bluuebunny/crossref_metadata_2025_split huggingface dataset:
Investigating the Impact of Capacity Utilization on Carbon Dioxide Emission: Evidence from China’s Iron and Steel Industry
Authors: Chen Ya , Zhang Xintian , Liu Haoxiang 
Published: February 2022

Abstract
In recent years, China’s industrialization and urbanization have deepened, and the economy has grown considerably. But at the same time, they have also brought about many environmental problems. As a pillar of the national economy, the iron and steel (IS) industry is one of the most emitting and energy-consuming sub-sectors of the industrial sector. It is also one of the industries with the most severe overcapacity problem in China. In this paper, we explore the impact of capacity utilization on carbon dioxide emission based on panel data of China’s iron and steel industry from 2005 to 2014. We also tested the heterogeneity in different regions and different sub-samples. Results show that capacity utilization and carbon dioxide emission are positively correlated. However, the impact of capacity utilization on carbon dioxide emission differs when considering regional heterogeneity. Results in all three regions show a positive relationship between capacity utilization and carbon dioxide emission, but the impact intensity is strongest in the western region, followed by the eastern and central regions. Moreover, capacity utilization impacts carbon dioxide emission by influencing firm numbers in the iron and steel industry and energy consumption. Further analysis shows that there exists a threshold effect in different stages of energy consumption and energy structure. Finally, some findings and practical policy recommendations are provided.
The Cross-Correlation Relationship Between the Real Estate Industry and High-Quality Economic Development
Authors: Zhang Mengwan , Gao Fengfeng , Gao Jialin , Chen Weiguo , Lee Chong Aik 
Published: February 2022

Abstract
Based on China’s real estate investment (REI), commercial housing sales (CHS), and GDP data from 2001 to 2020, we studied the dependence, asymmetry, and transmission direction of China’s real estate and economy. Their cross-correlation is demonstrated through the deep cross-correlation analysis (DCCA) algorithm, multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) algorithm and DCCA algorithm based on a time delay. Both REI and CHS have persistence with GDP. The real estate industry and economy have asymmetric multifractal characteristics. Meanwhile, real estate and the economy have different trends, and they can affect each other with the same length of time delay. This effect diminishes in a short period of time, and there are unsteady fluctuations in cross-correlation persistence in the long term.
Adaptation des cours présentiels en cours en ligne dans le contexte de covid-19 : quels défis, quelles solutions?
Authors: Bertrand Juliane , Soto Monica 
Published: February 2022

Avec le confinement nécessité par l’apparition de la pandémie de covid-19, l’enseignement des langues a dû se tourner de façon précipitée vers des modalités d’enseignement en ligne. Considérant l’importance de développer un esprit de communauté pour faciliter la motivation, quels facteurs facilitent le succès d’une expérience d’enseignement des langues en ligne? Qu’est-ce qui peut complexifier le processus? À travers une comparaison de deux parcours – l’un en enseignement de l’espagnol langue étrangère au niveau débutant, l’autre en enseignement du français langue seconde au niveau avancé – le présent article vise à exposer quelques pistes permettant de répondre aux besoin des apprenants.
Traducteurs automatiques neuronaux comme outil didactique/pédagogique : DeepL dans l’apprentissage du français langue seconde
Authors: Barysevich Alena , Costaris Claire 
Published: February 2022

Cet article aborde un sujet très actuel dans l’apprentissage des langues secondes et étrangères du XXIe siècle : l'utilisation excessive des outils de traduction automatique par les apprenants et l’incertitude des enseignants universitaires comment gérer cette nouvelle réalité. Nous proposons d’aborder cette problématique de manière positive, en considérant des traducteurs automatiques en ligne comme outils pédagogiques. Ainsi, nous suggérons un atelier de formation conçu pour préparer les apprenants de français langue seconde/étrangère à l'usage responsable et efficace de DeepL, un des logiciels de TA les plus sophistiqués. Notre approche privilégie une discussion ouverte sur l’utilisation et l’utilité de la TA dans l’apprentissage des langues et met à la disposition des enseignants universitaires une ressource pédagogique à explorer afin de reconsidérer le rôle des technologies langagières dans l’apprentissage des langues.
Approach Couples Reservoir Simulation With Wellbore Model for Horizontal Wells
Authors: Carpenter Chris 
Published: February 2022

