bluuebunny/crossref_metadata_2025_split huggingface dataset:
Expediting Chemical Enhanced Oil Recovery Processes with Prediction of Chemical Reaction Yield Using Machine Learning
Authors: Kumar Ashutosh , Rajendran Robin Marlar 
Published: October 2022

Abstract
Chemical enhanced oil recovery involves enormous combinations of chemicals, surfactants, etc. The reservoir properties such as temperature, capillary pressure, permeability keeps changing, making the process of identification of suitable chemicals even more challenging. Data driven modelling holds solutions for the complexity involved in identification of most suitable parameters for chemical enhanced oil recovery. Over the last decade, Artificial Intelligence has found its numerous applications in different branches of chemistry. From the selection of chemicals to the operating conditions during synthesis all can be estimated by the use of deep learning models. This paper presents yield prediction which is of high economic significance for chemical enhanced oil recovery, because they enable calculation of investment versus return.
These models give us the conversion of reaction into products before performing the lab experiment. This will help chemists in selecting high performance chemicals for specific reservoirs without spending time on costly iterative chemical processes. These models require application of deep learning architectures like transformers and natural language processing techniques like tokenization for the prediction task. Encoder models like BERT are used for receiving the information on chemical reactions in text-based form for a reaction which is then combined with a regression extension layer to give us the desired reaction yield.
We demonstrate our model on a HTE dataset with an excellent prediction score. Efforts are also made on the USPTO patent dataset which covers a wide variety of chemical reaction space. The USPTO patent dataset consists of almost every chemical reaction published since late 1970s till 2006. Diverse techniques starting with Multi Layer Perceptrons, Sequence to sequence modelling, Long short term memory models and finally transformers are employed for the improvement of accuracy of patent reactions. The paper presents detailed comparative results of predicting chemical reaction yield, and the enhancements that it will bring to Chemical Enhanced Oil Recovery.
Reaction yield prediction models receive very little attention in spite of their enormous potential of determining the reaction conversion rates and its contribution to chemical enhanced oil recovery processes . The paper introduces a novel approach of modelling chemical reaction yield with deep learning models to the petroleum community. Unprecedented result of accuracy beyond 90% in predicting chemical reactions yield and its significance in chemical enhanced oil recovery has been proposed in the paper.
Semi-Supervised Learning for Geotechnical Soil Property Estimation in Offshore Windfarm Sites
Authors: Di Haibin , Abubakar Aria 
Published: October 2022

Abstract
Site characterization and monitoring of the subsurface formations around wind turbine locations are crucial for reliable wind farm construction, operation and maintenance. In order to extract relevant information about subsurface soils, ultrahigh-resolution (UHR) seismic survey and geotechnical cone- penetration testing (CPT) is often acquired, processed, interpreted and integrated, which could be repeated over time for site monitoring purposes. Due to the size of the area to be investigated and the manual efforts to complete multiple steps in the traditional workflow, the turnaround time for soil property estimation in a wind farm site can be quite long. In this study we implement a semi-supervised learning workflow to automate the task, which integrates URH seismic and CPT logs through two convolutional neural networks (CNNs), with one for seismic denoising and feature engineering (SDFE) and the other for seismic-CPT integration (SCI), which reduces the difficulties in CNN training due to poor data quality and small data quantity. The two components are connected by implementing the encoder of the pretrained SDFE-CNN as part of the SCI-CNN encoder. As tested on a public wind farm site, the use of deep learning leads to promising results in terms of both quality and efficiency. The proposed workflow is also extensible to include additional information, such as structure and velocity models, for further constraining the SCI-CNN.
Highlights: A semi-supervised learning workflow is proposed for soil property estimation from UHR seismic and CPT tests in a wind farm site,allows estimating the essential soil properties such as cone-tip resistance from post-stack UHR seismic as tested on a real windfarm site HKZ, andreduces the turnaround time of windfarm site characterization compared to traditional workflows.
Making Under Uncertainty - Edge Intelligent Decision for Drilling: From Data to Actions in Real Time Well Engineering
Authors: Samuel Robello , Agrawal Abhishek 
Published: October 2022

