bluuebunny/crossref_metadata_2025_split huggingface dataset:
Geochemical Artificial Intelligence Tool for Enhanced Water Management
Authors: Birkle Peter , Ismailova Leyla , Tirikov Egor , Saeed Waleed , AlSaif Maram , Al Ibrahim Mustafa 
Published: June 2023

Abstract
Produced water from crude oil and gas producing zones is geochemically heterogeneous due to the input from diverse underground sources and/or operation-related fluids. The present work targets developing and implementing an innovative numerical tool to automatically predict the type and provenance of recovered produced water from exploration and production. Machine learning-based tools was assessed for the automated classification of produced water types in oilfields with a differentiation of formation water from operation-related fluids. Multiple algorithms in machine learning were tested on hundreds of training and testing datasets of produced water samples to identify methods with the highest performance rates. A combination of the supervised XGBoost algorithm with semi-supervised self-training methods resulted in the two best-performing algorithms for all metrics (accuracy, precision, recall) with an accuracy above 90%.
The developed software allows the automated recognition of representative formation water samples, and its differentiation from drilling or operation-related fluids, such as mud filtrate, completion brine, supply water, condensate water, and mixed water types. The algorithm was successfully tested for the classification of hundreds of produced water samples from different fields and reservoirs to define accurate formation water properties for several wells and lithological units. In general, the majority of produced water samples resulted in being post-testimonies from drilling, workover, and production-related activities.
Enhanced water management by processed geochemical data can directly contribute to a potential reduction in drilling lost-time, well cleanup time, and reducing the risk of drilling dry holes. Additionally, well control can be optimized by identifying water inflow zones, and the amount of required bottomhole samples can be reduced by identifying nonrepresentative sampling intervals. Furthermore, geochemical fingerprinting of formation water as dominant reservoir fluid can provide analogous clues on the source rock, migration, trapping, and alteration of hydrocarbons.
Water Digital Avatar—Where Chemistry is Mixed with Machine Learning
Authors: Farrell Jesse , Makarychev-Mikhailov Sergey 
Published: June 2023

Abstract
Water affects almost every operation in the exploration and production (E&P) industry, with its properties important to flow assurance, to three-phase flow pressure/volume/temperature (PVT) modeling, and for fluid compatibility purposes across well construction, stimulation, and production operations. Until now, time-intensive laboratory tests or cumbersome third-party simulators were required to extract physicochemical properties. Here, a family of machine-learning-based reduced-order models (ROM), trained on rigorous first-principle thermodynamic simulation results, is presented.
Approximately 90,000 representative produced-water samples were generated using the United States Geological Survey (USGS) Produced Waters Geochemical Database (Blondes et al. 2019), with systematic variation of the concentrations of 14 common ions. A training data set of 1 million rows was constructed, further varying temperatures and pressures using broad ranges (50-400°F and 14.7-20,000 psi). Thermodynamic simulations were used to generate a data set with more than 500 parameters, including speciation; physicochemical properties such as density, thermal conductivity, heat capacity, and salinity; and notably, the scaling potential for 11 common oilfield scale-forming minerals. More than 20 machine-learning algorithms were screened using cross-validation, and boosted decision trees were found to provide the best accuracy. The CatBoost algorithm (Prokhorenkova et al. 2018) was selected and further optimized. Model validation using unseen data showed relative errors of less than 1% for the majority of predicted properties, which is remarkable for such a complex multicomponent and multiphase system. Simulation details, modeling, and validation results are discussed.
Trained and optimized ROMs can be incorporated in any workflow that depends on water property predictions. As a demonstration, a web application, Water Digital Avatar, was built from these ROMs to quickly and accurately process predictions of water properties and scaling potential on the basis of the entered water composition and desired conditions. The streamlined workflow provides users with model predictions in tabulated and graphical forms for analysis within the web application or offline by means of a downloaded spreadsheet.
The developed ROMs that predict water properties enable automated decision making and improve water management workflows. The presented approach can be further extended to other oilfield, chemical, and chemical engineering applications.
From Chicory Roots to Salmon Fish Waste: All the Challenges of a Trip to Sustainability for Scale Inhibitors and Low Dosage Hydrate Inhibitors
Authors: Parravicini Davide , Di Toto Raul Antonio , Davies Hannah 
Published: June 2023

