bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identifying Formation Creep: Ultrasonic Bond Logging Field Examples
Authors: Govil Amit , Nevøy Harald , Hovda Lars , Obando Palacio Guillermo A. , Kjeldaas Geir 
Published: September 2021

Summary
As part of plug and abandonment (P&A) operations, several acceptance criteria need to be considered by operators to qualify barrier elements. In casing annuli, highly bonded material is occasionally found far above the theoretical top of cement. This paper aims to describe how the highly bonded material can be identified using a combination of ultrasonic logging data, validated with measurements in laboratory experiments using reference cells and how this, in combination with data from the well construction records, can contribute to lowering the costly toll of P&A operations.
Ultrasonic and sonic log data were acquired in several wells to assess the bond quality behind multiple casing sizes in an abandonment campaign. Data obtained from pulse-echo and flexural sensors were interactively analyzed with a crossplotting technique to distinguish gas, liquid, barite, cement, and formation in the annular space. Within the methodology used, historical data on each well were considered as an integral part of the analysis. During the original well construction, either water-based mud (WBM) or synthetic oil-based mud (OBM) was used for drilling and cementing operations, and some formation intervals consistently showed high bonding signatures under specific conditions, giving clear evidence of formation creep. Log data from multiple wells confirm that formation behavior is influenced by the type of mud used during well construction.
The log data provided information of annulus material with a detailed map of the axial and azimuthal variations of the annulus contents. In some cases, log response showed a clear indication of formation creep, evidenced by a high bond quality around the production casing where cement cannot be present. Based on observations from multiple fields in the Norwegian continental shelf, a crossplot workflow has been designed to distinguish formation from cement as the potential barrier element. NORSOK Standard D-010 (2013) has initial verification acceptance criteria both for annulus cement and creeping formation as a well barrier element, both involving bond logs; however, in the case of creeping formation, it is more stringent stating that “two independent logging measurements/tools shall be applied.” This paper aims to demonstrate how this can be done with confidence using ultrasonic and sonic log data, validated against reference barrier cells (Govil et al. 2020).
Logging responses like those gathered during full-scale experiments of reference barrier cells with known defects were observed in multiple wells in the field. Understanding the phenomenon of formation creep and its associated casing bond signature could have a massive impact on P&A operations. With a successful qualification of formation as an annulus barrier, significant cost and time savings can be achieved.
Conditioning Model Ensembles to Various Observed Data (Field and Regional Level) by Applying Machine-Learning-Augmented Workflows to a Mature Field with 70 Years of Production History
Authors: Vanegas Gisela , Nejedlik John , Neff Pascale , Clemens Torsten 
Published: September 2021

SummaryForecasting production from hydrocarbon fields is challenging because of the large number of uncertain model parameters and the multitude of observed data that are measured. The large number of model parameters leads to uncertainty in the production forecast from hydrocarbon fields. Changing operating conditions [e.g., implementation of improved oil recovery or enhanced oil recovery (EOR)] results in model parameters becoming sensitive in the forecast that were not sensitive during the production history. Hence, simulation approaches need to be able to address uncertainty in model parameters as well as conditioning numerical models to a multitude of different observed data.Sampling from distributions of various geological and dynamic parameters allows for the generation of an ensemble of numerical models that could be falsified using principal-component analysis (PCA) for different observed data. If the numerical models are not falsified, machine-learning (ML) approaches can be used to generate a large set of parameter combinations that can be conditioned to the different observed data. The data conditioning is followed by a final step ensuring that parameter interactions are covered. The methodology was applied to a sandstone oil reservoir with more than 70 years of production history containing dozens of wells.The resulting ensemble of numerical models is conditioned to all observed data. Furthermore, the resulting posterior-model parameter distributions are only modified from the prior-model parameter distributions if the observed data are informative for the model parameters. Hence, changes in operating conditions can be forecast under uncertainty, which is essential if nonsensitive parameters in the history are sensitive in the forecast.
Real-Time Multi-Event Anomaly Detection using Elliptic Envelope and A Deep Neural Network for Enhanced MPD Robustness
Authors: Gu Qifan , Fallah Amirhossein , Ashok Pradeepkumar , Chen Dongmei , Van Oort Eric 
Published: September 2021