Abstract
Planning and learning are two primary approaches to intelligent decision making at the edge when the well is constructed. Planning enables us to take immediate actions far into the future, but it requires accurate well engineering models, which are often difficult to acquire in practice. The models are built on engineering assumptions which may not be valid all the time. But the data will provide additional support to suppress the assumption and improve the models. This paper presents the results that can be used under uncertainty through planning, through learning, most importantly by integrating planning and learning.
Planning and learning are two primary approaches to intelligent decisions. When it comes to the edge decision whether it is manual or semi-automated or fully automated (Dupre, 2013) it requires tighter coupling of engineering models through microservices and reinforced learning from the data and feedback from the driller. Five major types of uncertainties are considered in calculating the drilling operational parameters are measurement uncertainty, data uncertainty, engineering model uncertainty, computational or algorithmic uncertainty and decision uncertainty. Several examples are presented as the well is steered and navigated with interactive tasks.
Surface hookload and torque values serve as good indicators for some undesirable scenarios or anomalies during drilling, such as stuck pipe, buckling, and inadequate hole cleaning. However, to detect these risks, it requires drilling engineers to perform engineering model calibration manually and regularly, which costs more efforts and poses significant uncertainties on the detection. This paper describes how these problems are circumvented in addition by providing project ahead paths based on various constraints in real-time with predicted uncertainty zone. This option at the edge makes it possible for drilling engineers to monitor live drilling wells anywhere and anytime while enabling the rig personnel to make significant improvement to operations. Hence, the optimization is no longer static and becomes a dynamic function of depth. These conditions result in constantly varying constraints and, thereby, constantly varying optimized operating parameters to maximize the rate of penetration or minimize specific energy or well cost. This study presents a real-time optimization technique for rate of penetration with energy-based models. Based on the place and position of the bit, the updated data has been used to modify the proposed well design on the fly. The above underpinning methodology have been supported with practical field examples.
Case Study for the Deployment of Private LPWA (Low Power Wide Area) Network in ADNOC Onshore for Remote Monitoring and Operations
Authors: Ahmed Muhammad Shahzad , Al Bloushi Eisa Yousef , Al Bloushi Mahdi Abdula , Ali Asad 
Published: October 2022

Abstract
Remote monitoring and operation are a key aspect in oil and gas facilities due to remoteness of producing and gathering facilities. Traditionally, SCADA system based on wired Fiber Optics or wireless radio communication is used to gather this remote data and perform supervisory actions. During last decade IoT (Internet of things) solutions are being adopted in oil and gas industries because of their cost efficiency and ease in deployment.
Most of industrial internet of things solutions (IIoT) available in market are utilizing LPWAN (Low Power Wide Area Network) wireless technologies e.g., LoRaWAN, SigFox, NB-IOT with backbone network infrastructures based on GSM/Satellite/ public cellular network and cloud-based application/network servers. Such solutions, due to the involvement of 3rd party/public networks pose a concern related to data security and complete network ownership.
This paper explains the PoC trials conducted in Giant Oil and Gas Production Field (X Field) in ADNOC Onshore for the assessment of following attributes of LPWA networks. Deployment private LPWAN based on existing telecom backhaul consisting of FoC and WiMAX network.Centralized monitoring of existing active instrumentation by utilizing Class C LoRa/LoRaWAN end nodes through Lora/LoRaWAN private network.Effectiveness of Lora/LoRaWAN Class C devices for bidirectional communication. Thus, utilization of LPWAN for monitoring as well as remote operations.
At present, in ADNOC Onshore there are multiple remote facilities e.g., oil wells, water injection wells, CP system solar stations etc. which are without centralized remote monitoring; at the same instance, the field has a widespread SCADA network based on WiMAX and FoC. This situation is equally applicable for most of oil and gas brownfield facilities. Private LPWA networks built upon these existing SCADA networks are cost effective solutions to cover the gap for remote monitoring of the facilities where near real-time data monitoring is required.
Below LPWA technologies were tested during these PoC trials. LoRaWAN (LoRaWAN protocol over Physical layer of Lora)Private Lora (Modbus RTU over physical layer of Lora)
Remote Monitoring of Equipment Performance Through a Customized Real-Time Performance Solution
Authors: Kalhoro Muhammad Uddin , Al-Saedi Khaled Abdulla , Al-Awadhi Hesham Khalid , Nadeem Muhammed 
Published: October 2022