Abstract
The use of environmentally acceptable inhibitors – particularly in the North Sea - has been of interest for many years. Traditional scale and hydrate inhibitors show poor to moderate biodegradability. P-based inhibitors could lead to eutrophication and polymers are mainly petroleum derivatives. More sustainable technologies have historically suffered from higher costs to maintain the same performance. This paper highlights all the successful efforts made to develop new environmentally friendly and cost-effective technologies having best-in-class performance.
CMI is nowadays a well-known chemistry, derived from chicory roots and patented. It also represents one of the first biopolymer-based scale inhibitors for O&G applications. Despite its good scale inhibition and dispersion performance, the chemical structure is affected by poor thermal stability, which makes this technology less attractive for high temperature oilfield conditions. After extensive R&D work, carried out with the aim of developing new biodegradable, low toxic and ESG compliant technologies not suffering from the same limitations of CMI, two biodegradable phosphonated amino acid chemistries have been developed: PHAAC1 and PHAAC2. While the first product is inherently biodegradable and provides good performance against barium sulfate, the second and most recent readily biodegradable technology is characterized by an exceptional thermal stability and a MIC against calcite 7 to 10x lower than standard chemistries at high temperature (up to 190°C).
Lessons learned were then extended to hydrates inhibitors, with the development of a new cost-effective Kinetic Hydrate Inhibitor based on fish waste, representing a nice example of circular economy. Encouraging performance has been observed and industrialization is currently ongoing. Constant cooling and Isothermal tests provided encouraging results, in line with currently used KHI. More work has to be done to further explore the potential features of this technology.
The systematic applicative R&D approach replicating field conditions, the processes and results described in this work represent a significant step forward, confirming the possibility to develop ESG complaint technologies combining good ecotoxicological profiles and outstanding performance. An approach to circular technologies is also outlined as a base for future development.
Assessment of Friction Reducer Performance Within a Continuous Brine Spectrum
Authors: Aften Carl , Sullivan Keith , Thorpe Russell 
Published: June 2023

Abstract
The performance of friction reducers in standard flow loops is a function of the specific friction reducer, environmental factors such as brine composition and fluid temperature, and the unique loop design and run procedure. This study examines the performance of various commercial and experimental friction reducers using Design of Experiments with variables of friction reducer loading expressed in gallons per thousand gallons, brine Total Dissolved Solids expressed in parts per million, and R+ Hardness expressed as the mole ratio of cationic hardness ions to the Total Dissolved Solids in decimal percentage. The ultimate percent friction reduction is generally dependent upon these variables, and each friction reducer generates unique profiles used to predict performance within the above variable space.
This study evaluated 12 inverse emulsion friction reducers of various charge types in a 100 foot once through pipe. For this design of study, the Total Dissolved Solids was varied from 5,000 to 150,000 parts per million, the R+ Hardness varied from 0.0 to 0.3, and the friction reducer loading varied from 0.25 to 1.00 gallons per thousand gallons. Each reducer was injected into a reservoir containing a brine with the previously referenced, pre-established levels of Total Dissolved Solids and R+ Hardness, and the resulting maximum percentage friction reduction calculated by standard differential pressure protocol.
The response surfaces generated had Ajusted-R2 values from 0.86 to 0.99 demonstrating an exceptionally good fit for the chosen regression model. The contour profiles expressed a general similarity between related charge types with type of charge significantly influencing the response surface profile. Typically, as friction reducer loading increased, percent friction reduction increased or a maximum plateau was achieved, or performance declined. Certain friction reducers’ performances increased as Total Dissolved Solids and/or R+ Hardness increased whereas many reducers’ performances decreased. The gradient of the response surfaces varied from linear progressing to cubic functions with arithmetic transformations.
How friction loop results transmute to full scale hydraulic fracturing operations is not yet fully understood and no delineated process currently exists. However, mapping response surfaces of specific friction reducers then comparing responses to other friction reducers’ response data provides insight and can articulate how reducers’ performances are affected by friction reducer loading, TDS, and R+ Hardness, particularly in operations involving highly variable water quality. As brine character can vary during a hydraulic fractioning treatment, the best practice may be to either use a friction reducer known to perform well within the expected brine level or anticipate using multiple friction reducers for a specific treatment.
Enhancing Anatomy Education Through Cooperative Learning: Harnessing Virtual Reality for Effective Gross Anatomy Learning
Authors: Wang Chao-Ying , Yin Ti , Ma Kuo-Hsing , Shyu Jia-Fwu , Cheng Chia-Pi , Wang Yu-Chao , Huang Yun-Ling , Chiang Ming-Hsien 
Published: June 2023

