bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cervical Cancer Prevention, Its Challenges and Solutions in Iran and Worldwide: A Systematic Review
Authors: Pirani Narges , Jafari Mehdi , Bagherzadeh Rafat , Keikhosravi Marziyeh , Pirani Hadis 
Published: November 2023

Background: Widespread use of screening in high-income countries has led to significant reductions in mortality from cervical cancer. However, in Iran, the main reason for the late diagnosis of cervical cancer was the failure to perform a Pap smear (Papanicolaou). We aimed to investigate the status of cervical cancer prevention and its challenges and solutions in Iran.
Method: We conducted a systematic review of literature published from 1974 to 2021 in the electronic databases, including PubMed, Web of Science, Embase, Scopus, and Google Scholar, and retrieved all English-language articles. Following the application of the inclusion and exclusion criteria, full-text articles were identified and evaluated for eligibility. Finally, these publications were analyzed as part of the synthesis.
Results: Lower social-economic level, inadequate knowledge of screening tests and health centers for Pap test performance leading to worse outcomes such as lower screening participation or coverage.
Conclusion: By addressing these challenges through increasing education, increasing service accessibility, expanding screening programs, improving public awareness, improving insurance coverage, and establishing a control protocol for follow-up, it is possible to reduce cervical cancer incidence and mortality.
Textual Analysis of Tweets Associated with Domestic Violence
Authors: Chua Stephanie , Sabang Janice Allison , Chew Keng Sheng , Nohuddin Puteri Nor Ellyza 
Published: November 2023

Background: Domestic violence is a global public health concern as stated by World Health Organization. We aimed to conduct a textual analysis of tweets associated with domestic violence through keyword identification, word trends and word collocations. The data was obtained from Twitter, focusing on publicly available tweets written in English. The objectives are to find out if the identified keywords, word trends and word collocations can help differentiate between domestic violence-related tweets and non-domestic violence-related tweets, as well as, to analyze the textual characteristics of domestic violence-related tweets and non-domestic violence-related tweets.
Methods: Overall, 11,041 tweets were collected using a few keywords over a period of 15 days from 22 March 2021 to 5 April 2021. A text analysis approach was used to discover the most frequent keywords used, the word trends of those keywords and the word collocations of the keywords in differentiating between domestic violence-related or non-domestic violence-related tweets.
Results: Domestic violence-related tweets and non-domestic violence-related tweets had differentiating characteristics, despite sharing several main keywords. In particular, keywords like “domestic”, “violence” and “suicide” featured prominently in domestic-violence related tweets but not in non-domestic violence-related tweets. Significant differences could also be seen in the frequency of keywords and the word trends in the collection of the tweets.
Conclusion: These findings are significant in helping to automate the flagging of domestic-violence related tweets and alert the authorities so that they can take proactive steps such as assisting the victims in getting medical, police and legal help as needed.
Use of Pistachio Meal and Mono- and Diglyceride in the Production of Low-Fat Pistachio Butter
Authors: Shakerardekani Ahmad , Kavoosi Maryam 
Published: November 2023

Background: Pistachio butter is produced from ground roasted pistachio nuts. Methods: In this study, different formulas of low-fat pistachio butter with 0, 5 and, 10% of pistachio meal and 0, 1 and, 2% of mono- and diglyceride were examined in terms of physicochemical, sensory, and nutritional characteristics. Results: In the fourth month of storage, the highest oil separation (12.4%) from the product was observed in pistachio butter without emulsifier and meal (P≤ 0.05). The most value of carbohydrates and the least value of oil content were observed in the pistachio butter containing 10% meal. The lowest anisidine and peroxide value was detected in the formula with higher content of emulsifier (2%). The pistachio butter formulas without or with 5% meal showed a better green color (lower 'a' value). The pistachio butter with higher values of the meal obtained the least spreadability score. Conclusion: The formula containing 1% mono- and diglyceride and 5% pistachio meal was selected as the best low-fat pistachio butter
Household Food Insecurity and Its Determinant Factors in Eastern Ethiopia in 2021
Authors: Derese Tariku , Belay Yalelet , Fentie Dilnessa , Derese Kirubel 
Published: November 2023

