bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Simulation Augmented Machine Learning Approach for Cyclic Steam Stimulation Development Targeting Lower Carbon/Higher Return
Authors: Izgec Omer , Li Yunan , Javaheri Mohammad , Tawfik Miral , Osako Ichiro 
Published: April 2022

Abstract
Finding more energy-efficient and lower carbon emitting ways to develop heavy oil reservoirs is key for economically unlocking more than 50% of the remaining oil reserves worldwide. Continuous steam injection (CSI) is one of the most widely used yet energy-intensive heavy oil enhanced oil recovery techniques. One of the proposed methods to reduce the carbon footprint of heavy oil developments is using cyclic steam development (CSD) rather than continuous steam injection where applicable.
The study has two objectives. First, we developed a CSD conceptual model which describes the physical principles, represents the full life cycle of a CSD and focuses on key elements. Second, a thermal simulation model (based on the conceptual model) for CSD was calibrated to field data. Using thismodel, we conducted a parametric study to select the most impactful input parameters for machine learning model (MLM) training. We introduced a point-based regression method to train the MLM at every single time step for time series prediction of selected properties such as oil production, reservoir pressure, and so on. To manage large amounts of trained MLMs, we also conducted dimension reduction on prediction matrix using Principal Component Analysis (PCA). Once the MLM was trained with the field and simulated data, a web-based fast predictive tool was developed to provide consistent and robust predictions within seconds. This makes it possible to quickly evaluate various scenarios, assess uncertainties and optimize injection job parameters. Furthermore, the tool generates type-curves to identify the inflection point where increasing injected steam volume (per cycle) will not lead to improved recovery due to longer injection period or down-time for CSD wells. The presented workflow can also be widely applied to different recovery techniques, and asset classes.
Solvent Versus Surfactant Co-Injection with Steam to Improve Efficiency of Steam Assisted Bitumen and Heavy Oil Recovery Processes
Authors: Ozum Baki 
Published: April 2022

Abstract
In Alberta, Canada bitumen is produced by SAGD and CSS methods at about 1.5×106 barrels per day capacity. Efficiency of these operations is measured by steam-to-bitumen mass ration, which is reported in the range of 2.6 to 5. Oil industry has invested on solvent such as light hydrocarbons co-injection with steam to reduce SBR, speculating that solvent would reduce bitumen viscosity and increase Darcy mobility of bitumen in the reservoir, which has resulted limited commercial success. In majority of these studies, effect of solvent co-injected with steam on bitumen-water interfacial tension, therefore on Darcy permeability was ignored. Our laboratory has been studying to increase Darcy permeability of bitumen by reducing bitumen-water interfacial tension. For this purpose two methods were investigated: (i) surfactant species were produced by in-situ sulfonation-sulfoxidation of bitumen asphaltenes by co-injecting a trace amount of gaseous sulfur dioxide (SO2) with steam; and (ii) co-injection of biodiesel (BD) as a surfactant additive, with steam at under 2 g-BD/kg-bitumen dosages, which corresponds to under 0.32 kg-BD/bbl-bitumen, or under 0.67 kg-BD/ton-steam if SBR is 3:1. Bitumen recovery tests were also made to compare performances of solvent versus surfactant co-injection with steam methods by operating the test apparatus in bath mode, which most likely stimulate the steam zone of the steam zone of the SAGD and CSS reservoirs. These tests showed that solvent co-injection potentially reduces bitumen recovery efficiency compared to steam-only methods. We wonder that the solvent co-injected with steam increases bitumen-water interfacial tension, increases slip velocity at bitumen-water interface and reduces Darcy permeability of bitumen. Experimental observations supported with fluid dynamics analysis of two immiscible fluids with slip boundary condition at the interface of two fluids were encouraging to expand our research in BD-water emulsions flooding for cold heavy oil recovery, CHOPS and Post CHOPS, and BD-solvent-water emulsions flooding for extra viscous heavy oil, and bitumen production from high permeability reservoirs, which would eliminate demand for steam. Experimental data on bitumen viscosity, bitumen-water interfacial tension and potential effects of solvent and surfactant co-injection with steam on commercial steam assisted bitumen and heavy oil recovery operations will be discussed.
Well Production Optimization through Annulus Gas Management using Gas Handling System in Auca Field, Ecuador.
Authors: Briones Luna César Alejandro , Villa Alzate Carlos , Pastrana Vargas Wilmar Andrés , Azancot Annalyn , González Martínez Darío De Jesús , Segovia Galárraga Rubén Darío , Campana Álvarez Danny Rafael , Salcedo Vallejo Jaime Patricio , Cadena Espín Raúl Fernando , Canaveral Caizaluisa Edwin Gonzalo , Gambaretto Agustín , Paredes Martínez Walter Vinicio , Molina Edison , Vera César Andrés , Arévalo Diego 
Published: April 2022