Abstract
ADNOC Onshore team embarked on a digitalization project contributing towards ADNOC's digitalization and smart growth strategy to maximize the asset efficiency and integrity. Accordingly, to achieve this objective, a tailor-made real-time asset condition monitoring system (ACMS) mainly for rotating equipment was implemented in ADNOC Onshore BuHasa field. Compared to conventionally available solutions, the ACMS is a customized digital solution integrating the multiple online and offline data on a one platform used to ensure asset integrity and optimum performance. This digital solution provides advanced predictive diagnostics and real-time performance monitoring one a single platform connected to IT network to enable remote desktop access to multiple users. The ACMS is integrated to different tools used for condition and performance monitoring. The use of globally recognized performance standards like ISO, ASME PTC and digital performance curves are used to provide actual real-time condition and performance taking into consideration the varying operating conditions. ACMS is connected with different data sources like process operating parameters through DCS and SCADA, gas turbine control system, machinery health manager for vibration monitoring, oil analysis and provides a complete health assessment of rotating equipment in real-time. ACMS calculates thermodynamic performance in real-time in accordance with ASME PTC standards, eliminating any human intervention or manipulation. Through ACMS, we monitor polytropic efficiency, BEP deviation, head loss and power consumption against expected target within allowable variance. Whereas AMS Machinery Manager along with ACMS predictive feature is used for component condition assessment, which includes vibration analysis and temperature trends. This system is primarily covering all the rotating equipment; however, it has capability to monitor other type of plant equipment as well. Through effective utilization of this condition monitoring system, we transformed the maintenance overhauling of rotating equipment from meter based to condition-based overhaul, contributing to efficiency improvement, and reduced operating cost.
Total Business Transformation to Future Proof the Business, Build Change Agility, and Drive Sustainability and Cost Optimization
Authors: Seth Vivek , Goyal Deepak 
Published: October 2022

Abstract
Energy industry is at the crossroads and a major transition is in progress. This puts tremendous expectations on the supporting Logistics industries.
As the industry balances significant challenges and uncertainties, Future Proofing the Business and driving Sustainability was a top priority for us. To future proof the business, both revenue and cost were important. With this premise we embarked on an ambitious journey to drive total business transformation covering the Operating Model, Operations, Commercial, Organization Structure, People, Performance, Data, Technology. Operating Model defines the Business Moat and drives Customer Solutions and to achieve sustainable successes building change agility was also important. This was also aligned with Corporate Values and 3 Key Pillars – People, Profitability and Sustainability.
While the right Processes, Systems, etc. are critical to any business, importance of People can't be underestimated. Without the right mindset, any transformation will end up being a paper exercise on the surface, while the story underneath the waves will be very different. A successful transformation, though often a management prerogative, needs to involve a larger employee population. And while one can be tempted to change everything, it's vital to understand the high impact areas that will deliver the maximum results. This also helps in ensuring smooth business continuity and not disrupt the day to day to operations, while keeping the cost in control and building success stories that help fuel the Change Mindset and appetite for Transformation.
Our transformation journey, Transform 1.0 had 3 focus areas with high impact, and changing the very DNA of the business. People & Mindset. Building Change AgilityFuture Proofing the Business, impacting top and bottom line. Profitability Cost Optimization Talent Asset OptimizationDriving Sustainability
Development and Utilization of Analytical Methods for Rapid GM Detection in Processed Food Products: A Case Study for Regulatory Requirement
Authors: Singh Monika , Paliwal Aparna , Kaur Kushaldeep , Palit Paramita , Randhawa Gurinderjit 
Published: October 2022