Abstract
Background: The advent of virtual reality (VR) in education offers unique possibilities for facilitating cooperative learning strategies, particularly in fields demanding intricate spatial understanding such as gross anatomy.
Objectives: This study investigates the impact of integrating cooperative learning strategies within a VR-based gross anatomy curriculum, focusing on enhancing students’ anatomy knowledge and skills.
Methods and Participants: We analyzed the performance of two cohorts of first-year nursing students across five semesters (2016-2020), where traditional learning methods were used in the first three semesters (2016-2018), and a VR-based cooperative learning approach was adopted in the last two semesters (2019-2020).
Results: Our findings suggest that the VR-based cooperative learning group achieved significantly higher scores in their gross anatomy laboratory courses compared to their counterparts learning through traditional methods. Furthermore, instances of VR-induced motion sickness were reported to be less prevalent in the cooperative learning environment.
Conclusions: This research provides valuable insights into how the integration of VR technology and cooperative learning strategies can not only enhance learning outcomes, but also improve the VR-learning experience by reducing motion sickness. It accentuates the potential of VR-based cooperative learning as an impactful educational tool in anatomy education. Future research should further explore the optimal integration of VR and cooperative learning strategies in diverse course types and their potential in enhancing educational outcomes and learning experience.
A SAT Solver + Computer Algebra Attack on the Minimum Kochen-Specker Problem
Authors: Li Zhengyu , Bright Curtis , Ganesh Vijay 
Published: June 2023

Abstract
One of the most fundamental results in the foundations of quantum mechanics is the Kochen--Specker (KS) theorem, a `no-go' theorem which states that contextuality is an essential feature of any hidden-variable theory. The theorem hinges on the existence of a mathematical object called a KS vector system. Although the existence of a KS vector system was first established by Kochen and Specker, the problem of the minimum size of such a system has stubbornly remained open for over 50 years. In this paper, we present a new method based on a combination of a SAT solver and a computer algebra system (CAS) to address this problem. We improve the lower bound on the minimum number of vectors in a KS system from 22 to 23 and improve the efficiency of the search by a factor of over 1000 when compared to the most recent computational methods. Finding a minimum KS system would simplify experimental tests of the KS theorem and have direct applications in quantum information processing, specifically in the security of quantum cryptographic protocols based on complementarity, zero-error classical communication, and dimension witnessing.
Criminal Face Detection System
Authors: Venkatesh Machala , Dhanalakshmi Chintha , Adapa Aditya , Manzoor  , K Anvesh 
Published: June 2023