Abstract
Improving production is a main goal in the industry, mainly in countries where now it is possible to apply accessible technology. Digital Oil Field projects have been successfully implemented worldwide, showing their value in terms of losses reduction and oil production optimization. Annular Gas handling system (AGHS) is part of those projects, aiming to improve well production systems.
Gas accumulation in the annulus is a well-known operational condition which may lead, depending on the volume and associated gas, to ESP production system issues (gas lock). With this in mind, a technical team was formed to create an autonomous annular gas handling system with the main objective to optimize production by avoiding gas related issues, but also to reduce human intervention in the process. This is translated into less kilometers driven and therefore less people exposure.
The main task of the gas handling system consists in regulate the gas outlet through an automatic valve according to a remote order. This order can be given as a percentage of opening denominated "manual control", or as a value of casing head pressure (CHP), denominated "automatic mode" where the valve keeps adjusting automatically in order to maintain the set point pressure (SP). In this way, the manual operation of the annulus valve from an operator on-field is no more needed.
This case solution presents the pilot test results and a feasibility analysis for the expansion of the AGHS Skids. The pilot included two wells in the Auca brownfield: ACAK-182 and ACAK-124, in which an incremental production of 30% and 12% respectively was achieved after pilot implementation. Additionally, manual annular valve interventions were reduced 98% after the automated gas-handling system was tested.
This project demonstrated that intelligent, customized and specific solutions enabled incremental production at a very low cost, reducing people exposure by avoiding trips from pad to pad for annular valve manipulation. Additionally, AGHS Skid improved the efficiency of the ESP in 11% and reduced energy consumption in 11% KVA resulting in higher production with lower power consumption. Also, people were able to spend time working on production optimization rather than operational tasks.
Evaluation of Geological Carbon Storage Opportunities in California and a Deep Look in the Vicinity of Kern County
Authors: Kim Tae Wook , Yaw Sean , Kovscek Anthony R. 
Published: April 2022

Abstract
Geological carbon storage has a critical role to play for the US to accomplish carbon neutrality by 2050. In this work, previous studies of geological carbon storage are reviewed, redefined, and evaluated to focus on providing proper candidate storage sites in the Southern San Joaquin Basin. This study clarifies not only the CO2 capture and storage opportunity but also the potential economic benefit. A three-stage selection method is applied to a catalog of saline formations and hydrocarbon fields to qualify sites for additional in-depth study. The three stages consist of screening using geological criteria, defining exclusion zones, and qualifying sites (Callas and Benson, 2020; Kim et al., 2022). Exclusion zones define potentially unacceptable storage sites based on seismic risk, surface environment such as sensitive habitats, social, and economic aspects. Nine saline formations and 133 hydrocarbon fields were examined. The exclusion zones including faulted, seismically active, large population density, restricted lands, and sensitive habitats, were subtracted from hydrocarbon fields and saline formations. This process resulted in qualified sites. Finally, qualified sites were prioritized using a scoring system. The estimated CO2 storage resource in the qualified saline formations ranged from 16.6 to 52 GtCO2 whereas the estimated CO2 storage resource in hydrocarbon fields ranged from 0.45 - 1.15 GtCO2. Among hydrocarbon fields, 15 CO2-EOR candidate fields with storage resources of 0.36 – 0.88 GtCO2 are located in Kern County. Considering the scoring system, a total of 41 storage sites including 7 hydrocarbon fields were defined as target CO2 storage sites. The opportunities for 41 CO2 storage sites in and around Kern County were linked to selected large CO2 emitters in Southern California including Imperial, Kern, Los Angeles, Orange, San Bernardino, Santa Barbara, San Luis Obispo, and Ventura Counties. Finally, the prospective storage sites and emitters were analyzed technoeconomically using SimCCS to find optimal conditions to deploy CCS projects. Regional GHG emissions from oil and gas facilities such as EOR steam generators and CHPs can be captured and stored economically in geological formations as a result of LCFS and 45Q credits. The sensitivity of 45Q credit value and covered period are critical factors to incentivize CCS deployment. The deployment scenarios evaluated using SimCCS teach that the Southern San Joaquin basin is an excellent potential regional carbon storage hub.
Hardware Realization of Polar Decoder Using Reinforcement Learning Algorithm
Authors: A Albert Raj , A Calin , T Latha , D Subbulakshmi , J Vijila 
Published: April 2022