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 203977, “Coupling a Geomechanical Reservoir and Fracturing Simulator With a Wellbore Model for Horizontal Injection Wells,” by Shuang Zheng, SPE, and Mukul Sharma, SPE, The University of Texas at Austin. The paper has not been peer reviewed.
Reservoir cooling during waterﬂooding or waste-water injection can alter the reservoir stress ﬁeld signiﬁcantly by thermoporoelastic eﬀects. Colloidal particles in the injected water decrease the matrix permeability and build up the injection pressure. Fractures may initiate and propagate from injectors. These fractures are of great concern for environmental reasons and are a strong inﬂuence on reservoir sweep and oil recovery. The complete paper introduces methods to fully couple reservoir simulation with wellbore ﬂow models in fractured injection wells.

Introduction
In this study, a fully integrated 3D geomechanical thermal reservoir simulator is presented. This simulator is fully coupled with a wellbore model and allows fracture propagation. The model accounts for multiphase ﬂow, solid mechanics, thermal stresses, ﬁltration, and fracture growth in coupled reservoir- fracture-wellbore domains and can be used for simulations in both conventional and unconventional reservoirs. The simulator allows study of induced fracture propagation in cased- and openhole injectors while fully accounting for thermoporoelastic and particle-ﬁltration eﬀects. It also allows study of hydraulic fracture propagation in unconventional reservoirs considering the complex wellbore dynamics. The complete paper provides a practical simulation tool to maximize well injectivity while minimizing environmental risks, and to analyze and optimize the completion design in unconventional reservoirs.
Self-Organizing-Map Approach Characterizes Naturally Fractured Reservoir in a Mature Field
Authors: Carpenter Chris 
Published: February 2022

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 206027, “Naturally Fractured Basement Reservoir Characterization in a Mature Field,” by Muhammad Nur Ali Akbar, MOL Hungary. The paper has not been peer reviewed.
The complete paper describes an alternative solution for identifying the presence of natural fractures, classifying them into fracture-quality-related ﬂowability, and distributing them vertically within the well interval, and proposes a lateral distribution method for reservoir modeling. The proposed approach, using the machine-learning technique of self-organizing-map (SOM) clustering, eﬀectively assists recognition of fracture presence and quality along the well-depth interval.

Field Overview and Data Used
The case study was conducted in an oil ﬁeld discovered in the early 1980s in southwestern Hungary. Thirty-six wells penetrated the naturally fractured carbonate in the Triassic formation. The main lithology of this reservoir is limestone and dolomite associated with faults and exhumation breccia and marl/shale.
The reservoir is saturated oil (with gas cap) with unlimited aquifer (strong water drive). The gas cap, however, is mainly composed of 85% carbon dioxide and up to 1,800 ppm of hydrogen sulﬁde. Generally, the oil is intermediate to heavy, with a gravity of approximately 20 °API.
The reservoir rock properties of this case study are fully complex for both the pore system and its composition. In general, the matrix pore system does not signiﬁcantly contribute to storativity or permeability. The eﬀective porosity is approximately 4.2% on average. This value sometimes directs to the matrix porosity, but in this case study, high intensity of the microfracture presence behaves in a way similar to matrix porosity, a phenomenon the author terms “pseudomatrix porosity.”
Moreover, the eﬀective oil permeability value of the studied fractured reservoir is extremely high (up to 70 darcies per well-test interpretation). The permeability ranges from 1 to 2000 md in brecciated fractures or in naturally fractured rock samples.
Two types of core samples were used in this study—fractured breccia and naturally fractured rock. Both types have similar behavior in terms of the porosity/permeability relationship. In this study, that relationship is not the one normally observed in clastic reservoir rock. Marl/shale content is one of the more- critical parameters, indicating low fracture quality in terms of permeability.
More than 50% of the well-log data for this study were measured by Russian-type well logs, meaning that only simple electrical, gamma ray, and neutron- capture gamma logs were available. Other wells have standard triple-quad combination logs. Image logs are available only from two wells drilled in the 2000s. By considering these log-data limitations, cores, and production-test results, the study aimed to deﬁne the eﬀective fracture locations and intervals.
Technology Focus: Digital Data Acquisition (January 2022)
Authors: Sarma Pallav 
Published: February 2022

