bluuebunny/crossref_metadata_2025_split huggingface dataset:
The devil we know and the angel that did not fly: An examination of devil/angel shift in twitter fracking “debates” in NY 2008–2018
Authors: Pattison Andrew , Cipolli William , Marichal Jose 
Published: September 2021

AbstractRecent work has applied the Narrative Policy Framework (NPF) to examine narrative strategies in policy debates on social media platforms. We contribute to the literature by applying the NPF to fracking policy debates in New York using well‐established Natural Language Processing tools, including sentiment analysis. We combine this computational approach with a qualitative hand‐coding of pro‐ and antifracking Twitter influentials. This approach allows us to consider a much larger corpus of tweets over a much longer time frame than has been done thus far. We adapt and test NPF propositions related to the use of the devil/angel shift strategies before and after a major state‐wide policy change, that is, a state‐wide moratorium on high volume hydraulic fracturing or fracking. Overall, we find evidence for the use of the devil shift narrative strategy by the pro‐fracking coalition aimed at the Governor prior to the moratorium. After the moratorium, the relative percentage of Tweets containing devil shift sentiments decreases as the pro‐fracking coalition generally downshifts in its use of angel shift language without a corresponding increase in devil shift language, whereas, conversely, the anti‐fracking coalition generally downshifts in its use of devil shift language without a general increase in angel shift language. When we shifted our analysis to Tweets containing fracking and the Governor, we found a similar postban decrease in devil shift language among anti‐fracking users. Our findings offer lessons for using computational tools in the NPF as an approach to expand analytic ability and for the operationalization of concepts such as narrative strategies and policy entrepreneurs.
Activation of vagovagal reflex prevents hepatic ischaemia–reperfusion‐induced lung injury via anti‐inflammatory and antioxidant effects
Authors: Deng Jielin , Jiang Yunqiu , Wang Meng , Shao Ling , Deng Changjin 
Published: September 2021

New Findings

What is the central question of this study?
Does vagus nerve stimulation have protective effects against both direct liver damage and distant lung injury in a rat model of hepatic ischaemia–reperfusion?

What is the main finding and its importance?
Vagus nerve stimulation provides protection through anti‐inflammatory and anti‐oxidative stress effects, possibly achieved by the vagovagal reflex.

AbstractHepatic ischaemia–reperfusion (I/R) is not an isolated event; instead, it can result in remote organ dysfunction. The aim of this study was to investigate whether vagus nerve stimulation (VNS) can alleviate hepatic I/R‐induced lung injury and to explore the underlying mechanism. Thirty male Sprague–Dawley rats were randomly allocated into five groups (n = 6 each): the sham group (without I/R or VNS), the I/R group (hepatic I/R) and three different VNS treatment groups (hepatic I/R plus VNS). The hepatic I/R group was subjected to occlusion of the portal vein and hepatic artery for 1 h, followed by 6 h of reperfusion. The intact afferent and efferent cervical vagus nerves were stimulated throughout the I/R process. During VNS, cervical neural activity was recorded. At the end of the experiment, liver function, the wet‐to‐dry lung weight ratio, histology of the liver and lung and inflammatory/oxidative indices were evaluated. We found that VNS significantly mitigated lung injury, as demonstrated by alleviation of pulmonary oedema and pathological alterations, by limiting inflammatory cytokine infiltration and increasing antioxidant capability. This proof‐of‐concept study suggested that VNS might protect patients from lung injury induced by hepatic I/R related to various circumstances.
Functional Cathode Coatings of LiH2PO4 and LiTi2(PO4)3 for Solid-State Batteries
Authors: Fang Aili , Jia Xiaoping 
Published: September 2021