Background: Many households in urban areas suffer from severe food insecurity. In Ethiopia, current economic recession, pandemic, locust invasion, and conflict, disproportionately aggravated the problems. This study aims to assess food insecurity in household and its factors in the Eastern Ethiopia. Methods: A community-based, cross-sectional study was conducted to assess 616 households in urban areas of Eastern Ethiopia using simple random sampling. FANTA Indicator Guide (v.3) Questionnaires were used as data collection tool. Then, the information was entered into Epi-data version 3.02 and exported to SPSS version 21. To determine factors affecting household food insecurity, binary regression analysis was performed, and a p-value of < 0.05 at a 95% confidence interval was considered statistically significant. Model fitness was checked using the Hosmer-Lemeshow test. Results: The prevalence of household food insecurity was 41.7% (95% CI: 37.4–45.6). The following factors were determined as food insecurity predictors: household head's sex [Adjusted Odds Ratio (AOR)=1.75, 95% CI: 1.08–2.84], household head's education (AOR= 0.52, 95% CI: 0.33–0.99), mother's occupation [AOR= 0.3, 95% CI: 0.19–0.57], households with the medium wealth index (AOR =1.72, 95% CI: 1.06–2.78) and households with the lowest wealth index (AOR=4.01, 95% CI: 2.35–6.86). Conclusions: The prevalence of household food insecurity in urban areas of Eastern Ethiopia is high. The food insecurity was significantly affected by residency, education, occupation, and wealth index. The government should focus on socio-economic policies targeting the vulnerable and improve their economic status.
Does Fracture Pattern Really Predict Displacement of LC1 Sacral Fractures?
Authors: Livesey Michael G. , Salmons Harold I. , Butler Bennet A. , Edmond Tyler J. , Slobogean Gerard P. , O’Toole Robert V. 
Published: November 2023

Background:
Operative management of minimally displaced lateral compression type-1 (LC1) pelvic ring injuries remains controversial. We aimed to assess the proportion of LC1 pelvic fractures that displaced following nonoperative management as a function of specific ring fracture patterns, and we quantified the magnitude of this displacement.

Methods:
A retrospective review of the billing registry of a level-I trauma center was performed. Two hundred and seventy-three patients with a high-energy LC1 pelvic ring fracture and <5 mm of sacral displacement were included. The fracture pattern was characterized with use of computed tomography (CT) scans and radiographs. Absolute and interval pelvic ring displacement were quantified with use of previously described methodology.

Results:
Thirty-five pelvic ring injuries (13%) were displaced. The rate of displacement was 31% (15 of 49) for LC1 injuries involving a complete sacral fracture and bilateral ramus fractures, 12% (7 of 58) for injuries involving a complete sacral fracture and a unilateral ramus fracture, and 10% (5 of 52) for injuries involving an incomplete sacral fracture and bilateral ramus fractures. In displaced injuries, the average interval displacement was 4.2 mm (95% confidence interval [CI], 1.8 to 6.8) and the final displacement was 9.9 mm ± 4.2 mm.

Conclusions:
Our study suggests that fracture characteristics can be used to predict the likelihood of displacement of LC1 fractures that are treated without surgery. To our knowledge, the present study is the first to describe the magnitude of displacement that may occur in association with LC1 pelvic ring injuries that are treated nonoperatively; however, further studies are needed to determine the clinical impact of this displacement.