This third installment of the Digital Data Acquisition Technology Focus will focus on computer vision (CV) for improved data acquisition. CV is defined as a field of study that seeks to develop techniques to help computers “see” and understand the content of digital images such as photographs and videos.
The field of computer vision has seen significant growth in recent years because of the convergence of multiple events, such as advancements in machine-learning technology (e.g., deep learning), an exponential increase in availability of visual data from mobile phones and platforms such as YouTube, and the growth of new use cases such as autonomous driving. As such, it is no wonder that CV is also having a major effect on the oil and gas industry, reflected in the number of papers this year on this subject alone.
The oil and gas industry is awash with image and video data such as core images, seismic proﬁles, maps, video feeds for monitoring remote operations, and, these days, even drone images. The traditional approach to interpreting and acting on this data has been highly manual, which can be extremely time-consuming and prone to bias and error. CV technology intends to automate these processes and signiﬁcantly improve the turnaround time from data to decisions.
The papers chosen this year include application of CV for reservoir characterization, such as source-rock reconstruction, facies identiﬁcation, and seismic data enhancement; and safety applications such as rig-state identiﬁcation and automated corrosion mapping. Given the versatility of CV technology, its application across the oil and gas industry clearly will continue to grow.
Recommended additional reading at OnePetro: www.onepetro.org.
SPE 204216 - Deep-Learning-Based Vuggy Facies Identification From Borehole Images by Jiajun Jiang, Baylor University, et al.
SPE 202710 - Machine-Learning-Based Seismic Data Enhancement Toward Overcoming Geophysical Limitations by Shotaro Nakayama, INPEX, et al.
SPE 205347 - Machine-Learning-Assisted Segmentation of Focused Ion Beam Scanning Electron Microscopy Images With Artifacts for Improved Void-Space Characterization of Tight Reservoir Rocks by Andrey Kazak, Skolkovo Institute of Science and Technology, et al.
Computer Vision Analytics Enables Determination of Rig State
Authors: Carpenter Chris 
Published: February 2022

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 204086, “Determining Rig State From Computer Vision Analytics,” by Crispin Chatar, SPE, and Suhas Suresha, Schlumberger, and Laetitia Shao, Stanford University, et al. The paper has not been peer reviewed.
While companies cannot agree on a standard deﬁnition of “rig state,” they can agree that, as further use is made of remote operations and automation, rig-state calculation is mandatory in some form. By use of a machine-learning model that relies exclusively on videos collected on the rig ﬂoor to infer rig states, overcoming the limitations of existing methods is possible as the industry moves into a future of rigs featuring advanced technologies.

Introduction
The complete paper presents a machine-learning pipeline implemented to determine rig state from videos captured on the ﬂoor of an operating rig. The pipeline is composed of two parts. First, the annotation pipeline matches each frame of the video data set to a rig state. A convolutional neural network (CNN) is used to match the time of the video with corresponding sensor data. Second, additional CNNs are trained, capturing both spatial and temporal information, to extract an estimation of rig state from the videos. The models are trained on a data set of 3 million frames on a cloud platform using graphics processing units. Some of the models used include a pretrained visual geometry group (VGG) network, a convolutional 3D (C3D) model, and a two-stream model that uses optical ﬂow to capture temporal information.
A Discrete Model for Pressure Transient Analysis in Discretely Fractured Reservoirs
Authors: Chen Zhiming , Yu Wei 
Published: February 2022

