bluuebunny/crossref_metadata_2025_split huggingface dataset:
Smart Mobile Worker for FCS PM
Authors: Al Kindi Omar Al Khatab , Al Kalbani Fahad Khamis 
Published: October 2022

Abstract
Since 1980s, most process industries have embraced digital technology for better understanding and recourses managing, increase overall efficiencies and improve safety. In fact, the Industrial Automation (IA) field has given increase to the 3rd industrial revolution for nearly half a century. However, since then, IT megatrends have driven a significant growth in storage, information, computing power, and bandwidth. In this environment, the present of the network has enabled an open platform in sharing ideas, tool development and an agile strategy to exponentially speed up the software development effects, new business models and algorithms. Consequently, digital transformation and digitalization are becoming progressively in many industry. Digitalization can be defined as a use of current available digital technologies to enhance the business model or specific process and deliver new value and revenue. It is considered also as an approach that making processes and workflow easier than before and more efficient. Especially in today's tech - driven world, it is really important to adopt a digital culture in order to enhance the overall work and succeed. PDO is pushing the frontier of digitalisation in its maintenance strategies by implementing various modern smart technologies that can make its approach more succeed and effective. Hence, Smart Mobile Worker (SMW) is designed where it gives the site team the capability to log their maintenance observations in a hand-held tablet easily. Then, all the entered reports are transferred immediately to Nibras system where there is a customized design dashboard that gives the first line support team the overall performance of the Field Control System (FCS) along with alerts and notifications for any abnormal behavior. SMW and Nibras system are both integrated to conduct remote maintenance and proactively identify any exists issues in FCS/IPS/FGS system cabinets. This type of platform helps in bringing the expert office capabilities into the field which will help in taking the decision immediately and in short time.
Empowering Frontline Workers to Work Effectively with Hands-Free Computing Devices
Authors: Alyassi Hussain Ahmed Saeed 
Published: October 2022

Abstract
The purpose of this document is to provide an insight to ADNOC's latest technology ‘Digital Workforce’ which supports the ‘Wearable Device’ for the frontline workers at ‘ADNOC Onshore’ facilities. Mobility and real-time data exchange are of primary importance to enhance operational efficiency, safety and reliability in oil and gas industries. In the past, the trend was to equip field workers with industrial smartphones or similar devices to ensure uninterrupted communication. ‘Wearable technology’ aims to take the advantage of the hands-free functionality allowing workers to perform their duties while maintaining the communication remotely with the control room. The application, ‘Digital Workforce’, provides an effective software solution for the device to work efficient and flawless. The methodologies adopted aids to overcome the limitations of using such devices in areas with limited network bandwidth while adhering to the security parameters set by ADNOC. The document discusses on the technical specifics of the application, the advantages and the limitations of the current version of the solution.
Shell Autonomous Integrity Recognition - Machine Vision Application for Inspections
Authors: Blom David , Berchenko Ilya , Samie Farid , Frajberg Darian 
Published: October 2022

Abstract
Shell Autonomous Integrity Recognition (AIR) on C3 AI Platform is an Artificial Intelligence (AI) application that allows pressure equipment and structural steel inspectors to quickly and easily make use of automated image capture and evaluation to support execution of external integrity inspections. By processing data in the cloud coming from inspections carried out with handheld devices, drones and robots, this application enables inspectors to objectively evaluate issues, identify items that may have been overlooked, reduce the time needed to generate reports, and improve inputs to maintenance planning. By using Shell Autonomous Integrity Recognition on C3 AI Platform, users can improve the quality, efficiency, and standardization of visual inspections.
Successful Implementation of Electronically Delivering the Limit Project for Drilling Process Optimization
Authors: Lamki Hamood , Kalbani Abdallah , Khatri Fahad , Joshi Ashish , Wahaibi Amira 
Published: October 2022

Abstract
Drilling is a complex process and delivering a successful well requires identifying proper technologies and utilizing them efficiently to save time & cost. Today in Oil & Gas industry there is a huge focus on digital technologies to improve Drilling Process efficiency and PDO decided to implement an innovative approach of process optimization by implementing a unique project "electronically Delivering the Limit (eDtL)".
The overall approach with eDtL project was to implement a platform which can provide Drilling Operations team the technical limit for all Drilling Activities, which is the theoretical minimum time required to perform an activity, based on available knowledge and technology.
eDtL system utilizes rig sensors data transmitted in Real-Time from Drilling Rigs to automatically detect the Rig Activity and focus on identifying the areas of Drilling Performance Improvements and minimizing redundant tasks for rig and office teams. The identified opportunities are communicated with rig team for implementation and the performance is tracked again to highlight the improvements.
eDtL system also provides capability for continuous improvement of organizational processes by introducing automation of redundant tasks. One of such improvement was partial automation of Daily Drilling Report which was historically manually recorded by rig team daily.
Automatic Drilling Operations Coding and Classification Utilizing Text Recognition Machine Learning Algorithms
Authors: Amer Mustafa Mohamed , Otaibi Bader M , Othman Amr I 
Published: October 2022