Abstract
Error control coding is nearly ubiquitous in our information-based society. In recent days, one amongst the error correcting techniques referred to as the polar codes which attracts the researchers as it represents one of the foremost breakthroughs in 5G standard. It is built by polarization effect of polarization matrix and it is one of the capacity achieving algorithms. It is proved that Successive List decoding (SCL) algorithm improves the efficiency of Polar codes. However, when the codelength increases the latency also increases. It is expected that Reinforcement learning algorithm (RLA) will be able to reduce the latency of the decoder. Therefore, in this article, Markov decision-process algorithm is proposed. RLA uses this Markov decision process when the decoding probabilities are unknown. The same is implemented in the hardware architecture. The implementation result shows that, this method reduces decoding latency to 33% without sacrificing a frame error rate. Experiment result shows that the hardware complexity is also reduced when compared to SCL decoding algorithm. This project is developed using System Generator (Xilinx), with a target device of FPGA-Virtex6.
Identification of Attention Deficit Hyperactivity Disorder with Deep Learning Model
Authors: KASIM Ömer 
Published: April 2022

Abstract
This article explores the detection of Attention Deficit Hyperactivity Disorder (ADHD), a neurobehavioral disorder, from electroencephalography (EEG) signals. Due to the unstable behavior of EEG signals caused by complex neuronal activity in the brain, frequency analysis methods are required to extract the hidden patterns. In this study, the feature extraction was performed with the Multitaper and Multivariate Variational Mode Decomposition methods. Then, these features were analyzed with the neighborhood component analysis and the features that contribute effectively to the classification were selected. The deep learning model including the convolution, pooling, and bidirectional long short term cell and fully connected layer was trained with the selected features. The trained model could effectively classify the subjects with ADHD with a deep learning model, support vector machines and linear discriminant analysis. The proposed approach was validated with an ADHD open access dataset (doi:10.21227/rzfh-zn36). Experimental results showed that the proposed approach can innovatively classify ADHD subjects from Control group effectively. The proposed method was able to classify 1210 test samples in 0.1 seconds with an accuracy of 95.54%. The proposed method is promising for distinguishing subjects with attention deficit hyperactivity disorder effectively.
Efficiency of the H2O2 consumption by the Mineralization of Hydrochlorothiazide via Photo-Fenton UVA: A Time Dependent Analysis
Authors: Mota-Lima Andressa , Cunha-Filho Fernando José Vieria , Chiavone-Filho Osvaldo , Nascimento Claudio Augusto Oller do 
Published: April 2022