Level of Evidence:
Diagnostic Level IV. See Instructions for Authors for a complete description of levels of evidence.
Methane Recovery with Carbon Dioxide Sequestration as Hydrates in Deep Marine Environments: An Extensive Examination of Potential, Technical Advancements, and Economic Feasibility
Authors: Shrivastava Saket Kumar , Kumar Saket , Kumar Arvind 
Published: November 2023

Abstract
In this study we aim to evaluate carbon dioxide (CO2) hydrate sequestration in ultradeep marine settings, focusing on the Indian deepwater context. Major scientific issues and opportunities are highlighted, including methane (CH4) production through hydrate formation. Economic, regulatory, and technical aspects are explored comprehensively, considering Indian offshore zones. The research involves comparing deposition and potential reserves with CO2 hydrate formation characteristics, kinetic promoters, and potential factors for optimal design criteria. Factors affecting hydrate development, like porosity, salinity, and CO2 interactions, are discussed. Technological and environmental feasibility of hydrate-based carbon sequestration in subsea basins are established. Challenges include kinetics, diffusion, and economics for large-scale Indian basin testing. Chemical interactions, pore water saturation, and depositional environment effects are summarized. We also outline the research necessary for commercial CO2 hydrate storage in Indian basins, building upon previous work. This research contributes to CO2 sequestration and substantial CH4 production in deep marine environments.
Dynamic Assisted History Matching of CO2 Plumes Using 4D Seismic
Authors: Mumtaz Yasir , Blondeau Christophe , Zerkoune Abbas , Khan Osama Hasan , Ali Samad 
Published: November 2023

Abstract
While Carbon Capture and Storage (CCS) has a pivotal role in meeting climate change targets, the question remains, "can we adequately predict the CO2 plume dynamics?" The results so far are not encouraging, which raises concerns about the integrity of sequestration projects and needs to be addressed to capitalise on the value of underground storage. This work is focused on developing an adequate understanding of CO2 migration in a storage unit using an ensemble-based 4D Assisted History Matching (AHM) methodology to improve predictive modelling. Additionally, the study will investigate the critical contributing parameters in the spatial and temporal development of the plume.
We perform a sensitivity study for appropriate selection of the compositional model, accounting for relative permeability hysteresis and identifying influential parameters. The high-resolution reservoir simulator coupled with EnHM Ensemble History Matching software developed by TotalEnergies, is employed to integrate static and dynamic parameters in the AHM workflow. We build an ensemble of 100 realisations for facies and petrophysical properties in the initial step using Truncated Gaussian Simulation (TGS) and Sequential Gaussian Simulation (SGS), respectively. These algorithms cater for uncertainties during data assimilation and ensure geological coherency by constraining the models to the prior information.
The correlation between all uncertain model parameters i.e., static and dynamic, and observations is assessed. The ensemble of the models is then modified using correlations to minimise the difference between simulated response and historical data in an iterative manner. The iteration methodology is based on the Ensemble Kalman Filter (EnKF) and is further enriched by considering the time-lapse seismic as an observation dataset. We define 4D signal in workflow by extracting geobodies from seismic anomalies, and the distance to observed geobody and simulated response is treated as objective function.
The proposed methodology of inculcating the AHM workflow with geological uncertainties and dynamic parameters resulted in a good agreement between simulated and field response while respecting geological realism. The workflow addresses the modelling gaps mainly attributed to the lack of iteration between static and dynamic models, and application of fixed multipliers. It calibrates the hundred reservoir models simultaneously and enables us to make robust and reliable predictions. We conclude that the proposed methodology can potentially improve the prediction of plume migration and make well-informed decisions for all stakeholders. Nonetheless, one topic for future work is to convert the simulation model into a petro-elastic model for direct comparison with seismic response to avoid pre-processing for preparation of geobody and improve the rigorousness of the model.
Experimental Study on Nano Polymer Microspheres Assisted Low Salinity Water Flooding in Low Permeability Reservoirs
Authors: Yuan W. , Hou J. , Yang Y. , Zhao Y. , Nie H. 
Published: November 2023