Abstract
The rise in crime rates and the increasing demand for effective criminal identification and prevention measures have prompted the implementation of security technologies, such as CCTV cameras. To address this need, we present a research study that introduces an automated facial recognition system based on the Local Binary Patterns Histogram (LBPH) classifier and Fisherface algorithm. This system utilizes a Haar feature-based cascade classifier to detect faces in real-time, and subsequently matches the identified faces against a criminal database. Despite the inherent challenges in achieving accurate face identification, the Viola-Jones framework is employed to precisely locate facial positions and other distinguishing features within an image. Face detection classifiers, which are publicly accessible through organizations like OpenCV, contribute to the implementation of our proposed system. The potential benefits of our system include bolstering criminal identification and prevention efforts within both public and private spaces.
How Common is Remission in Rheumatoid Factor-Positive Juvenile Idiopathic Arthritis Patients? The Multicenter Pediatric Rheumatology Academy (PeRA) Research Group Experience
Authors: Ozdel Semanur , Sonmez Hafize Emine , Caglayan Sengul , Akgun Ozlem , Aydın Tuncay , Baba Ozge , Bagrul Ilknur , Yener Gulcin Otar , Ozturk Kubra , Demir Ferhat , Yıldırım Deniz Gezgin , Karadag Serife Gul , Baglan Esra , Cakan Mustafa , Kalyoncu Mukaddes , Makay Balahan Bora , Unsal Sevket Erbil , Bakkaloglu Sevcan , Bulbul Mehmet , Sozeri Betul , Ayaz Nuray Aktay 
Published: June 2023

Abstract
Objective
Rheumatoid factor (RF)-positive polyarthritis is the least common type of juvenile idiopathic arthritis (JIA). Functional disability in RF-positive polyarthritis patients is much more severe than in patients with other subtypes; but data on this subtype alone is limited. This study aimed to analyze clinical features, long-term follow-up, treatment response, and remission status in a large multicenter cohort of RF-positive polyarthritis patients.
Methods
This retrospective study included RF-positive polyarthritis patients that were followed up for ≥ 6 months between 2017 and 2022 by the Pediatric Rheumatology Academy (PeRA)-Research Group (RG). Data on patient demographics, clinical and laboratory characteristics were obtained from medical charts. JIA treatments and duration of treatment were also recorded. The patients were divided into 2 groups based on methotrexate (MTX) response, as follows: group 1: MTX responsive, group 2: MTX unresponsive. Clinical and laboratory findings were compared between the 2 groups.
Results
The study included 56 (45 female and 11 male) patients. The median age at onset of RF-positive polyarthritis was 13.2 years [(interquartile range) (IQR) : 9.0–15.0 years] and the median duration of follow-up was 41.5 months (IQR: 19.5–75.7 months). Symmetrical arthritis affecting the metacarpophalangeal and proximal interphalangeal joints of the hands was commonly observed. Subcutaneous MTX was the preferred initial treatment; however, it was ineffective in 39 (69.6%) of the patients. Of 25 patients followed for 24 months, 56% still had active disease at 24 months.
Conclusion
During 2 years of treatment, 56% of the RF-positive polyarthritis patients did not achieve remission, and they should be considered as a distinct and important clinical entity requiring aggressive and early treatment.
Quantifying the Vulnerabilities of the Online Public Square to Manipulation Tactics
Authors: Truong Bao Tran , Lou Xiaodan , Flammini Alessandro , Menczer Filippo 
Published: June 2023

Abstract
Social media, the modern public square, is vulnerable to manipulation. By controlling inauthentic accounts impersonating humans, malicious actors can amplify disinformation within target communities. The consequences of such operations are difficult to evaluate due to the ethical challenges posed by experiments that would influence online communities. Here we use a social media model that simulates information diffusion in an empirical network to quantify the impacts of adversarial manipulation tactics on the quality of content.
We find that social media features such as high information load, limited attention, and the presence of influentials exacerbate the vulnerabilities of online communities. Infiltrating a community is the most harmful tactic that bad actors can exploit and the most likely to make low-quality content go viral. The harm is further compounded by inauthentic agents flooding the network with engaging low-quality content, but is mitigated when influential or vulnerable individuals are targeted. These insights suggest countermeasures that platforms could employ to increase the resilience of social media users to manipulation.
Pattern formation in reaction-diffusion information propagation model on multiplex simplicial complexes
Authors: Ye Yong , Zhou Jiaying , Zhao Yi 
Published: June 2023