Abstract
In managed pressure drilling (MPD), robust and fast event detection is critical for timely event identification and diagnosis, as well as executing well control actions as quickly as possible. In current event detection systems (EDSs), signal noise and uncertainties often cause missed and false alarms, and automated diagnosis of the event type is usually restricted to certain event types.
A new EDS method is proposed in this paper to overcome these shortcomings. The new approach uses a multivariate online change point detection (OCPD) method based on elliptic envelope for event detection. The method is robust against signal noise and uncertainties, and is able to detect abnormal features within a minute or less, using only a few data points. A deep neural network (DNN) is utilized for estimating the occurrence probability of various drilling events, currently encompassing (but not limited to) six event types: liquid kick, gas kick, lost circulation, plugged choke, plugged bit, and drillstring washout. The OCPD and the DNN are integrated together and demonstrate better performance with respect to robustness and accuracy. The training and testing of the OCPD and the DNN were conducted on a large dataset representing various drilling events, which was generated using a field-validated two-phase hydraulics software.
Compared to current EDS methods, the new system shows the following advantages: (1) lower missed alarm rate; (2) lower false alarm rate; (3) earlier alarming; and (4) significantly improved classification capability that also allows for further extension to even more drilling events.
The Perspectives of Patients with Parkinson's Disease: A Systematic Review and Thematic Synthesis
Authors: Nørgaard Birgitte , Titlestad Signe Beck , Abrahamsen Charlotte , Marcussen Michael 
Published: September 2021

Abstract
Background: Parkinson’s disease has considerable impact on the quality of life of both patients and their caregivers. Patients’ perspectives are a source of vital knowledge that informs health professionals’ ability to provide individualised and patient-centered care. The aim of this systematic review was to identify the perspectives of patients with Parkinson’s disease on treatment, care and rehabilitation.Methods: We conducted a systematic review and searched the following electronic bibliographic databases: MEDLINE, EMBASE, CINAHL, PsycInfo and Scopus for original studies published before June 2020. Grey literature was searched at www.parkinson.org, http://www.epda.eu.com, www.apdaparkinson.org and in the OpenSIGLE and HMIC databases. We included studies focusing on patients with Parkinson’s disease aged 18 or older that reported patients’ perspectives on treatment, care and rehabilitation. Results were analysed using thematic synthesis. Results: Ten studies were included, each of them applying qualitative methods and including between 1 and 28 patients. A total of 156 codings led to 17 descriptive themes which resulted in three analytic themes: Significance of self, Significance of informal caregivers and peers, and Significance of professionals.Conclusions: Our findings could be important in helping healthcare professionals plan and deliver treatment, care and rehabilitation based on patients’ priorities in the context of suffering from a chronic disease. The results might also guide future research regarding patient perspectives, as well as how and why to incorporate patients’ perspectives in healthcare delivery. Systematic review registration: The protocol was registered in Prospero (183551).
Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design
Authors: Sharma Aanchna , Mukhopadhyay Tanmoy , Rangappa Sanjay Mavinkere , Siengchin Suchart , Kushvaha Vinod 
Published: September 2021

Abstract
The superior multi-functional properties of polymer composites have made them an ideal choice for aerospace, automobile, marine, civil, and many other technologically demanding industries. The increasing demand of these composites calls for an extensive investigation of their physical, chemical and mechanical behavior under different exposure conditions. Machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modeling, leading to unprecedented insights and exploration of the system properties beyond the capability of traditional computational and experimental analyses. Here we aim to abridge the findings of the large volume of relevant literature and highlight the broad spectrum potential of ML in applications like prediction, optimization, feature identification, uncertainty quantification, reliability and sensitivity analysis along with the framework of different ML algorithms concerning polymer composites. Challenges like the curse of dimensionality, overfitting, noise and mixed variable problems are discussed, including the latest advancements in ML that have the potential to be integrated in the field of polymer composites. Based on the extensive literature survey, a few recommendations on the exploitation of various ML algorithms for addressing different critical problems concerning polymer composites are provided along with insightful perspectives on the potential directions of future research.
Influence of Ar Gas Pressure On The Structural And Optical Properties And Surface Topography of Al-Doped ZnO Thin Films Sputtered By DC-Magnetron Sputtering Method
Authors: Shiravand Mahdiyeh , Ghobadi Nader , Hatam Ebrahim Gholami 
Published: September 2021