Abstract
Genetically modified (GM) food crops for desired traits have been approved in some of the countries. The approval status of a GM event varies from country to country. In India, Bt cotton is the only approved GM crop. So far, no GM food crop has got regulatory approval in the country, which may be considered as unauthorized GM (UGM) event in the Indian context. The entry of UGM events of food crops needs to be checked in the imported consignments as well as in the marketplace. In the present study, screening elements were identified based on the genetically modified organism (GMO) matrix developed as decision support system for 22 GM food crops approved globally. Three sets of multiplex PCR assays were developed and validated for GM detection in food crops and products: triplex PCR targeting control elements [Cauliflower Mosaic Virus 35S promoter (P-35S), Figwort Mosaic Virus promoter (P-FMV), nopaline synthase terminator (T-nos)], triplex PCR targeting marker genes [aminoglycoside-3’-adenyltransferase (aadA), neomycin phosphotransferase (nptII), phosphinothricin-N-acetyltransferase (pat)] and duplex PCR targeting Bt genes (cry1Ab/Ac and cry2Ab2). Limit of detection (LOD) ranged from 0.5 − 0.05% for different targets, which is in compliance with the labelling threshold of many countries. The developed assays were utilized to check the GM status of apple and maize products along with an additional test for ctp2-cp4epsps for herbicide tolerance in maize. These procedures could be efficiently employed as a part of GMO testing to trace GM contamination, if any, in both the imported as well as domestic food products.
El Niño impacts on the ozone column over Mato Grosso do Sul
Authors: Souza Amaury , Dumka Umesh C. , Pobocikova Ivana , Oliveira-Júnior José Francisco , Abreu Marcel Carvalho , Ihaddadene Razika , Cavazzana Guilherme Henrique , Reis Carlos José , Aristone Flavio , Gul Sajid , Ihaddadene Nabila , Shah Munawar , Fernandes Widinei A 
Published: October 2022

Abstract
The El Niño-South Oscillation (ENSO), a dominant factor in interannual climate variations worldwide, is characterized in the Pacific by anomalous sea surface heating during the El Niño phase and cooling during the La Niña phase. Although ENSO strongly affects atmospheric circulation, its effects on tropospheric ozone are not fully explored. We used satellite measurements of the tropospheric column of ozone to assess the effects of atmospheric circulations driven by ENSO on tropospheric column ozone levels in Mato Grosso do Sul (MS). The objective of this work is to analyze the annual variation and the effects of the El Niño atmospheric variability mode in the Total Ozone Column (TCO) on MS between 2005 and 2020 using data from the AUREA satellite and the \Ozone Monitoring Instrument (OMI) sensor. We found that observed ozone tends to increase in the troposphere after the La Niña peak, corresponding to anomalous downward motions and suppressed convection. The model also reveals that the La Niña-related ozone increase in MS is largely due to intensified transport of ozone-rich air from higher latitudes. This suggests that ENSO should be considered for estimating the ozone concentration in MS, requiring close attention to the properties of ENSO in a changing climate.
Infarction of cerebellum vermis attributed to a right-to-left shunt by pulmonary arteriovenous malformation: a case report.
Authors: Chen Guojuan , Zhang Weitao , Kan Fengjie , Cao Yibin 
Published: October 2022

Abstract
Objectives: We reported this case to remind neurologists that pulmonary right-to-left shunt is one of the causes of paradoxical embolism.
Methods: A 47-year-old woman was diagnosed with infarction of cerebellum vermis by MRI. Atheromatous, immunological, infectious and hypercoagulable abnormality was negated. A doubtful pulmonary arteriovenous malformation (PAVM) was accidentally found in the process of CT angiography of subclavian arteries.
Results: Then the pulmonary angiography confirmed the existence of PAVM in her left pulmonary lobe. Meanwhile, the bubble contrast study with transcranial Doppler found bubble signals passing her middle cerebral artery.
Discussion: Other than intracardiac, but pulmonary right-to-left shunt can induce embolic stroke.
Frequency of risk factors of coronary heart diseases in patients with acute coronary syndrome and its comparison in male and female
Authors: Mazhar Naqash , Khan Usman Hassan , Mand Bakhat , Rehman Abdur , Mubeen Maryam , Malik Mohammad Hamza Bin Abdul , Hussain Syed Anas 
Published: October 2022