Abstract
The interfacial reactivity and resistance between the cathode and the solid-state electrolyte (SSE) of a solid-state battery (SSB) usually lead to quite poor cycling performance and fast capacity decay. Hence, cathode coatings are generally applied to reduce cathode/SSE interfacial impedance in SSBs. In recent years, based on high-throughput screening, several promising coating materials have been recognized. In the present work, density functional theory (DFT) calculations were conducted on LiH2PO4 and LiTi2(PO4)3 to examine their characteristics as potential cathode coating materials. It was found that both of these materials had high oxidation potentials (&gt;4.5 V), good chemical stability against the electrolyte and the cathode, reasonable ionic conductivity, and wide bandgaps; therefore, they can be used as outstanding cathode coating materials for SSBs.
Advanced Exergy-Based Audit of Heat Recovery Steam Generators: A Case Study
Authors: Sohrabi Arvin , Behbahaninia Ali , Sayadi Saeed , Banifateme Mohsen 
Published: September 2021

Abstract
The current paper enhances the methods presented in The American Society of Mechanical Engineers (ASME) Performance Test Code (PTC) 4.1 and 4.4 and proposes an exergy-based loss method (LM) for assessing heat recovery steam generators (HRSGs) performance. First, energy and exergy analyses are applied to one HRSG unit in an existing combined cycle power plant. Then, the calculated exergy destructions are further split into avoidable and unavoidable parts. The sources of inefficiency consist of three energy and exergy loss terms and two exergy destruction terms. The loss terms are associated with the release of the exhaust gas to the atmosphere, Carbon Monoxide formation, and the heat loss from the casing, while the destruction terms represent exergy destruction within the duct burner and the heat transfer unit. The advanced exergy analysis was conducted based on a realistic perspective, considering the integrated operation of both subcomponents. Results reveal that the main source of inefficiency corresponds to the losses associated with the exhaust gas from the stack. Moreover, utilizing semi-ideal heat exchangers can avoid a considerable part (18.9%) of the exergy destruction in the heat transfer unit. The HRSG exergy efficiency is obtained by 71.7% and can be increased to 75.3% in unavoidable operating conditions.
Combustion Characteristics of Coal, Petroleum Coke, Biomass, and Their Ternary Blends
Authors: Kumar Pritam , Nandi Barun Kumar 
Published: September 2021

Abstract
This work presents the combustion characteristics of coal, petroleum coke (PC), rice straw (RS), mustard cake (MC), and their blends to assess the applicability of blended fuel for thermal power generation. Characterization results show that PC has the highest gross calorific value (GCV) (35,990 kJ/kg) to improve the overall energy density of the blend significantly. Higher volatile matter (VM) present in RS and MC improved the ignition behavior and combustion efficiency of the blend. For 10 °C/min heating rate at 350 °C, with the increase in RS in blends from 10% to 30%, combustion efficiency increased from 12.85% to 32.66%. Synergistic analysis signifies that higher biomass content enhances blends’ combustion characteristics through catalytic effects of alkali oxides present in RS/MC. Thermodynamic analysis (ΔH, ΔG, and ΔS) inferred that RS and MC combustion is easier than coal and PC. With the increase in MC in blends from 10% to 30%, ΔH decreased from 114.81 to 82.31 kJ/mol, ΔG declined from 159.33 to 122.86 kJ/mol, and ΔS improved from −63.59 to −58.14 J/mol · K, indicating blending of biomass improved the combustion.
Applying Ensemble Kalman Filter to Transonic Flows Through a Two-Dimensional Turbine Cascade
Authors: Ito Sasuga , Furukawa Masato , Yamada Kazutoyo , Manabe Kaito 
Published: September 2021