Abstract
Digital transformation is becoming a major goal for oil and gas (O&G) companies, and one major component of digital transformation initiatives is utilizing artificial intelligence (AI) and machine learning (ML) algorithms/techniques to automate critical business processes for the sake of consistency and objectivity. Drilling operations classification and coding is always challenging due to the subjectivity of end users; however, using machine learning and big data to automate operations classification based on natural language operations descriptions provided by drilling personnel can ensure objective and consistent classification.
In this paper, a new approach is introduced for using ML algorithms to classify drilling operations based on the operation description provided by rig personnel in their morning reports, with time broken down using natural language.
The new ML predictive model learns from historical data how to define the proper coding of drilling operations based on operation description to minimize human interaction with the coding system for drilling operations. The approach utilizes a set of prediction models to predict the proper code combinations that classify the activity carried out on the rig floor. Each model of these proposed models will feed its prediction into the next model to define the next level of code predictions. The model was trained using around 800,000 records to define the coding patterns based on operation remarks and then predicted a multi-level operational coding for any given operational remarks.
The model has been tested and validated with around 800,000 records from datasets from multiple years, and showed significate results when compared to recent reports and subject matter expert evaluations showing a very good level of consistency and level of accuracy of more than 80%.
The result of this work is an ML classification model that can reduce daily morning report data entry by 40% and improve the operational classification quality and consistency significantly. This approach also improves internal processes such as services invoicing verification, bit selection and end-of-well reporting.
Expert System for Question Answering on Anomalous Events and Mitigation Strategies Using Bidirectional Transformers and Knowledge Graphs
Authors: Sreekantan Jaijith , Hutchison Chad , Amatya Pratuat 
Published: October 2022

Abstract
Daily drilling reports provide vital information for well planning as they capture anomalous events and mitigation measures during drilling operations. Previous works predominantly focus on search frameworks for information retrieval from these reports. However, the context between searches is lost, preventing users from narrowing down to the exact answer. Here, we present a transformer-based closed domain conversational agent for longer dialogues to guide users to contextual information for anomalous drilling events through natural language.
Automated text extraction, cleaning and validation tasks are initially performed to resolve data quality issues prior to language modeling on a validated data set. Subsequently, a knowledge-based graph is created by node embedding using entity extractions and by learning the semantic-level relationships between entity nodes such as well names and events. Further, conversational agents are trained on the knowledge graphs for natural dialogue generation using neural machine translation models. Here, users’ questions are translated into a query in a structured language that is evaluated directly over the knowledge graph in order to generate the desired answers.
The workflow was tested on an asset with multiple wells experiencing several anomalous events during drilling such as stuck pipe, circulation losses and kicks. The end-to-end workflow was tested on its ability to retrieve anomalous events and present mitigation measures in the aforementioned data set based on the descriptions input by survey participants. Performance on the anomaly extraction, attribute mapping and mitigation performance were evaluated through F1 scores. A significantly high F1 score was recorded for anomaly extraction. This is predominantly driven by high precision due to explicit modeling of the reports as a knowledge graph. In addition to testing the workflow end to end, we tested the knowledge graph representation in isolation. For this, ranking metrics and triple classification with negative samples were used for the evaluation. The adjusted mean rank index was close to one, indicating high performance. Structured querying on the knowledge graphs also showed high accuracy for classifying anomalous events in the drilling report.
The work described in this paper automates the end-to-end workflow for building an expert system for answering questions about anomalous events and mitigation strategies using daily drilling reports. Our novel approach using a knowledge graph with a transformer-based conversational agent enables users to perform detailed interactive investigation of anomalous events observed in daily drilling reports and create mitigation strategies. The workflow also allows for incorporating prior domain knowledge from drilling experts.
Cultural adaptation and administration of public awareness questionnaire on hearing health and hearing loss in Karnataka
Authors: Cherian Jincy Mary , P.G Bhargavi 
Published: October 2022