Abstract
In the photo-chemical conversion of hydrogen peroxide (H2O2) into radical ⦁OH, the impact of radiation dose (the quantum yield) on the oxidation kinetics has been properly reported, whereas the H2O2 dose is mostly treated as an attempt-and-error variable. This present work formally analyses the oxidant dose in terms of the efficiency of ⦁OH consumption by the micropollutant mineralization, which ultimately enables a generalist strategy for more cost-effective dosimetry of the oxidant. The experimental time-dependent measure of two reagents (pollutant and oxidant) and one product (CO2) enables assessment to the micro-kinetics from the perspective of the chemical dynamic. The analysis was demonstrated for photo-Fenton (FP) oxidation of hydrochlorothiazide (HCT) using a tubular photo-reactor and UVA radiation. The average value of ~ 38% was on top of the best efficiencies in association with some of the fastest rates of mineralization. Such efficiencies are demonstrated to depend on the stochiometric concentration of the oxidant. Here, the variable stoichiometric H2O2 excess for mineralization is proposed as a universal metric to quantify the (under-) over-dose of H2O2. Overall, H2O2 excess between 2 and 5 leads to H2O2 consumption efficiencies above 30% together with a fast rate of CO2 formation (mineralization), whereas any value below 1 invariably leads to a sluggish oxidation rate, leading even to the full depletion of the oxidant. Aside from proposing a selection criterion for the most cost-effective H2O2-dose and providing some examples, this work carefully analyzes the commitment of the H2O2 excess with respect to the energy costs (EEO).
Low FTO expression serves as a potential biomarker for diagnosis and prognosis of patients with liver cancer
Authors: Shi Hongyun , Fu Chang , Quan Panpan , Zhang Liyue , Liu Yunpeng , Jiao Yan 
Published: April 2022

Abstract
Background: As an important part of m6A modification, FTO plays a carcinogenic effect in cancer. In this study, we aim to explore whether FTO could be a new target for diagnosis and prognostic of patients with liver cancer.Material and Methods: We obtained and analysis data from TCGA database by using R software (version 3.5.1). The box plot was used to analyze the pattern of FTO expression. Receiver operating characteristic (ROC) curve was performed to evaluate diagnostic value of FTO expression in liver cancer. Fisher’s exact and chi-square tests were used to analyze relationship between FTO expression and clinical pathological characteristics. Kaplan-Meier curve and Cox regression analysis were used to explore the prognostic value of FTO in liver cancer.Results: We observed that FTO was down-regulated in liver cancer and was associated with patients’ age. ROC curve showed that FTO expression had good clinical diagnostic value. Our results also revealed that FTO expression was associated with age, histologic grade, M classification, vital status, and overall survival (OS) of patients. Low FTO expression decreased OS, and was an independent predictor of poor prognosis in patients with liver cancer.Conclusion: FTO could serves as a potential biomarker for the diagnosis and prognosis of liver cancer
Deep Learning for MYC binding site recognition
Authors: Fioresi R. , Demurtas P. , Perini G. 
Published: April 2022

Abstract
Motivation: The definition of the genome distribution of the Myc transcription factor is extremely important since it may help predict its transcriptional activity particularly in the context of cancer. Myc is among the most powerful oncogenes involved in the occurrence and development of more than 80% of different types of pediatric and adult cancers. Myc regulates thousands of genes which can be in part different, depending on the type of tissues and tumours. Myc distribution along the genome has been determined experimentally through chromatin immunoprecipitation This approach, although powerful, is very time consuming and cannot be routinely applied to tumours of individual patients. Thus, it becomes of paramount importance to develop in-silico tools that can effectively and rapidly predict its distribution on a given cell genome. New advanced computational tools (DeeperBind) can then be successfully employed to determine the function of Myc in a specific tumour, and may help to devise new directions and approaches to experiments first and personalized and more effective therapeutic treatments for a single patient later on. Results: The use of DeeperBind with DeepRAM on Colab platform can effectively predict the binding sites for the MYC factor with an accuracy above 0.96 AUC, when trained with multiple cell lines. The analysis of the filters in DeeperBind trained models shows, besides the consensus sequence CACGTG classically associated to the MYC factor, also the other consensus sequences G/C box or TGGGA, respectively bound by the SP1 and MIZ-1 transcription factors, which are known to mediate the MYC repressive response. Overall, our findings suggest a stronger sinergy between the machine learning tools as DeeperBind and biological experiments, which may reduce the time consuming experiments by providing a direction to guide them.
External validation of VO2max prediction models based on recreational and elite endurance athletes
Authors: Wiecha Szczepan , Kasiak Przemysław Seweryn , Cieśliński Igor , Takken Tim , Pałka Tomasz , Knechtle Beat , Nikolaidis Pantelis T , Małek Łukasz A , Postuła Marek , Mamcarz Artur , Śliż Daniel 
Published: April 2022