Abstract
Turbulence is one of the most important phenomena in fluid dynamics. Large eddy simulation (LES) generally allows us to analyze smaller eddies than when using simulations based on unsteady Reynolds-averaged Navier–Stokes equations (URANS). In addition, the numerical solutions of LES show good agreements with experiments and numerical solutions based on direct numerical simulation. URANS simulations are, however, frequently used in academia and industry because LES computations are much more expensive compared with URANS simulations. In this investigation, an optimization of unsolved coefficients of the k–ω two equations model is performed on the transonic flow around T106A low-pressure turbine cascade to improve the accuracy of turbulence prediction with URANS. For the optimization approach, two-dimensional URANS is combined with ensemble Kalman filter which is one of the data assimilation techniques. In the assimilation process, a time- and spanwise-averaged LES result is used as pseudo-experimental data. Three-dimensional URANS simulations are performed for the evaluation of the optimization effect. URANS simulations are also applied to a different turbine cascade flow for the evaluation of the robustness of the optimized coefficients. These URANS results confirmed that the optimized coefficients improve the accuracy of turbulence prediction.
Parametric Behavior of a Vortex-Induced Vibration Model of Cylinders With Two Degrees-of-Freedom Using a Wake Oscillator
Authors: Fehér Rafael , Avila Juan Pablo Julca 
Published: September 2021

Abstract
A recent model to predict vortex-induced vibrations of a rigid cylinder is analyzed, and its response is compared with different experimental data presented in the literature. One database with the tuning parameters for different mass ratios, damping ratios, Reynolds number, and Strouhal number is presented. This article provides a set of predefined tuning parameters for different experimental conditions. We presented the results of the cross-flow and in-line reduced amplitudes, the mean drag coefficient, the lift coefficient, and the cross-flow reduced frequency, all versus the reduced velocity. Also, an equation to estimate the cross-flow maximum reduced amplitude as function of the mass ratio was generated. The model shows to be efficient in predicting the maximum amplitude of vibration in the cross-flow direction when compared to experimental data for mass ratios varying from 2.36 to 12.96 and for damping ratios from 0.002 to 0.4, predicting the reduced amplitude in both directions. The simulation results when varying the Reynolds number and the Strouhal number are in good agreement with experimental data. Moreover, the model shows to be less sensitive to variations in the damping ratio when compared to variations in the mass ratio.
Machine Learning Applications to Predict Surface Oil Rates for High Gas Oil Ratio Reservoirs
Authors: Ibrahim Ahmed Farid , Al-Dhaif Redha , Elkatatny Salaheldin , Al Shehri Dhafer 
Published: September 2021

AbstractWell-performance investigation highly depends on the accurate estimation of its oil and gas flowrates. Testing separators and multiphase flowmeters (MPFMs) are associated with many technical and operational issues. Therefore, this study aims to implement the support vector machine (SVM), and random forests (RF) as machine learning (ML) methods to estimate the well production rate based on chokes parameters for high GOR reservoirs. Dataset of 1131 data points includes GOR, upstream and downstream pressures (PU and PD), choke size (D64), and actual data of oil and gas production rates. The data have GOR was up to 9265 SCF/STB, the oil rate varied from 1156 and 7982 BPD. SVM and RF models were built to estimate the production rates. The ML models were trained using seventy percent of the dataset, while the models were tested and validated using 30% of the dataset. The dataset was classified to 622 wells that were flowing at critical flow compared with 509 wells that were flowing at subcritical conditions based on a PD/PU ratio of 0.55. Four machine learning models were developed using SVM and RF for subcritical flow and critical flow conditions. Different performance indicators were applied to assess the developed models. SVM and RF models revealed average absolute percentage error (AAPE) of 1.3 and 0.7%, respectively, in the case of subcritical flow conditions. For critical flow conditions, the AAPE was found to be 1.7% in the SVM model, and 0.8% in the RF model. The developed models showed a coefficient of determination (R2) higher than 0.93. All developed ML models perform better than empirical correlations. These results confirm the capabilities to predict the oil rates from the choke parameters in real-time without the requirement of instrument installation of wellsite intervention.
Changes in benzodiazepine use in the French general population after November 2015 terrorist attacks in Paris: an interrupted time series analysis of the national CONSTANCES cohort
Authors: Gouraud Clement , Airagnes Guillaume , Kab Sofiane , Courtin Emilie , Goldberg Marcel , Limosin Frédéric , Lemogne Cedric , Zins Marie 
Published: September 2021

