bluuebunny/crossref_metadata_2025_split huggingface dataset:
Data-Driven Machine Learning Approach to Predict the Trapping Indices in Coupled Chemo-Hydro-Mechanical Model of CO2 Sequestration into Deep Saline Aquifer
Authors: Tariq Zeeshan , Yan Bicheng , Sun Shuyu 
Published: May 2023

Abstract
Storing carbon dioxide (CO2) in deep geological formations, such as saline aquifers and depleted oil and gas reservoirs, through Geological Carbon Sequestration (GCS) offers tremendous potential for large-scale CO2 storage. However, ensuring the successful implementation of GCS requires a thorough evaluation of the risks associated with confinement of plumes and storage capacity at each storage location. To gain a better understanding of how CO2 is trapped in saline aquifers, it is important to create robust and speedy tools for assessing CO2 trapping efficiency. Therefore, this study focuses on using machine learning techniques to predict the efficiency of CO2 trapping in deep saline formations as part of Geological Carbon Sequestration (GCS). The methodology involves simulating the CO2 trapping mechanisms using a physics-based numerical reservoir simulator and creating training, testing, and validation datasets based on uncertainty variables. The study used a numerical reservoir simulator to simulate CO2 trapping mechanisms over 170 years, with uncertainty variables like petrophysical properties, reservoir physical parameters, and operational decision parameters being utilized to create a large dataset for training, testing, and validation. The study identified key control variables through feature importance index calculation and utilized the Latin-Hypercube approach to account for a wide range of parameters. 722 reservoir simulations were performed and the results of residual trapping, mineral trapping, solubility trapping, and cumulative CO2 injection were analyzed. The outliers and extreme data points were removed using statistical and exploratory data analysis techniques. Deep neural network was applied to predict the CO2 trapping efficiency. The results showed that the deep neural network model can predict the trapping indices with a coefficient of determination above 0.95 and average absolute percentage error below 5%. These findings suggest that machine learning models can serve as a more efficient alternative to traditional numerical simulation for estimating the performance of CO2 trapping in GCS projects.
Liquid-Liquid Equilibrium Studies of Carbon Dioxide/Bitumen System and Utilizing Ethyl Acetate as a Co-Solvent
Authors: Khan Mohammad Shah Faisal , Hassanzadeh Hassan 
Published: May 2023

Abstract
In situ bitumen extraction from oil sands using thermal recovery processes has faced challenges due to reliance on steam. Additionally, the produced bitumen is highly viscous and needs to be diluted with lighter hydrocarbon products, such as field condensates, for pipeline transportation. Therefore, exploring less energy-intensive options to produce and transport bitumen economically with less environmental impact is essential. This work aimed to study the liquid-liquid equilibrium (LLE) of CO2 and bitumen at ambient temperature. First, the impact of CO2 feed mass fraction and pressure on equilibrium mixture properties are investigated. In the next step, the effect of ethyl acetate (EA) as an additive on the equilibrium properties of the mixture is studied. The equilibrium properties of the mixtures, including CO2 solubility in the heavy liquid phase, the viscosity of the heavy liquid phase, and the densities of light and heavy liquid phases, are reported. The results suggest that the viscosity of bitumen is considerably reduced by mixing it with liquid CO2 at ambient temperature. It was also shown that the bitumen viscosity could be further reduced by the addition of ethyl acetate as a co-solvent.
A Robust Four-Phase Equilibrium Calculation Algorithm for Hydrocarbon-Water Mixtures at Pressure and Enthalpy Specifications
Authors: Li Sirui , Li Huazhou 
Published: May 2023