Abstract
In this work, Aluminum doped Zinc oxide thin films were sputtered on glass substrate by the direct current (DC) magnetron sputtering method. The influence of Ar gas pressure on the structural and optical properties was measured. The optical parameters were calculated by UV- Visible spectroscopy, the nature of transition reveals direct allowed transition for the prepared films. Also, some physical quantities such as the strength of electron-phonon interaction, dissipation factor (tanδ) in the visible region and the lattice dielectric constant were presented for these thin films. The AFM analysis extracts surface parameters of the AZO thin films that help us to investigate the surface analysis with numerical data. The band gap energy and transition index without any presumption about transition natural were calculated from Derivation Ineffective Thickness Method (DITM) and the reaction of Ar gas pressure plays an essential part in controlling the physical quantities of AZO thin films.
Study on the Relationship Between Musculoskeletal Disorders, BDNF Gene, TPH-2 Gene, and Mental Health in Coal Mine Workers in China
Authors: Jiang Ting , Li Xue , Xue Qiaoyun , Liu Jiwen 
Published: September 2021

Abstract
Background: Coal is the core energy source of the global energy system. To ensure the sustainable development of the coal industry, the mental health of coal miners is crucial. Methods: This cross-sectional study was conducted between August 2018 and June 2019. A total of 1,675 coal miners were surveyed using the Musculoskeletal Disorders (MSDs) Questionnaire and the Symptom Checklist-90 (SCL-90). At the same time, polymorphisms in the BDNF gene (rs6265, rs10835210) and TPH2 gene (rs4570625, rs4131347) in whole blood DNA were detected to analyze the influence of a gene-environment interaction on mental health. Results: The results showed that the mental health status of coal miners was poorer than the Chinese norm (P < 0.05). The mental health status of workers with MSDs was lower; MSDs interact with the BDNF gene rs6265 and TPH2 gene rs4570625, which affects mental health. Conclusions: It is suggested that employers should improve the working environment of workers, shorten the working time, pay attention to the mental health of workers, and provide timely psychological counseling to workers who suffer from mental health problems, so as to improve workers’ mental health and working ability, as well as their quality of life.
‘it Is Beyond Our Reach’: Policies and Infrastructure Influencing Postpartum Care in Rural Kenya
Authors: Kemei Janet , Etowa Josephine 
Published: September 2021

Abstract
BackgroundMaternal mortality in low middle-income countries is still high. Like most countries in Sub-Saharan Africa, the progress towards reducing maternal mortalities in Kenya is slow. Approximately 488 women out of every 100,000 live births die during the childbearing process. Kenya has put in place several strategies to mitigate maternal mortalities. For instance, Kenya introduced free maternity services in 2013 to remove financial barriers to skilled health services for mothers and children under five years old. Hence, it is necessary to explore how the policies and infrastructure intersect with other socioeconomic factors to influence postpartum care in rural Kenya to mitigate maternal and infant deaths.MethodsThis qualitative research conducted in-depth focused ethnographic (FE) interviews with 23 nurses and midwives working in nine health centres and the County Hospital in Nandi County, Kenya, between July 2017 and February 2018. We used thematic analysis approach as described by Braun and Clarke to analyze the data. Lincoln and Guba criteria for establishing the trustworthiness of data was used. ResultsThe analysis of data generated six themes. The findings from the theme, Policies and Infrastructure Influencing Postpartum Care will be discussed in this paper. The findings will be discussed under three sub-themes 1) Free maternity services, 2) Adherence to perinatal care guidelines, and 3) Recruitment and retention of nurses and midwives. Facilities lacked the essential equipment and supplies required to provide these services, recruitment and retention of staff, demotivation of healthcare providers, lack of regular training and supervision of staff, and lack of adherence to postpartum guidelines. These issues intersected to determine the quality of skilled postpartum services provided to childbearing women and their families as well as women and infants’ overall perinatal health outcomes. ConclusionThe findings have underscored the importance of having a functional healthcare system that supports both the clinical and emotional aspects of the women and healthcare providers. Efforts should be directed into addressing the negative factors influencing care provision at the facility level. Suboptimal care could cause women not to attend skilled health care and sabotage the global goals of eliminating maternal and infant mortalities. This can be achieved by creating policies that considers the diverse causes and power-relations withing the healthcare organization.
Trends in Household Air Pollution Contributed by the Type of Cooking Fuel Used in Ugandan Households from 2000 to 2016 and Associated Household Factors: An Analysis of Demographic and Health Surveys
Authors: Abila Derrick Bary , Racheal Nalunkuma , Wasukira Sulaiman Bugosera , Ainembabazi Provia , Siya Aggrey , Kisuza Ruth Ketty , Nakiyingi Elizabeth Kiyingi , Mustafa Asia , Adebisi Yusuff Adebayo , III Don Eliseo Lucero-Prisno 
Published: September 2021

Abstract
Background

The type of cooking fuel used in households can expose the members to polluted air which has been shown to have multiple health effects. We describe the trend in household air pollution (HAP) caused by the type of cooking fuel used in Ugandan households from 2001 to 2016 and explore the household factors associated with it.
Methods