ObjectivesTo determine whether the terrorist attacks occurring in Paris on November 2015 have changed benzodiazepine use in the French population.DesignInterrupted time series analysis.SettingNational population-based cohort.Participants90 258 individuals included in the population-based CONSTANCES cohort from 2012 to 2017.Outcome measuresBenzodiazepine use was evaluated according to two different indicators using objective data from administrative registries: weekly number of individuals with a benzodiazepine delivered prescriptions (BDP) and weekly number of defined daily dose (DDD). Two sets of analyses were performed according to sex and age (≤50 vs >50). Education, income and area of residence were additional stratification variables to search for at-risk subgroups.ResultsAmong women, those with younger age (incidence rate ratios (IRR)=1.18; 95% CI=1.05 to 1.32 for BDP; IRR=1.14; 95% CI=1.03 to 1.27 for DDD), higher education (IRR=1.23; 95% CI=1.03 to 1.46 for BDP; IRR=1.23; 95% CI=1.01 to 1.51 for DDD) and living in Paris (IRR=1.27; 95% CI=1.05 to 1.54 for BDP) presented increased risks for benzodiazepine use. Among participants under 50, an overall increase in benzodiazepine use was identified (IRR=1.14; 95% CI=1.02 to 1.28 for BDP and IRR=1.12; 95% CI=1.01 to 1.25 for DDD) and in several strata. In addition to women, those with higher education (IRR=1.22; 95% CI=1.02 to 1.47 for BDP), lower income (IRR=1.17; 95% CI=1.02 to 1.35 for BDP) and not Paris residents (IRR=1.13; 95% CI=1.02 to 1.26 for BDP and IRR=1.13; 95% CI=1.03 to 1.26 for DDD) presented increased risks for benzodiazepine use.ConclusionTerrorist attacks might increase benzodiazepine use at a population level, with at-risk subgroups being particularly concerned. Information and prevention strategies are needed to provide appropriate care after such events.
Moderate to severe OSA screening based on support vector machine of the Chinese population faciocervical measurements dataset: a cross-sectional study
Authors: Zhang Liu , Yan Ya Ru , Li Shi Qi , Li Hong Peng , Lin Ying Ni , Li Ning , Sun Xian Wen , Ding Yong Jie , Li Chuan Xiang , Li Qing Yun 
Published: September 2021

ObjectivesObstructive sleep apnoea (OSA) has received much attention as a risk factor for perioperative complications and 68.5% of OSA patients remain undiagnosed before surgery. Faciocervical characteristics may screen OSA for Asians due to smaller upper airways compared with Caucasians. Thus, our study aimed to explore a machine-learning model to screen moderate to severe OSA based on faciocervical and anthropometric measurements.DesignA cross-sectional study.SettingData were collected from the Shanghai Jiao Tong University School of Medicine affiliated Ruijin Hospital between February 2019 and August 2020.ParticipantsA total of 481 Chinese participants were included in the study.Primary and secondary outcome(1) Identification of moderate to severe OSA with apnoea–hypopnoea index 15 events/hour and (2) Verification of the machine-learning model.ResultsSex-Age-Body mass index (BMI)-maximum Interincisal distance-ratio of Height to thyrosternum distance-neck Circumference-waist Circumference (SABIHC2) model was set up. The SABIHC2 model could screen moderate to severe OSA with an area under the curve (AUC)=0.832, the sensitivity of 0.916 and specificity of 0.749, and performed better than the STOP-BANG (snoring, tiredness, observed apnea, high blood pressure, BMI, age, neck circumference, and male gender) questionnaire, which showed AUC=0.631, the sensitivity of 0.487 and specificity of 0.772. Especially for asymptomatic patients (Epworth Sleepiness Scale <10), the SABIHC2 model demonstrated better predictive ability compared with the STOP-BANG questionnaire, with AUC (0.824 vs 0.530), sensitivity (0.892 vs 0.348) and specificity (0.755 vs 0.809).ConclusionThe SABIHC2 machine-learning model provides a simple and accurate assessment of moderate to severe OSA in the Chinese population, especially for those without significant daytime sleepiness.
Identifying persistent somatic symptoms in electronic health records: exploring multiple theory-driven methods of identification
Authors: Kitselaar Willeke M , Numans Mattijs E , Sutch Stephen P , Faiq Ammar , Evers Andrea WM , van der Vaart Rosalie 
Published: September 2021