Summary
Natural fractures are of great significance to the management of petroleum resources, groundwater, and carbon sequestration. Given that many naturally fractured reservoirs have poorly connected fractures, the traditional dual-porosity model like Warren and Root’s model is not reasonable for pressure transient analysis in these reservoirs. Therefore, the objective of this study is to develop a new discrete model (DM) in discretely fractured reservoirs based on a semianalytical approach.
The DM comprises three domains: wellbore-connecting (WC) fractures and/or wellbore-isolating (WI) fractures, and matrix. The DM is verified by case studies with numerical simulators for four kinds of discretely fractured reservoirs with (1) WC fracture networks, (2) WI fracture networks, (3) WC and WI fractures, and (4) WI fractures. Results indicate that the pressure transient behaviors in discretely fractured reservoirs can be classified into three types: WC fracture networks, WC and WI fractures, and WI fracture (networks). The special flow regimes of WC fracture networks are fluid supply and pseudoboundary dominated flow. For WC and WI fractures, the special flow regimes are bilinear flow, linear flow, and isolated fracture effect. The early pseudoradial flow and isolated fracture effect (“V-shaped” or “dip”) are the special flow regimes of WI fracture (networks), and they are similar to traditional dual-porosity models like the Warren and Root model. However, the source of “V-shaped” in DM is quite different from that in the Warren and Root model. This paper gives a new insight into the well testing in discretely fractured reservoirs.
Improved Learning Cycle Assessment of Stimulated Wells’ Performance through Advanced Mathematical Modeling
Authors: Donovan Diane , Azadi Mohsen , Ganpule Sameer , Nuralishahi Turaj , Smith Andrew , Josserand Sylvain , Thompson Bevan , Reay Thomas , Gay Laura , Burrage Kevin , Burrage Pamela , Lawson Brodie , Yazici Emine 
Published: February 2022

Summary
In this paper, we forecast cumulative production for stimulated gas wells using a combination of fast-to-implement modeling methodologies, including polynomial chaos expansion (PCE) and Gaussian processes (GP) proxy models coupled with populations of phenomenological models (POMs). These modeling techniques allow for a reduction in forecast uncertainty and are shown to be effective techniques for extrapolating early time data for stimulated well production from a field of wells in the Surat Basin, Queensland, Australia. The proposed techniques strategically capture and capitalize on production trends across an entire gas field, even in the presence of early production transients. We demonstrate that learning cycles can be shortened, leading to reasonable forecasts, as well as meaningful and actionable insights.
How can clinicians choose between conflicting and discordant systematic reviews? A replication study of the Jadad algorithm
Authors: Lunny Carole , Thirugnanasampanthar Sai Surabi , Kanji Salmaan , Ferri Nicola , Pieper Dawid , Whitelaw Sera , Tasnim Sara , Reid Emma K , Zhang Jia He , Kalkat Banveer , Chi Yuan , Abdoulrezzak Reema , Zheng Di Wen , Pangka Lindy R.S. , Wang Xin Ran , Safavi Parisa Safavi , Sooch Anmol , Kang Kevin T. , Tricco Andrea C , Nelson Harrison 
Published: February 2022

Abstract
Introduction: The exponential growth of published systematic reviews (SRs) presents challenges for clinicians seeking to answer clinical questions. In 1997, an algorithm was created by Jadad et al. to choose the best SR across multiple. Our study aims to replicate author assessments using the Jadad algorithm to determine: (i) if we chose the same SR as the authors; and (ii) if we reach the same results. Methods: We searched MEDLINE, Epistemonikos, and Cochrane Database of SRs. We included any study using the Jadad algorithm. We used consensus building strategies to operationalise the algorithm and to ensure a consistent approach to interpretation. Results: We identified 21 studies that used the Jadad algorithm to choose one or more SRs. In 62% (13/21) of cases, we were unable to replicate the Jadad assessment and ultimately chose a different SR than the authors. Overall, 18 out of the 21 (86%) independent Jadad assessments agreed in direction of the findings despite 13 having chosen a different SR. Conclusions: Our results suggest that the Jadad algorithm is not reproducible between users as there are no prescriptive instructions about how to operationalise the algorithm. In the absence of a validated algorithm, we recommend that healthcare providers, policy makers, patients and researchers address conflicts between review findings by choosing the SR(s) with meta-analysis of RCTs that most closely resemble their clinical, public health, or policy question, are the most recent, comprehensive (i.e. in terms of number of included RCTs), and at the lowest risk of bias.
An Randomised Interventional Study Investigating the Effect of Video Feedback by Mini-Clinical Evaluation Exercise (Mini-CEX) Method Compared to Conventional Directly Observed Feedback Method by Mini-Clinical Evaluation Exercise (Mini-CEX) on Final Year Medical (Internship Year) Students’ Clinical Competence in Shiraz University of Medical Sciences, Iran
Authors: Managheb Seyed Esmaeil , Malekpour Fatemeh , Pouralimohammadi Neda , Taghipour Kaveh 
Published: February 2022