Abstract
Background: Good hearing health is necessary for an individual to communicate and stay oriented within the environment. Certain factors like present lifestyle, trauma, illness, genetic disorders, age, and leisure activities can lead to hearing loss when left unattended. The study aims to culturally adapt, administer, and assess public awareness of Karnataka's hearing loss and hearing health.
Method: The design used in this study was a cross-sectional survey design. The sampling method used in this study was Quota sampling. In total, 720 participants aged 20-60 completed a culturally adapted questionnaire. The questionnaire consisted of 22 questions targeting the awareness of hearing loss and hearing health, focusing on four domains: (1) Knowledge about infant hearing loss, (2) Cleaning and treatment, (3) The effect of overexposure to noise and loud sounds, (4) Diagnostic delay.
Results: Approximately 70% of the correct responses were given to almost all the statements. However, specific essential Knowledge was lacking. Only 56% and 50.23% knew about specialized tests available for a hearing evaluation and the recommended standards on the duration of noise exposure.
Conclusion: There is a general lack of public awareness about ringing sensation and its impact on an individual's daily activities. Most people were also unaware of recommendation guidelines regarding reducing exposure duration to high-intensity noises; hence, these findings support the need for noise reduction informative initiatives. In conclusion, this audiological questionnaire appeared to be a simple, practical, and reliable tool.
The outcome showed a need for continued development of comprehensive hearing conservation programs focusing on hearing aid management, early infant hearing loss detection, and noise exposure prevention, which will be necessary to continue developing.
Information Infrastructure, Spatial Spillover and the Status of Digital Trade
Authors: Chunbao Ge , Jinping Yu , Na Li , Yapan Liu , Xiaojin Yuan 
Published: October 2022

Abstract
Based on the value-added trade data of UNCTAD from 2003 to 2018, this paper firstly constructs a topological network of digital industry value-added directional trade along the 'Belt and Road' route(B&R) using the complex network method. Moreover, it analyzes the evolution characteristics of the digital trade status(DTS) of each country. Then we construct a theoretical model of the trade network of three countries to analyze the relationship between information infrastructure and its spatial spillover and DTS and conducts empirical tests. The results show that: (1) The digital value-added trade network of the countries along B&R has gradually expanded and formed a "Three-Legged" distribution pattern with China, Singapore, and Malaysia as the core and stable structure, but the DTS of most countries is relatively low. (2) There is the apparent positive spatial spillover of information infrastructure in the countries along B&R, and each country's division of labor and trade in the digital industry shows a "complementary promotion" relationship. (3) The improvement of information infrastructure in the countries along B&R will not only directly promote the DTS of the country but also indirectly promote the improvement of information infrastructure in the neighboring geographic countries through spatial spillover, thus indirectly and more positively affecting the DTS of the country. (4) The positive spatial spillover of information infrastructure is affected by the degree of digital trade competition among countries, and too high a degree of competition will hinder its spillover effect.
Omega-3 polyunsaturated fatty acids play roles in skeletal muscle-adipose tissue crosstalk through myokines
Authors: Wei Wenting , Zeng Huanting , Mao Limei 
Published: October 2022

Abstract
Skeletal muscle-adipose tissue crosstalk is important for maintaining metabolic homeostasis. Myokines play an indispensable role in the skeletal muscle-adipose tissue crosstalk. This study was conducted to elucidate the association between Omega-3 polyunsaturated fatty acids (n-3 PUFAs) and myokine in vitro and figure out the role of n-3 PUFAs in the skeletal muscle-adipose tissue crosstalk. As result, different forms of n-3 PUFAs increased the expression of multiple myokines in C2C12 myotubes. The expression of p-AMPK and PGC-1α in C2C12 myotubes were up-regulated by DHA/EPA mixture. The culture supernatant of myotube cells incubated with the DHA/EPA mixture regulated the lipid metabolism-related genes and stimulated browning in adipocytes. Therefore, n-3 PUFAs play roles in the skeletal muscle-adipose tissue crosstalk by regulating the expression and release of myokines associated with lipid metabolism and browning. Part of the mechanism of this process should be attributed to n-3 PUFAs activating AMPK signaling in C2C12 myotube.
Predictive and prognostic markers from endoscopic ultrasound with biopsies during definitive chemoradiation therapy in esophageal squamous cell carcinoma
Authors: Du Qingwu , Wu Xiaoyue , Zhang Kunning , Cao Fuliang , Zhao Gang , Wei Xiaoying , Guo Zhoubo , Li Yang , Dong Jie , Zhang Tian , Zhang Wencheng , Wang Ping , Chen Xi , Pang Qingsong 
Published: October 2022