Abstract
Water flooding in low permeability reservoirs generally results in severe channeling and a large amount of remaining oil. Polymer microspheres and low-salinity water are proven practical approaches for profile control and oil displacement, respectively, and their combination is expected to achieve both effects. This paper evaluates the co-injection of nano-polymer microspheres and low salinity water and its impacts on oil displacement in low permeability reservoirs. Firstly, the influence of injection velocity and injection concentration on the plugging effect of nano-polymer microspheres was evaluated by core displacement experiments. Secondly, the nano-polymer microsphere solutions were prepared using 10-time and 100-time diluted formation water to evaluate the impacts of the co-injection of nano-polymer microspheres and low-salinity water. Meanwhile, the Nuclear Magnetic Resonance T2 spectrum and imaging test were used to reveal the extent of residual oil in pores of various sizes during core flooding as well as the mechanism of oil displacement. The experimental results showed that, compared with nano-polymer microsphere flooding, the composite system of low salinity water and nano-polymer microsphere increased the recovery rate from 17.8% to 24.4%. The subsequent waterflooding stabilization injection pressure increased from 1.40 MPa to 2.43 MPa, and the corresponding plugging efficiency increased from 49.3% to 67.9%. The NMR study indicated that, in the polymer microsphere drive stage, the produced oil mainly came from the large pore spaces, accounting for 75% on average. With a lower solution salinity, the percentage of crude oil produced from the medium pore space to the total oil produced in the microsphere drive stage increased from 15% to 23%. The lower the salinity, the higher the oil produced from small- and medium-sized pores. Our results showed that polymer microspheres eliminated water channeling and changed flow direction, forcing the low-salinity water to enter smaller pores and improving the sweep and oil displacement efficiency. This study confirms the potential of synergistic flooding with low salinity water and nano-polymer microspheres in enhancing oil recovery in low permeability reservoirs. This study is the first to visually assess the impacts of nano-polymer- assisted low-salinity water flooding using NMR online tests. We confirmed that this combined technology successfully achieved both profile control and oil displacement. The nano-polymer-assisted low-salinity water flooding holds the advantages of low cost and simple construction, implying great potential in low permeability reservoirs.
Prediction of Uniaxial Compressive Strength of Sandstone Formations Using Artificial Neural Network
Authors: Mahdi Doaa Saleh , Alhaleem A. Alrazzaq Ayad A. 
Published: November 2023

Abstract
A detailed understanding of rock geo-mechanical characteristics is necessary for enhancing well productivity, optimizing hydraulic fracturing, and maintaining wellbore stability. The expensive cost of measurements of these characteristics makes the log-based estimation a possible alternative. These days, in-situ rock characteristics are estimated utilizing wireline log data and machine learning algorithms. Even though there are many correlations had been proposed to estimate the Uniaxial (Unconfined) Compressive Strength (UCS), the majority of these correlations are built for specific rock types. UCS is affected by various rock properties such as porosity, texture, fluid content and grain size. In this study, an artificial neural network (ANN) model is proposed to estimate the UCS of sandstone formations from well log data (i.e., neutron porosity, bulk density, formation resistivity, and gamma ray) and the corresponding static Young's modulus and shale volume. The performance of the rock strength model is evaluated using statistical techniques to guarantee model dependability and accuracy. The findings demonstrate that the created ANN model is capable of predicting rock strength, which is supported by the excellent agreement between model predictions and Sonic-derived UCS. The Results demonstrate that the ANN model is competent in predicting the sandstone UCS with high accuracy (i.e. R coefficient of the 96% and average absolute error of 7.75%). The suggested approach is anticipated to improve wellbore performance by enhancing the ability of gas and oil professionals to estimate UCS as well as reducing the cost of estimating the geo-mechanical characteristics.
Comparative Analysis Between Empirical Correlations and Time Series Models for the Prediction and Forecasting of Unconventional Bakken Wells Production
Authors: Laalam A. , Tomomewo O. S. , Khalifa H. , Bouabdallah N. , Ouadi H. , Tran T. H. , Perdomo M. E. 
Published: November 2023

