the spatial temporal graph convolution network(ST-GCN) has better performance than the unchanged matrix model. The experimental results indicate that MCGNet+ is robust enough to learn the interpretable features and outperforms the current state-of-the-art methods.
Structural Analysis And Topology Optimization Design For Bandwidth Extension of Magnetoelectric Seismometer
Authors: Yao Zhenjing , Zhang Jingyi , Gao Zhitao , Liu Yaran , Li Mingyang 
Published: November 2021

Abstract
Magnetoelectric seismometers can measure earthquake information and play an important role in earthquake monitoring. Aiming at the wider effective frequency bandwidth of magnetoelectric seismometers, a novel seismometer based on topology optimization structural pendulum is reported. The topology optimization of leaf spring structure in magnetoelectric seismometer is designed, the natural frequency and spurious frequency characteristics of the novel seismometer are analyzed. Based on variable density theory, the Solid Isotropic Material with Penalization (SIMP) model of the seismometer is established, and the Method of Moving Asymmetric (MMA) is adopted to obtain the optimal topology structure. The finite element analysis using ANSYS shows that novel seismometer after topology optimization structure is characteristic with lower natural frequency and higher spurious frequency than that of before optimization seismometer. The real vibration experimental results indicate that after topology optimization, the effective frequency bandwidth of seismometer is increased by 55.50%, improving from [1s, 51Hz] to [4s, 78Hz].
Clinical Outcomes of Endoscopic Synovectomy With Adjuvant Radiotherapy of Pigmented Villonodular Synovitis of The Hip: A Case Series of Single Center
Authors: Sun Hao , Ju XiaoDong , Huang Hong-Jie , Zhang Xin , Wang Jian-Quan 
Published: November 2021

Abstract
Background: Though radiotherapy has been widely used for knee pigmented villonodular synovitis (PVNS), there is few literatures about radiotherapy for the treatment of PVNS hip. Thus, the purpose of this study was to analyze the clinical outcomes of endoscopic synovectomy with/without radiotherapy postoperatively of PVNS hip.Methods: We performed a retrospective study of patients who underwent endoscopy in our hospital from November 2010 to January 2021. Patients with magnetic resonance image (MRI) signs, endoscopic findings and/or histological evidence of PVNS were included. All patients underwent synovectomy endoscopically and were divided into two groups depending on receiving postoperative radiotherapy or not. The primary outcome measurements were the recurrence of PVNS, receiving revision, and/or converting to total hip arthroplasty (THA). The secondary outcome measurements were the patient-reported outcome (PRO) collected at pre- and post-operation, which consist of Hip Outcome Score Activities of Daily Living (HOS-ADL), modified Harris Hip Score (mHHS), International Hip Outcome Tool-12 (IHOT-12), Non-arthritic Hip Scale (NAHS), and visual analog scale (VAS).Results: In a case series of 16 patients (8 cases of male, 50%), 4 (25%) cases were localized type and 12 (75%) cases were diffuse type. The average follow-up was 44.8±38.2 months (range,3 to 110). 8 (50%) cases (6 diffuse cases and 2 localized cases) received radiotherapy postoperatively, and the rest (6 diffuse cases and 2 localized cases) received endoscopic treatment alone. At the latest follow-up, 3 (18.75%) cases (2 diffuse cases and 1 localized case) who did not receive radiotherapy converted to arthroplasty. The preoperative HOS-ADL, mHHS, IHOT-12, NAHS, VAS scores of remaining 13 patients were 63.1±19.1 (range,32.0 to 98.8), 54.8±20.1 (range, 10.0 to 77.0), 50.9±15.4 (range, 31.0 to 76.6) ,51.6±15.9 (range, 20.0 to 84.4), 6.0±1.4 (range,4.0 to 8.0) points, respectively. The latest HOS-ADL, mHHS, IHOT-12, NAHS, VAS scores of the 13 patients were 79.7±10.8 (range, 58.0 to 97.6), 78.6±9.1 (range,55.0 to 87.0), 74.7±9.7 (range, 55.6 to 91.0), 78.9±18.7 (range,20.0 to 92.5), 3.1±1.2 (range,2.0 to 6.0) points respectively. There was a statistically significant difference between pre- and post-operation PRO.Conclusion: Endoscopic synovectomy can achieve satisfactory PRO in PVNS hip patients. Besides, postoperative adjuvant radiotherapy can achieve higher hip survivability than synovectomy alone in this present study.
The Serum Uric Acid Level of Bipolar Depression: A Retrospective Real-World Study
Authors: Li Gaizhi , Liu Huishan , Lei Lei , Zhang Xuemin , Gao Mingxue , Tu Hongwei , Zhang Yu , Liu Zhifen , Zhang Kerang 
Published: November 2021