Abstract
Introduction:Video feedback (VF) patient encounter is a novel approach that has been developed, which involves videotaping doctor-patient consultations and subsequently an assessor watching the consultation and providing feedback. The aim of our study is to evaluate the effect of mini-CEX by video feedback method on the clinical competency of final year medical (internship year) students in Shiraz University of Medical Sciences, Iran.Methods:Our study is a prospective randomized, controlled double-blinded interventional study which involves a total of 40 internship year students of Shiraz University of Medical Sciences. The 40 participants were then assessed using OSLER checklist in order to ascertain baseline clinical competency (OSLER1). The 40 participants were randomly allocated to two groups of 20 students each, a control group who underwent traditional directly observed Mini-CEX feedback and an intervention group who underwent video Mini-CEX feedback. Following the participation of interns in the feedback workshops, a post- intervention assessment of clinical competency was undertaken (OSLER 2). This interventional trial is registered in the ISRCTN Registry [ ISRCTN47543844] (16/02/2022).Results:The mean age of participants in the video feedback group was 24.8 years compared to 24.9 years in the traditional directly observed feedback group (p=0.687). There was no significant difference between the mean number of months students had completed of their internship year (final year) studies (p=0.906). Comparison between the OSLER 1 and OSLER 2 score of the Video Feedback group showed there was a significant improvement of OSLER 2 scores (p<0.001). The mean difference between the OSLER 1 and OSLER 2 scores in the Video Feedback group was 18.6 compared to a mean difference score of 2.4 in the traditional directly observed feedback group (p<0.001).Discussion:Mini-CEX is an effective tool in improving clinical and communication skills when conducted by either the traditional method or by video feedback. However, the use of mini-CEX in combination with video feedback methods significantly improved the clinical and communication skills competency of internship year students in comparison to traditional directly observed feedback methods.Conclusion:Our results demonstrate that video Feedback more effective in improving clinical skills competency learning among medical students. Therefore, greater use of video feedback based formative assessments (mini-CEX) for medical students’ clinical and communication skills is recommended.
Synthesis and Characterization of Ag2O, CoFe2O4, GO and their ternary composite for antibacterial activity
Authors: Aftab Komal , Naseem Taiba , Hussain Shahzad , Haq Sirajul , Rehman Mahfooz Ur , Waseem Muhammad 
Published: February 2022

Abstract
Currently, nanomaterials with exceptional antibacterial activity have become an emerging domain in research. The optimization of nanomaterials against infection causing agents is the next step in dealing with the present-day problem of antibiotics. In this research work, Ag2O, CoFe2O4, and Ag2O/CoFe2O4/rGO are prepared by chemical methods. The structural and morphological properties were studied by UV-Vis spectroscopy, X-ray diffraction spectroscopy (XRD), scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), and Fourier-transform infrared (FTIR) spectroscopy. The synthesized Ag2O, CoFe2O4, and Ag2O/CoFe2O4/rGO nanomaterials were used for antibacterial activities. The nanomaterials were investigated for antibacterial properties, and results showed that all these materials exhibit superior antibacterial efficacy against gram-positive strains like Staphylococcus aureus (S. aureus) and gram-negative bacterial strains like Escherichia coli (E. coli). According to the antibacterial results, the nanocomposite significantly reduced the reproduction rate of E. coli and S. aureus. Moreover, the antibacterial activity of nanomaterials showed a gradual increment with an increase in the concentration of the materials. These results demonstrate that all the nanomaterials, as a kind of antibacterial material, have a great potential for application in a wide range of biomedical applications.