ObjectivePersistent somatic symptoms (PSSs) are defined as symptoms not fully explained by well-established pathophysiological mechanisms and are prevalent in up to 10% of patients in primary care. The present study aimed to explore methods to identify patients with a recognisable risk of having PSS in routine primary care data.DesignA cross-sectional study to explore four identification methods that each cover part of the broad spectrum of PSS was performed. Cases were selected based on (1) PSS-related syndrome codes, (2) PSS-related symptom codes, (3) PSS-related terminology and (4) Four-Dimensional Symptom Questionnaire scores and all methods combined.SettingCoded electronic health record data were extracted from 76 general practices in the Netherlands.ParticipantsPatients who were registered for at least 1 year during 2014–2018, were included (n=169 138).Outcome measuresIdentification methods were explored based on (1) PSS sample sizes and demographics, (2) presence of chronic conditions and (3) healthcare utilisation (HCU) variables. Overlap between methods and practice specific differences were examined.ResultsThe percentage of cases identified varied between 0.3% and 7.0% across the methods. Over 58.1% of cases had chronic physical condition(s) and over 33.8% had chronic mental condition(s). HCU was generally higher for cases selected by any method compared with the total cohort. HCU was higher for method B compared with the other methods. In 26.7% of cases, cases were selected by multiple methods. Overlap between methods was low.ConclusionsDifferent methods yielded different patient samples which were general practice specific. Therefore, for the most comprehensive data-based selection of PSS cases, a combination of methods A, C and D would be recommended. Advanced (data-driven) methods are needed to create a more sensitive algorithm for identifying the full spectrum of PSS. For clinical purposes, method B could possibly support screening of patients who are currently missed in daily practice.
Characteristics of older adults with back pain associated with choice of first primary care provider: a cross-sectional analysis from the BACE-N cohort study
Authors: Vigdal Ørjan Nesse , Storheim Kjersti , Munk Killingmo Rikke , Småstuen Milada Cvancarova , Grotle Margreth 
Published: September 2021

ObjectivesTo describe characteristics of older adults with back pain in primary care, and to assess associations between patient characteristics and type of first primary care provider (general practitioner (GP), physiotherapist (PT) or chiropractor).DesignCross-sectional analysis from the Back Complaints in the Elders-Norway cohort study.SettingNorwegian GP, PT and chiropractic primary care centres.ParticipantsPatients aged ≥55 years seeking Norwegian primary care with a new episode of back pain were invited to participate. Between April 2015 and February 2020, we included 452 patients: 127 first visited a GP, 130 first visited a PT and 195 first visited a chiropractor.Primary and secondary outcome measuresFor the first objective, the outcome measure was descriptive statistics of patient characteristics, covering the following domains: sociodemographic, general health, current and previous back pain, psychological and clinical factors. For the second objective, first primary care provider was the outcome measure. Associations between patient characteristics and visiting a GP or PT compared with a chiropractor were assessed with multiple multinomial regression analyses.ResultsMedian (IQR) age was 66 (59–72) years. Levels of back-related disability was moderate to severe, with a median (IQR) Roland-Morris Disability Questionnaire (range 0–24) score of 9 (5–13). Recurring episodes were common, 301 (67%) patients had monthly or yearly recurrences. Patients with worse back-related disability, longer duration of symptoms, lower expectations for full recovery and worse physical performance measured with the Back Performance Scale had higher odds of visiting a GP or PT compared with a chiropractor (p<0.05).ConclusionOlder back pain patients in primary care had moderate to severe levels of back-related disability, and most had recurring episodes. Our results suggest that older adult’s choice of first primary care provider was associated with important patient characteristics, which highlights the need for caution with generalisations of study results across primary care populations.Trial registration numberNCT04261309.