Abstract
A robust multiphase equilibrium calculation algorithm with pressure and enthalpy (PH) specifications, i.e., an isenthalpic algorithm, plays an important role in the compositional simulations of steam-based enhanced oil recovery (EOR) applications (such as steam and solvent co-injection process for heavy oil recovery). Up to now, there are few works documented in the literature focusing on four-phase isenthalpic algorithms. In this paper, we propose a four-phase isenthalpic algorithm with a nested approach. It contains an inner loop and an outer loop. In the inner loop, a well-designed isobaric/isothermal (PT) multiphase (up to four phases) equilibrium algorithm is employed to solve the phase fractions and compositions, while the Brent's method (1971) is applied in the outer loop to update the temperature by satisfying the energy conservation equation. We test the performance of the proposed algorithm using four case studies under different pressure-enthalpy conditions. Calculation results demonstrate that the proposed PH algorithm is always able to converge to the correct phase equilibria with only tens of PT algorithm calls.
Distributed Agent Optimization for Large-Scale Network Models
Authors: Nagao Masahiro , Sankaran Sathish , Guo Zhenyu 
Published: May 2023

Abstract
Optimization of production networks is key for managing efficient hydrocarbon production as part of closed-loop asset management. Large-scale surface network optimization is a challenging task that involves high nonlinearity with numerous constraints. In existing tools, the computational cost of solving the surface network optimization can exponentially increase with the size and complexities of the network using traditional approaches involving nonlinear programming methods. In this study, we accelerate the large-scale surface network optimization by using a distributed agent optimization algorithm called alternating direction method of multipliers (ADMM).
We develop and apply the ADMM algorithm for large-scale network optimization with over 1000 wells and interconnecting pipelines. In the ADMM framework, a large-scale network system is broken down into many small sub-network systems. Then, a smaller optimization problem is formulated for each sub-network. These sub-network optimization problems are solved in parallel using multiple computer cores so that the entire system optimization will be accelerated. A large-scale surface network involves many inequality and equality constraints, which are effectively handled by using augmented Lagrangian method to enhance the robustness of convergence quality. Additionally, proxy or hybrid models can also be used for pipe flow and pressure calculation for every network segment to further speed up the optimization.
The proposed ADMM optimization method is validated by several synthetic cases. We first apply the proposed method to surface network simulation problems of various sizes and complexities (configurations, fluid types, pressure regimes, etc.), where the pressure for all nodes and fluxes in all links will be calculated with a specified separator pressure and reservoir pressures. High accuracy was obtained from the ADMM framework compared with a commercial simulator. Next, the ADMM is applied to network optimization problems, where we optimize the pressure drop across a surface choke for every well to maximize oil production. In a large-scale network case with over 1000 wells, we achieve 2X – 3X speedups in computation time with reasonable accuracy from the ADMM framework compared with benchmarks. Finally, we apply the proposed method to a field case, and validate that the ADMM framework properly works for the actual field applications.
A novel framework for surface network optimization was developed using the distributed agent optimization algorithm. The proposed framework provides superior computational efficiency for large- scale network optimization problems compared with existing benchmark methods. It enables more efficient and frequent decision-making of large-scale petroleum field management to maximize the hydrocarbon production subject to numerous system constraints.
Effects of Axial Tension and Torque on Fatigue Performance of Threaded OCTG Connection
Authors: Lin Bisen , Bennett Frederick , Coe David , Pattillo Phillip 
Published: May 2023

Abstract
Casing strings, consisting of hundreds of ∼40 feet long pipe joints connected by threaded connections, usually experience axial tension and/or torsion when rotationally running through the build sections during casing installation of horizontal wells. It is important to understand the influence of axial tension and torsion on the connection's fatigue performance. In this paper, fatigue performance of threaded OCTG connections subjected to cyclic dogleg bending deformation is evaluated using fatigue crack growth methodology and full-scale connection fatigue testing.
Material constants in the fatigue crack growth model are first calibrated using a single set of full-scale connection fatigue test results on one connection. Several connection products (different combinations of connection design and size) are then analyzed using the calibrated fatigue model and compared to the full-scale fatigue-to-failure test results. Excellent agreement, in terms of both fatigue life and fatigue failure location, between the analysis and actual test results are observed.
Effects of connection torque and axial tension on fatigue performance are also investigated using the calibrated fatigue crack growth model for a broad range of connection products. For each connection analyzed, four different torques with values in-between the connection's minimum required makeup torque and yield torque are considered. Four different axial tension levels, equivalent to 0, 10, 20, and 30 ksi axial tensile stress of the corresponding pipe, are also analyzed. Overall, different connection torques or axial tensions do not impact the connection's fatigue performance significantly except for the small size (e.g. 4.500"pipe OD) T&C connections.
Prediction of Permeability from Logging Data Using Artificial Intelligence Neural Networks
Authors: Al-Otaibi Mohammed Abdullah , Abdullah Eassa , Hanafy Sherif Mahmoud 
Published: May 2023