Abstract
Objective

The aim of this study was to evaluate whether serum uric acid can be used as a biomarker to identify bipolar disorder (BD) from unipolar depression (UD).
Methods

We reviewed the medical records of 18-65-year-old depressive patients hospitalized in the first hospital of Shanxi Medical University from October 2015 to October 2020. The main discharge diagnosis of these patients was recurrent depressive disorder(RDD) or BD. The data of gender, age, education level, drug use and serum uric acid level were extracted. The differences of uric acid levels between the two groups at admission and discharge were compared.
Results

The uric acid level of BD decreased after treatment, the uric acid level of BD group was significantly higher than that of UD group at baseline (P=0.02) and after treatment (P=0.025).
Conclusion

UA may be a potential biomarker to distinguish BD from UD.
Machine Learning-Based Turbulence-Risk Prediction Method for the Safe Operation of Aircrafts
Authors: Mizuno Shinya , Ohba Haruka , Ito Koji 
Published: November 2021

Abstract
Customer comfort is an important requirement for airlines, and avoiding and mitigating aircraft shaking have always been crucial in this regard. In particular, managing aircraft operations during turbulence is a major issue for airlines. We propose a method for predicting the occurrence of turbulence to support the safe and comfortable operation of aircrafts. Our method integrates meteorological data from Japan and turbulence information provided by Fuji Dream Airlines. Because turbulence occurs rarely, we define a risk cluster that includes turbulence observation data and use it as turbulence training data. Hence, we first estimated the risk cluster, then performed a principal component analysis (PCA) on meteorological data to obtain a projection matrix \(W\) for reducing data dimensions. Using the turbulence-occurrence indicator and the meteorological data coordinates linear transformed by \(W\), we calculated the risk cluster using the k-means method which, in turn, was used in conjunction with support vector classification (SVC) to predict the turbulence-risk dates based on meteorological data from 2019. The results revealed that the days with turbulence risks were accurately identified from the meteorological data; thus, we believe that this method can help support the safe operation of aircrafts. Furthermore, we believe this study will lead to the development of human resources by providing a guide for making safety decisions through the effective use of aviation data.
Bayesian variable selection and survival modeling: Assessing the most important comorbidities that impact lung and colorectal cancer survival in Spain
Authors: Rubio Francisco Javier , Alvares Danilo , Redondo-Sanchez Daniel , Marcos-Gragera Rafael , Sánchez María-José , Luque-Fernandez Miguel Angel 
Published: November 2021

Abstract
Cancer survival represents one of the main indicators of interest in cancer epidemiology. However, the survival of cancer patients can be affected by a number of factors, such as comorbidities, that may interact with the cancer tumour. Moreover, it is of interest to understand how different cancer sites and tumour stages are affected by different comorbidities. Identifying the comorbidities that affect cancer survival is thus of interest as it can be used to identify factors driving the survival of cancer patients. This information can also be used to identify vulnerable groups of patients with comorbidities that may lead to a worst prognosis of cancer. We address these questions and propose a principled selection and evaluation of the effect of comorbidities in the overall survival in cancer patients. In the first step, we apply a Bayesian variable selection method that can be used to identify the comorbidities that predict overall survival. In the second step, we build a general Bayesian survival model that accounts for time-varying effects. In the third step, we derive several posterior predictive measures to quantify the effect of individual comorbidities on the population overall survival. We present applications to data on lung and colorectal cancers from two Spanish population-based cancer registries. The proposed methodology is implemented with a combination of the R-packages mombf and rstan. We provide the code for reproducibility at https://github.com/migariane/BayesVarImpComorbiCancer.