Abstract
Introduction: Cardiovascular disease (CVD) is a group of diseases that include both the heart and blood vessels, thereby including coronary heart disease (CHD) and coronary artery disease (CAD), and acute coronary syndrome (ACS) among several other conditions.
Objectives: The main objective of the study is to find the frequency of risk factors of coronary heart diseases in patients with acute coronary syndrome and its comparison in male and female.
Material and methods: This cross-sectional study was conducted in Services Institute of medical sciences during June 2021 to June 2022. The data was collected with the permission of ethical committee of hospital.
Results: The data was collected from 100 male and female patients. The mean age was 45.67±2.89 years for males and 49.89±5.67 years for females. The major risk factors for diseases is hypertension, hyperlipidemia, smoking and diabetes. According to data all the patients must contain one of these major risk factors and many of the patients had more then one risk factors.
Conclusion: It is concluded that all the patients must have one of the major risk factors as a cause of diseases. Women with acute coronary syndrome, as compared to men, have more prevalence of diabetes and hypertension, and less prevalence of smoking.
Tsc2 mutation rather than Tsc1 mutation dominantly causes a social deficit in a mouse model of tuberous sclerosis complex
Authors: Kashii Hirofumi , Kasai Shinya , Sato Atsushi , Hagino Yoko , Nishito Yasumasa , Kobayashi Toshiyuki , Hino Okio , Mizuguchi Masashi , Ikeda Kazutaka 
Published: October 2022

Abstract
Background: Tuberous sclerosis complex (TSC) is an autosomal dominant disorder that is associated with neurological symptoms, including autism spectrum disorder. Tuberous sclerosis complex is caused by pathogenic germline mutations of either the TSC1 or TSC2 gene, but somatic mutations were identified in both genes, and the combined effects of TSC1 and TSC2 mutations have been unknown.
Methods: The present study investigated social behaviors by the social interaction test and three-chambered sociability tests, effects of rapamycin treatment, and gene expression profiles with a gene expression microarray in Tsc1 and Tsc2 double heterozygous mutant (TscD+/-) mice.
Results: TscD+/- mice exhibited impairments in social behaviors, and the severity of impairments was similar to Tsc2+/- mice rather than Tsc1+/- mice. Impairments in social behaviors were rescued by rapamycin treatment in all mutant mice. Gene expression profiles in the brain were greatly altered in TscD+/- mice more than in Tsc1+/- and Tsc2+/- mice. The gene expression changes compared with WT mice were similar between TscD+/- and Tsc2+/- mice, and the overlapping genes whose expression was altered in mutant mice compared with wild type (WT) mice were enriched in the neoplasm- and inflammation-related canonical pathways. The “signal transducer and activator of transcription 3, interferon regulatory factor 1, interferon regulatory factor 4, interleukin-2R a chain, and interferon-γ” signaling pathway, which is initiated from signal transducer and activator of transcription 4 and PDZ and LIM domain protein 2, was associated with impairments in social behaviors in all mutant mice. Limitations: It is unclear whether the signaling pathway also plays a critical role in autism spectrum disorders not caused by Tsc1 and Tsc2 mutations.
Conclusions: These findings suggest that TSC1 and TSC2 double mutations cause autistic behaviors similarly to TSC2 mutations, although significant changes in gene expression were attributable to the double mutations. These findings contribute to the knowledge of genotype-phenotype correlations in TSC and suggest that mutations in both the TSC1 and TSC2 genes act in concert to cause neurological symptoms, including autism spectrum disorder.