Abstract
While many factors influence the success of a given well, the permeability of the surrounding formation is one of the most important properties to understand the nature of any reservoir and to be utilized for effective oil and gas drilling. Gathering data from well logs for different wells can be highly expensive and time-consuming. The goal of this work is to find the best artificial intelligent model which can predict the permeability values with minimum error while saving time and money. Therefore, accurately estimating is highly beneficial to use such a model for further field and engineering applications. In this project, a trial was accomplished through a Machine Learning (ML) approach using several modules of Artificial Intelligent including ANFIS and ANN to examine and build a permeability prediction model based on nine (9) well-logging parameters taken from well-logging data measured at a borehole in carbonate rock. The permeability was predicted from well-log data using Artificial Intelligent (AI) technique. Field data were recorded at one borehole, where all logs are correlated together. After obtaining results, the prediction model can be considered successful, it is highly recommended to utilize ANFIS- Genfis2 as it gives outstanding results as the correlation coefficient training was 1.0 and testing was 0.9347 compared with ANFIS-Genfis1 which was not satisfying with training correlation coefficient of 1.0 and testing 0.4073, including a significant reduction in the percentage error of 14.3% compared of 301%, and utilize ANN with a double layer not single, as the result of single layer showed a correlation coefficient of 0.9337 in training and 0.9924 in testing. In addition, single layer method showed higher error compared with double layer. Conclusively, it is recommended to apply the model with other data obtained from the same reservoir, to minimize the number of unneeded data, enhance the measurement performance by avoiding human errors, and develop other relationships between a set of parameters that can result in a better and most effective prediction model. In novelty, utilizing and studying the output of this trial application of the machine learning approach will summarize the best models and techniques for predicting many important reservoir properties such as Permeability. The number of well logging parameters is high and has been statically analyzed to increase the resolution of the input data. Building this prediction model will increase the recovered amount from the subsurface and will lead to significant cost savings in drilling and exploration operational
The Global Prevalence of Anxiety Symptoms Among Healthcare Workers Before and During COVID-19: A Systematic Review and Meta-analysis
Authors: Qing Chang , Yan Zixuan , Xia Yang , Su Han , Yan Shuai , Gao Yitao , Zhang Xi , Zhang Yixiao , Liu Caigang , Zhao Yuhong 
Published: May 2023

Abstract

Importance:The COVID-19 pandemic’s effect on the prevalence of anxiety symptoms among healthcare workers remains unclear. Hence, an investigation into their prevalence and the changes before and during the pandemic is needed.Objective:To provide a comprehensive summary of the prevalence of anxiety symptoms among healthcare workers internationally and to compare this before and during the COVID-19 pandemic.Data Sources:A literature search was conducted using Medline, PubMed databases, and Embase for observational studies from their establishment in January 1963, January 1963, and January 1989, respectively, to August 2022.Study Selection:A standardized search strategy was applied to the databases with keywords assembled into three categories, including “healthcare workers”, “anxiety symptoms”, and “miscellaneous”. The inclusion criteria were: (I) evaluation of the prevalence of anxiety symptoms among healthcare workers employed in hospitals; (II) measured through validation tools or questionnaires; (III) published in English. The exclusion criteria were: (I) no overall prevalence of anxiety provided; (II) case series, case reports, intervention research, and commentaries.Data Extraction and Synthesis:The protocol was developed based on the PRISMA guidelines. The pooled prevalence and 95% confidence interval (CI) were estimated using a random effects model.Main Outcome(s) and Measure(s):The articles were analyzed according to subgroups divided based on the time of the studies conducted, participant occupations, locations, screening instruments, and publication years.Results:A total of 358 cross-sectional articles with 373 estimates from 65 countries were included. The overall prevalence of anxiety symptoms among healthcare workers was 41.4% (95% CI, 39.3-43.4). The prevalence of anxiety symptoms before and during the COVID-19 pandemic were 40.7% (95% CI: 34.1-47.3) and 41.2% (95% CI: 39.1-43.3), respectively. Compared with studies before the pandemic, a higher prevalence of anxiety symptoms was identified among nurses, residents, and physicians during the pandemic.Conclusions and Relevance:A considerable proportion of healthcare workers have experienced anxiety symptoms, with their global prevalence increasing during the COVID-19 pandemic. Further research is needed to determine effective prevention and treatment strategies, particularly during the COVID-19 pandemic.Systematic review registration:CRD42022370819.
MSSRCA Powered Sensitive Point-of-Care Detection of HPV mRNA for Cervical Cancer Screening
Authors: Long Yi , Tao Shurui , Shi Dongni , Jiang Xingyu , Yu Tian , Long Yingxi , Song Libing , Liu Guozhen 
Published: May 2023