Abstract
Turing pattern for explaining the spatial distribution in nature mostly focused on continuous media and existing networks but there are few attempts at studying them on the systems with high-order interactions. Considering that high-order interactions have a particularly significant impact on rumor propagation, this article establishes a generalized reaction-diffusion rumor propagation model based on a multiplex network, where simplicial complexes are employed to describe the high-order structures. It aims to provide the spatial distribution patterns of the population participating in rumor propagation and identify structural factors that affect such patterns. We theoretically give the necessary conditions for Turing instability in the single-layer and multiplex networks with consideration of high-order interactions. In the numerical simulation, we demonstrate that Turing pattern is controlled by adjusting the diffusion coefficient, high-order structure intensity, and average degree of the network. The results conclude that: (i) in a single-layer network, Turing pattern only exists when high-order interactions appear, and the difference in diffusion rate plays a decisive role, (ii) in a multiplex network, Turing pattern can still be observed under the same diffusion rates, which are affected by the difference of higher-order intensity between two layers, and (iii) in the existing networks, the average degree of the network takes an important impact on Turing pattern. All these findings contribute to comprehending the impact of network structure on pattern formation, especially the high-order interactions on Turing pattern.
Influence of the addition of glycerol-derived polymers on the properties of post-consumer recycled PET
Authors: Seixas Ana Amélia Aragão , Figueiredo L. R. F. , Santos A. S. F. , Medeiros E. S. 
Published: June 2023

Abstract
In the present work, the influence of glycerol prepolymers, as potential chain extenders in the reactive mixture with post-consumer recycled PET (PET-PCR), was studied, namely: poly(glycerol citrate) (PGC), poly(glycerol succinate) (PGSu) and poly(glycerol citrate-co-succinate) (PGCSu). Mixture of recycled PET with 2wt% of glycerol prepolymers or Joncryl ADR®4638, commercial chain extender, were processed in an internal mixer at 265°C for 6min and 60rpm. The compositions were characterized by viscosimetry, carboxylic end groups (CEG) through the Pohl method, crosslinking degree as the percentage of insoluble gel, Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). The addition of glycerol prepolymers increased the fluidity in the post-consumer PET resin, consequently, decreasing its viscosity by 33% for PETPCR/PGC, 24% for PET-PCR/PGSu, 44% for PET-PCR/PGCSu and 17% PET-PCR/Joncryl; the molecular weight in the order of 42% for PET-PCR/PGC, 31% for PET-PCR/PGSu, 55% for PETPCR/PGCSu and 23% for PET-PCR/Joncryl and increasing the number of carboxylic end groups. Although they did not show extensive chain crosslinking, the samples were fragile and brittle, thus making the tensile test unfeasible. Thermogravimetry showed that the polyesters were thermally stable up to approximately 350°C and the sample that presented the lowest thermal stability was the PETPCR/PGCSu corroborating to the highest reduction in the molecular weight recorded. The DSC results showed na almost constant melting and glass transition temperature among the samples, a decrease of 8°C in the crystallization temperature and an increase in the degree of crystallinity relative to neat PET-PCR, which corroborates to the intrinsic viscosity results. These results indicated that at the concentration and processing conditions used, the potential additives to chain extenders had an opposite effect to the desired one, that is, they reduced the molecular weight of recycled PET and the same effect occurred for the commercial chain extender. However, the additive that showed the greatest potential for use as chain extender was PGSu, which presented results similar to the commercial extender Joncryl ADR®4638, composed of multifunctional epoxy groups.
Majorana zero modes in Y-shape interacting Kitaev wires.
Authors: Kaushal Nitin , Alvarez Gonzalo , Dagotto Elbio , pandey Bradraj 
Published: June 2023

Abstract
Motivated by the recent experimental realization of minimal Kitaev chains using quantum dots, we investigate the Majorana zero modes (MZM) in Y -shape Kitaev wires. We solve the associated Kitaev models analytically at the sweet spot (th = ∆) and derive the exact form of MZM wave-functions in this geometry. The novelty of our result is the observation of non-local MZMs near the junction center, made of a linear combination of edge sites MZMs for each arm. Furthermore, we simulate the stability of local (on site) and non-local MZMs modes in the presence of Coulomb repulsion, using density matrix renormalization group theory. Our local density-of-states calculation shows that these non-local MZMs are as equally topologically protected as the local MZMs when in the presence of Coulomb repulsion.