Abstract
Introduction:
Endoscopic ultrasound (EUS) may play a role in evaluating treatment response after definitive chemoradiation therapy (dCRT) for esophageal squamous cell carcinoma (ESCC). This study explored the prognostic markers of EUS with biopsies and developed two nomograms for survival prediction.
Methods
A total of 821 patients newly diagnosed with ESCC between January 2015 and December 2019 were reviewed. We investigated the prognostic value of the changes in tumor imaging characteristics and histopathological markers by an interim response evaluation, including presence of stenosis, ulceration, tumor length, tumor thickness, lumen involvement, and tumor remission. Independent prognostic factors of progression-free survival (PFS) and overall survival (OS) were determined using Cox regression analysis and further selected to build two nomogram models for survival prediction. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to respectively assess its discriminatory capacity, predictive accuracy, and clinical usefulness.
Results
A total of 155 patients were enrolled in this study and divided into the training (109 cases) and validation (46 cases) cohorts. Tumor length, residual tumor thickness, reduction in tumor thickness, lumen involvement, and excellent remission (ER) of spatial luminal involvement in ESCC (ER/SLI) differed significantly between responders and non-responders. For patients undergoing dCRT, TNM stage (P = 0.001, 0.002), tumor length (P = 0.013, 0.008), > 0.36 reduction in tumor thickness (P = 0.004, 0.004) and ER/SLI (P = 0.041, 0.031) were independent prognostic markers for both PFS and OS. Time-dependent ROC curves, calibration curves, and DCA indicated that the predicted survival rates of our two established nomogram models were highly accurate.
Conclusion
Our nomogram showed high accuracy in predicting PFS and OS for ESCC after dCRT. External validation and complementation of other biomarkers are needed in further studies.
Dry Needling on Latent and Active Myofascial Trigger Points Versus Oral Diclofenac in Patients With Knee Osteoarthritis: a Randomized Controlled Trial
Authors: Ma Yan-Tao , Dong Yu-Lin , Wang Bo , Xie Wen-Pin , Huang Qiang-Min , Zheng Yong-Jun 
Published: October 2022

Abstract
Background: Latent and active myofascial trigger points (MTrPs) in knee-associated muscles may play a key role in pain management among patients with knee osteoarthritis (KOA). The aim of this study was to investigate the effect of dry needling treatment on pain intensity, disability, and range of motion (ROM) in patients with KOA.
Methods: This randomized, single-blinded, clinical trial was carried out for 6 weeks of treatment and 6-monthfollow-up. A total of 98 patients met the entry criteria and were randomly assigned to the dry needling latent and active myofascial trigger point (MTrPs) with the stretching group or the oral diclofenacwith the stretching group. Numeric Pain Rating Scale (NPRS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and ROM were statistically analyzed before and after treatment and at the 6-month follow-up.
Results: A total of 42 patients in the dry needling group (DNG)and 35 patients in the diclofenac group (DG), respectively,completed the study, and there was no significant difference in the general data between the two groups. After treatments, both the groups showed a good effect in knee pain, function, and ROM, However, the DNG showed asignificantly better result than the DG. Especially in the results of the 6-month follow-up, the DNG showed much better results than the DG.
Conclusions: Dry needling on latent and active MTrPscombined with stretching and oral diclofenac combined with stretching can effectively relieve pain, improve function, and restore knee ROM affected by KOA. However, the effects of dry needling and stretching are better and longer lasting than those of oral diclofenac and stretchingfor at least 6 months.
Trial registration: Registered in the Chinese Clinical Trial Registry (www.chictr.org.cn) in 17/11/2017 with the following code: ChiCTR-INR-17013432.
Socio-demographic factors associated with COVID-19 vaccine uptake and refusal among Ugandan women
Authors: Backhaus Andreas 
Published: October 2022

Abstract
Background: This paper analyzes associations of socio-demographic factors with the uptake of COVID-19 vaccines, the refusal to get vaccinated against COVID-19, and various reasons stated for refusing vaccination against COVID-19 among a representative sample of Ugandan women.
Methods: This paper utilizes a representative survey collected among women aged 15-49 years in Uganda between September and November 2021. Regression analyses are used to study the associations of a broad range of socio-demographic characteristics with COVID-19 vaccine uptake, refusal of vaccination, and reasons for refusal among the respondents.
Results: 4287 women were included in the analysis. 11.26% of them were vaccinated against COVID-19, 75.48% were willing to get vaccinated, 13.26% were unwilling to get vaccinated. Fear of side effects is the most commonly stated reason for refusing vaccination (68%). Factors significantly and positively associated with being vaccinated against COVID-19 are age, higher education, urban residency, recent labor supply, having savings, partial instead of complete income loss during the pandemic, and usage of modern contraceptives. Factors significantly and positively associated with refusing vaccination against COVID-19 are urban residency and current pregnancy, while age, relative household wealth, having savings, and using modern contraceptives are factors associated with a lower likelihood of refusing vaccination, albeit with varying statistical significance. Few factors are strongly related to the stated reasons for refusing the vaccines; the fear of side effects significantly increases with age, while having received negative information on the vaccines is significantly less common among women with higher education.
Conclusions: This study reveals a low COVID-19 vaccination rate and a high willingness to get vaccinated in the future among Ugandan women aged 15-49 years in the second half of 2021. Positive age and education gradients in vaccine uptake point to inequity in access to vaccination, potentially resulting from prioritizations of groups at particularly high risk. Unwillingness to be vaccinated is relatively low, but systematic factors behind vaccine refusal are hardly to be found, even less so for particular reasons given for refusal.