Abstract
Developing sensitive point-of-care testing (POCT) of oncogenic nucleic acids from HPV infection is essential in preventing cervical cancer, especially in resource-limited settings. Rolling circle amplification (RCA) is attractive in achieving POCT; however, still exists a bottleneck requiring multiple amplification steps with multiple detection probes due to the limited sensitivity. Here we developed a minimum secondary structured RCA technique (MSS-RCA) by designing a unique circular template, demonstrating significantly enhanced detection sensitivity with only one amplification step and one primer. We demonstrated that RCA amplification efficiency could be kinetically manipulated by controlling the secondary structure of the circular template. Introducing the invertase probe to MSS-RCA, Human Papillomavirus 16 (HPV16) E6/E7 nucleic acid target was detected with a personal glucose meter (PGM) with a sensitivity of 5 fM (50 zmol in 10 µL). This integrated MSS-RCA-PGM detection system was successfully applied to detect HPV16 E6/E7 mRNA extracted from 54 cervical swab samples reaching a positive predictive value of 100.00% and negative predictive values of 96.00% (77.77% to 99.40%, 95% CI). MSS-RCA-PGM provides a sensitive POCT platform for the detection of biomarkers for screening of cervical cancer or other diseases.
Enhancing Tendon Regeneration: Investigating the Impact of Topography on the Secretome of Adipose-Derived Stem Cells
Authors: Long Qiuzi , Zhang Wei , Liu Chuanquan , Zheng Haotian , Wang Mingyue , Cao Zhicheng , Sun Yuzhi , Mo Qingyun , Backman Ludvig J. , Huang Jinlong , Chen Jialin 
Published: May 2023

Abstract
Background: Tendons are vital for maintaining integrity and movement, but current treatment options are insufficient for restoring them after injuries. Previous studies have shown that the secretome from mesenchymal stem cells (MSCs) promoted tendon regeneration. However, limited studies have explored the impact of the cellular microenvironment on the secretome of MSCs in tendon repair. This study aims to investigate how the topographic orientation impacts the secretome of human adipose-derived stem cells (ADSCs) and its effect on tendon repair and regeneration.
Methods: Randomly oriented and aligned silk scaffolds were prepared by directional freeze-drying. Conditioned medium (CM) was generated from ADSCs cultured on the scaffolds with different topography (RCM: random scaffolds; ACM: aligned scaffolds). In vitro experiments were performed to access the effect of RCM and ACM on cell proliferation by live/dead staining, CCK-8 incubation, and Ki67 immunofluorescence. The effect on tenogenic differentiation of tendon stem/progenitor cells (TSPCs) and polarization of macrophages was confirmed by detecting the expression of related genes. Subsequently, RCM and ACM were injected into rats with patellar tendon defects. Tissue repair and immunomodulatory effects were evaluated through histological and immunohistochemical staining.
Result: In vitro results showed that the ACM group had a more potent effect in promoting the proliferation of TSPCs as compared to RCM group. ACM group promoted tenogenic differentiation of TSPCs, as evidenced by higher expression of SCX, TNMD, and MKX in contrast to RCM. In addition, ACM group up-regulated the expression of M2-related anti-inflammatory genes including ARG-1 and IL-10, and down-regulated M1-related inflammatory genes including CCR7, iNOS, and IL-1β in RAW 264.7 cells, as compared to RCM group. The ACM group exhibited a greater formation of tendon-like tissues, as confirmed by histological evaluation, and a higher expression of tendon-related specific proteins, including SCX, TNMD, and COL I as shown by immunohistochemistry as compared to RCM group. The tissue sections of the ACM group showed a high expression of the M2 anti-inflammatory polarity-related protein ARG-1, and a low expression of the M1 pro-inflammatory polarity-related protein iNOS. These results were consistent with the in vitro findings.
Conclusions: This study highlights the topographical dependency of ADSCs paracrine activities and demonstrates the potential of using oriented silk scaffolds to enhance the ADSCs secretome for tendon regeneration. These findings offer a promising, safer, and non-cell-based treatment option for tissue regeneration.
Preparation of ZIF-67/C3N4 composite material and adsorption of tetracycline hydrochloride
Authors: zhang mingyuan , Li Yueyao , Zhou Xiaoying , Wang Liping , Xie Yuke , Hou Chentao 
Published: May 2023