Abstract
In recent years, numerous prognostic models have been developed to predict VO2max. Nevertheless, their accuracy in endurance athletes (EA) stays mostly unvalidated. This study aimed to compare predicted VO2max (pVO2max) with directly measured VO2max.5,260 healthy adult EA underwent a maximal exertion cardiopulmonary exercise test (CPET) (84.76% male; age 34.6±9.5 yrs.; VO2max 52.97±7.39 mL·min-1·kg-1, BMI 23.59±2.73 kg·m-2). 13 models have been selected to establish pVO2max. Participants were classified into four endurance subgroups (high-, recreational-, low- trained, and “transition”) and four age subgroups (18-30, 31-45, 46-60, and ≥61 yrs.). Validation was performed according to TRIPOD guidelines. pVO2max was low-to-moderately associated with direct CPET measurements (p>0.05). Models with the highest accuracy were for males on a cycle ergometer (CE) (Kokkinos R2=0.64), females on CE (Kokkinos R2=0.65), males on a treadmill (TE) (Wasserman R2 =0.26), females on TE (Wasserman R2=0.30). However, selected models underestimated pVO2max for younger and higher trained EA and overestimated for older and lower trained EA. All equations demonstrated merely moderate accuracy and should only be used as a supplemental method for physicians to estimate CRF in EA. It is necessary to derive new models on EA populations to include routinely in clinical practice and sports diagnostic.
Polarized th2 cells attenuates high-fat-diet induced obesity through the suppression of lipogenesis
Authors: Dong Lijun , Gao Jingtao , Yu Lu , Liu Shibo , Zhao Yuxin , Zhang Wen , Liang Yinming , Wang Hui 
Published: April 2022

Abstract
Immune cells such as macrophages, B cells, neutrophils and T cell subsets are involved in obesity. However, the tissue-specific function of Th2 cells in adipose tissue remains unknown. We show here that adoptive transfer of polarized Th2 cells to CD3ε knockout (CD3ε−/−) mice attenuates obesity after high-fat-diet (HFD), accompanied by relieved hepatic steatosis, glucose intolerance, and insulin resistance. Mechanistically, Th2 cell treatment promoted oxidative phosphorylation of adipocytes, contributing to the decrease of lipid droplet accumulation. Our data indicate that Th2 cell treatment is a novel approach to the treatment of diet-induced obesity and other diseases involving lipid droplet accumulation disorders.
WITHDRAWN: Inflation Nexus Economic Growth in Ethiopia
Authors: Mognhod Haymanot Bassie 
Published: April 2022

Abstract
Price stability and achieving sustainable economic growth are the major goals of macroeconomic policy and the key indicators of macroeconomic stability. Pursuing of price stability is primary to long-run growth and development; it should be the concern of every economy. This study investigated the nexus of inflation and economic growth in Ethiopia, using the vector autoregressive (VAR) model by employing the data series for the period ranging from 1980 to 2021. The ADF Unit root test confirmed that the variables in the model are integrated of order I (0). The descriptive statistics concludes that the relationship between inflation and economic growth are inconclusive. From VAR regression result, the lagged value of inflation has positive and significant effect on inflation at 1% level of significance. But the lagged value of economic growth has insignificant effect on inflation in Ethiopia. On the other hand, the lagged value of economic growth has positive and significant effect on economic growth and the lagged value of inflation has positive and significant effect on economic growth at 1% level of significance. The estimated model passes diagnostic tests and the Eigen value stability condition displays all the Eigen values lie inside the unit circle. Hence, VAR satisfies stability condition. Long run sustainable development emphasis should be given to accelerate economic growth and compute the threshold inflation-growth level, so that growth rate takes care of the stability in inflation.