This was a retrospective analysis of four datasets of Ugandan demographic and health surveys (DHS) conducted from 2000/2001 to 2016 with the household as the unit of analysis. We included variables that described the households, the type of cooking fuel, and where the food was cooked. A household was considered to have exposure to HAP if they used cooking fuels like coal, charcoal, wood, straw/shrubs/grass, crops, or animal dung for cooking. Bivariate and multivariate logistic regression was used to determine the association between household characteristics and exposure to household air pollution (HAP) from the most recent DHS of 2016.
Results

A total of 45,376 households were included in the study. In all the surveys, the majority of the households were rural (74.33–85.11%), were headed by a male (68.98–72.46%), and with their highest educational attainment as incomplete primary education (37.21–42.44%). In all the surveys, the majority of the households used wood as a cooking fuel (71.69–82.08%). Most of the households were exposed to HAP (96.69–99.80%). From the multivariate logistic regression analysis, households with a household head having attained either complete secondary education [Adjusted Odd ratio: 0.13, 95% Confidence interval (0.03–0.58), p-value = 0.008] or higher education [Adjusted Odd ratio: 0.11, 95% Confidence interval (0.03–0.48), p-value = 0.003] were less likely to be exposed to HAP when compared with households with a household head having no education.
Conclusions

This study revealed that most households in Uganda have exposure to household air pollution caused by the type of cooking fuel used in the household. This underscores the need for innovations in cooking fuels like clean energy stoves that are cost-effective and cause less exposure to household air pollution.
Slide Type Landslide Susceptibility Assessment of the Büyük Menderes Watershed Using Artificial Neural Network Method
Authors: Tekin Senem , Çan Tolga 
Published: September 2021

Abstract
The Büyük Menderes watershed is the largest drainage watershed in Western Anatolia with an area of approximately 26000 km2. In the study area, almost 863 landslides occurred, extending over 222 km2 with a mean landslide area of 0.21 km2. In this study, landslide susceptibility assessment was carried out using Artificial Neural Network method which is one of the data driven methods. Geology, digital elevation model, slope, topographic wetness index, roughness index, plan, profile curvatures, and proximity to the active faults and rivers were used as landslide conditioning factors. In susceptibility assessments, landslides were separated by 70 % analysis, 15 % test and validation data sets by random selection method. The performance of the landslide susceptibility map was assessed by the area under the receiver operating characteristic curves, error histogram, and confusion matrix, respectively. The area under the receiver operating characteristic curves, analysis, testing, validation, landslides and study ares was found 0.82, 0.84, 0.86, 0.82. The susceptibility map had a high perediction rate in which high and very high susceptible zones corresponded to 26 % of the study area including 82 % of the recorded landslides.
Using The Random Forest Algorithm To Detect The Activity of Thyroid-Associated Ophthalmopathy
Authors: Wang Minghui , Zhang Hanqiao , Dong Li , Li Yang , Hou Zhijia , Li Dongmei 
Published: September 2021

Abstract
Objective: The aim of this study is to establish a random forest model to detect active and quiescent phases of patients with Thyroid-associated ophthalmopathy (TAO) and to evaluate its diagnostic performance.Methods：A total of 146 patients (292 eyes) who were diagnosed with TAO and were treated in the Ophthalmology Outpatient Clinic of Beijing TongRen hospital were retrospectively included in the study. We took the clinical activity score of TAO as the target; took gender, age, smoking status, I-131 treatment history, thyroid nodules, thyromegaly, thyroid hormone and TSH-receptor antibodies (TRAb) as predictive characteristic variables to establish a random forest model. The proportion of the training group to the testing group was 7:3. We analyzed the model’s accuracy, precision, sensitivity, specificity, positive predictive value (PPV), negative predictive value (PPV), F1 score and out-of-bag (OOB) error, with the accuracy, the brier loss and the area under the receiver operating characteristic curve compared with logistic regression model.Results：Our model has an accuracy of 0.93, a sensitivity of 0.88, a specificity of 0.96, a positive predictive value of 0.94, a negative predictive value of 0.93, an F1 score of 0.91 and an OOB error of 0.12. The accuracy of the random forest model and the logistic regression model were 0.93 and 0.79, respectively, the brier loss were 0.06 and 0.20, and the area under the receiver operating characteristic curve were 0.95 and 0.86.Conclusion：By integrating these high-risk factors, the random forest algorithm can be used as a complementary diagnostic method to determine the activity of TAO, showing prominent diagnostic performance.