Abstract
In recent years, wastewater treatment to remove tetracycline hydrochloride (TCH), has received much attention in water treatment problems. ZIF-67/C3N4 composite adsorbent, a nanosheet structured material stacked with MOFs, was prepared by in situ growth method, which has high adsorption activity for tetracycline hydrochloride in wastewater. Comparing the effect of monomeric and composite adsorbents, Z6C2 had the best adsorption effect (206 mg·g− 1), which was 77.6% higher than that of ZIF-67 (116 mg·g− 1) and 10.8 times higher than that of C3N4 (19 mg·g− 1).The structure of ZIF-67 stacked on C3N4 nanosheets has an excellent specific surface area and number of active sites, as well as π-π interactions, electrostatic interactions and hydrogen bonding interactions between the adsorbent and TCH, which combine to enhance the adsorption performance. The adsorption process is accompanied by a combination of chemisorption, mass transport and internal diffusion rate-limiting. The adsorbent exhibits good stability and adsorption capacity, which may be suitable for efficient and low-cost water purification.
Reliability and validity of a questionnaire measuring knowledge, attitude and practice regarding “oil, salt and sugar” among canteen employees in China
Authors: Mo Zhenhan , Li Xiaofan , Zhai Yuting , Men Yanyan , Tang Yuying , Qiao Jiarui , Jia Xue , Huang Yujie , Wang Baozhen 
Published: May 2023

Abstract
Background Excessive intake of oil, salt and sugar is closely associated with the prevalence of NCDs. Canteen employees’ knowledge, attitude and practice (KAP) about oil, salt and sugar directly affect the content of them in dishes and the consumers’ intake. However, there is no valid questionnaire used to assess KAP among canteen employees about the “oil, salt and sugar”. Therefore, the aim of the present study was to establish and validate a questionnaire to evaluate the KAP of canteen employees about the “oil, salt and sugar”.Methods A total of 100 canteen employees randomly selected from three different college canteens in Shandong Province. Participants completed the questionnaire and retested 1 month later. Internal reliability and test-retest reliability were assessed using Cronbach’s α and Pearson correlation coefficients, respectively. Validity was assessed using the exploratory factor analysis.Results The final questionnaire consisted of four sections, 76 items: demography (13 items), knowledge (21 items), attitude (20 items), and practice (23 items). A total of 100 participants finished the questionnaire of which 66% were females with a mean age of 40.3 years. The Cronbach’s α coefficients of the questionnaire and each dimension were 0.822, 0.830, 0.752 and 0.700, respectively. The test-retest reliability coefficient is 0.968; In exploratory factor analysis, 9 common factors are extracted, with a total of 26 items, and the cumulative contribution rate is 70.923%.Conclusion The questionnaire had a satisfactory property for measuring the KAP of the “oil, salt and sugar” among canteen employees in China.