Abstract
Accurately forecasting oil and gas well production, especially in complex unconventional reservoirs, is vital. Leveraging advanced techniques like machine learning and deep learning is becoming more common due to ample historical data availability. While traditional methods work for conventional reservoirs, they struggle in unconventional scenarios. Modern machine and deep learning models excel in such challenges, offering insights while bypassing temporary disruptions or pressure issues. This study compares ten empirical production forecast models with state-of-the-art deep learning and time series models (ARIMA, LSTM, GRU) in the Bakken shale play of the Williston Basin. After thorough calibration using extensive data, model efficacy is assessed using R2-score and MSE. Results highlight well-specific performance, with no single model consistently outperforming across all wells. Notably, optimally adjusted ARIMA produced commendable results for many wells. This research aids reservoir engineers by simplifying production decline trend identification, reducing reliance on intricate decline curve analyses. It ushers in a streamlined and dependable paradigm for production forecasting.
Methods for Differentiating Methane and Carbon Dioxide Sources in Aquifers Overlying Gas Reservoirs or Associated with CO2 Storage Sites
Authors: Pearce J. K. , Hofmann H. , Baublys K. , Golding S. D. , Fifield K. , Herbert S. J. , Bhebhe Z. , Matthews I. , Moser A. , Hayes P. 
Published: November 2023

Abstract
Understanding the sources of gases, including methane and carbon dioxide, and their concentrations and isotopic signatures in aquifer groundwater is important in regions where gas, coal or oil are extracted. It is also important where CO2 or other gases are stored or planned to be stored geologically. Methane and CO2 can exist naturally in an aquifer, for example when generated by microbial processes. Aquifer groundwater is often extracted by a variety of overlapping users for agriculture, stock, town and domestic water supply as well as by industries including mines and energy. Water bores drilled into these aquifers can be gassy or may become gassy over time, and gas shows can occur in shallow groundwater, soils, or rivers. Where these are overlying or adjacent to mines, gas and oil extraction or storage reservoirs it poses questions as to the source of gases and may raise queries about leakage. We show several examples where we have sampled gas and waters from Great Artesian Basin aquifers that overly coal seam gas (CSG) reservoirs. In addition, we regionally sampled deep bores in a proposed CO2 storage reservoir, and the surrounding and overlying aquifers. We used a combination of stable isotopes of gases and waters, major and minor ions, strontium isotopes, 36Cl, 14C, and other tracers to understand gas sources and inter aquifer connectivity. In the majority of cases the data support a lack of "leakage" with gases from natural in situ processes. The methods and tools described here can help distinguish sources of gases, and avoid environmental and social licence issues while protecting water resources. They are also applicable to understand sites and potential, real or claimed leakage from shale gas and oil reservoirs, or from other gas storage sites such as for H2, compressed air, or methane.
Rock Physics Based Direct Probabilistic Inversion of Seismic Data for Kerogen Thermal Maturity
Authors: Ross R. , Mandal P. P. 
Published: November 2023

Abstract
A rock physics model is developed that allows forward modelling of the elastic response for the thermal maturity variation of unconventional shale in the seismic domain. The model is calibrated to laboratory core measurements and wireline logs from the Early Cretaceous Toolebuc formation from Queensland, Australia. The model predicts that thermally mature shale should exhibit a significantly different seismic response to shales that are not thermally mature.
Significantly, in this paper, the rock physics model is then coupled with a probabilistic seismic inversion method. This allows the rock physics model to be used in the inverse sense, i.e., driven by the seismic data to predict areas with a high probability of thermally mature shale.
The use of the specific probabilistic inversion method in this study is also significant as the Toolebuc formation is thin relative to the seismic resolution, averaging 20 m - 45 m across the area of the study, and subdivided into three sub-units (Lithofacies1 (LF1), Lithofacies2 (LF2) and Lithofacies3 (LF3). The elastic contrasts are in some cases very weak.
The presented workflow concludes that through the novel combination of a rock physics model of shale thermal maturity, calibration of this model to extensive core measurements as well as wireline logs and a modern probabilistic inversion method, it is possible to infer both the thickness and the thermal maturity of thin shale units despite the bandwidth limitations of the input seismic data.