bluuebunny/crossref_metadata_2025_split huggingface dataset:
Estimating Residual Oil Saturation in Carbonate Rocks: A Combined Approach of Direct Simulation and Data-Driven Analysis
Authors: Rizk A. S. , Tembely M. , AlAmeri W. , Al-Shalabi E. W. , Farmanov R. , Markovic S. 
Published: August 2024

Summary
Estimating residual oil saturation (Sor) post-waterflooding is critical for selecting enhanced oil recovery strategies, further field development, and production prediction. We established a data-driven workflow for evaluating Sor in carbonate samples using microcomputed tomography (μ-CT) images. The two-phase lattice Boltzmann method (LBM) facilitated the flooding simulation on 7,192 μ-CT samples. Petrophysical parameters (features) obtained from pore network modeling (PNM) and feature extraction from μ-CT images were utilized to develop tree-based regression models for predicting Sor. Petrophysical features include porosity, absolute permeability, initial water saturation (Swi), pore size distribution (PSD), throat size distributions (TSD), and surface roughness (Ra) distribution. Our method excludes vugs and macro/nanoporosity, which complicates multiscale simulations—a recognized challenge in modeling carbonate rocks. When subdividing the image into numerous subvolumes, certain subvolumes may contain vugs exceeding the dimensions of the subvolume itself. Hence, these vugs were omitted given the entirety of the image constitutes a vug. Conversely, vugs with dimensions smaller than those of the subvolume were not excluded. Despite scale limitations, our subsampling, supported by substantial data volume, ensures our microscale porosity predictions are statistically reliable, setting a foundation for future studies on vugs and nanoporosity’s impact on simulations.
The results show that features obtained from dry-sample images can be used for data-driven Sor prediction. We tested three regression models: gradient boosting (GB), random forest (RF), and extreme gradient boosting (XGBoost). Among these, the optimized GB-based model demonstrated the highest predictive capacity for Sor prediction [R2 = 0.87, mean absolute error (MAE) = 1.87%, mean squared error (MSE) = 0.12%]. Increasing the data set size is anticipated to enhance the models’ ability to capture a broader spectrum of rock properties, thereby improving their prediction accuracy. The proposed predictive modeling framework for estimating Sor in heterogeneous carbonate formations aims to supplement conventional coreflooding tests or serve as a tool for rapid Sor evaluation of the reservoir.
Machine learning interatomic potential with DFT accuracy for general grain boundaries: Analysis of grain boundary energy and atomic structure in α-Fe polycrystals
Authors: Ito Kazuma , Yokoi Tatsuya , Hyodo Katsutoshi , Mori Hideki 
Published: August 2024

Abstract

To advance the development of high-strength polycrystalline metallic materials towards achieving carbon neutrality, it is essential to design materials in which the atomic-level control of general grain boundaries (GGBs), which govern the material properties, is achieved. However, owing to the complex and diverse structures of GGBs, there have been no reports on interatomic potentials capable of reproducing them. This accuracy is essential for conducting molecular dynamics analyses to derive material design guidelines. In this study, we constructed a machine learning interatomic potential (MLIP) with density functional theory (DFT) accuracy to model the energy, atomic structure, and dynamics of arbitrary grain boundaries (GBs), including GGBs, in α-Fe. Specifically, we employed a training dataset comprising diverse atomic structures generated based on crystal space groups. The GGB accuracy was evaluated by directly comparing with DFT calculations performed on cells cut near GBs from nano-polycrystals, and extrapolation grades of the local atomic environment based on active learning methods for the entire nano-polycrystal. Furthermore, we analyzed the GB energy and atomic structure in α-Fe polycrystals through large-scale molecular dynamics analysis using the constructed MLIP. Conventional interatomic potentials cannot accurately calculate the GB energy and atomic structure in α-Fe polycrystals. Conversely, the average GB energy of α-Fe polycrystals calculated by the constructed MLIP is 1.57 J/m2, exhibiting good agreement with experimental predictions. Our findings demonstrate the methodology for constructing an MLIP capable of representing GGBs with high accuracy, thereby paving the way for materials design based on computational materials science for polycrystalline materials.
Assessing crop health and its link to fungal soil microbiome composition through remote sensing
Authors: Sorensen Mathies , Faurdal David , Schiesaro Giovanni , Jensen Emil , Jensen Michael , Clemmensen Line 
Published: August 2024

Abstract

This study explores the link between crop health, observed via hyperspectral satellite imagery, and fungal soil microbiome taxonomy. We connect the normalized difference vegetation index (NDVI), a measure of crop health, to fungal microbiomes in wheat, barley, and maize utilizing a two-step machine learning process. The first step corrects NDVI values for abiotic confounders using a random forest model trained on Lucas 2018 topsoil and era5 climate datasets. The second step clusters operational taxonomy unit (OTU) counts from fungal DNA and investigates the corrected NDVI values for these clusters, revealing significant differences in NDVI values. To identify potential bio-fertilizer candidates, we compare the average relative abundance of OTU clusters and construct sparse biological networks. Bootstrapping and a graphical lasso model select significant links to build the networks. Key findings are: (I) clusters with higher plant pathogenic genera have lower NDVI values; (II) Clusters with higher influential scores for multiple beneficial genera have higher NDVI values; (III) taxonomy with 1-3\% abundance seems to play a key role in regulating microbial networks; (IV) the influence of beneficial vs. pathogenic taxonomy is relative to their abundance. The study links satellite data to fungal microbiomes, providing a baseline for exploring fungal bio-fertilizers.
Predicting the chemical composition of urinary calculi in vivo using gray scale ultrasound
Authors: Liu Ning ning , Li Jing 
Published: August 2024

Abstract

Objective The purpose of this study was to preliminarily predict the chemical composition of urinary calculi using ultrasound in vivo. Methods The data of 267 urinary calculi patients were analyzed retrospectively, including non-contrast computed tomography (NCCT) and ultrasound imaging data before the intervention and post-interventional chemical composition. The Hounsfield unit (HU) value of calculi, the grayscale value of calculi and the grayscale value of posterior acoustic shadow of calculi on ultrasound were measured and analyzed statistically. Results The chemical composition analysis indicated that there were four types of mixed calculi; the main components were calcium oxalate monohydrate (COM) calculi, calcium oxalate dihydrate (COD) calculi, carbonate apatite (CA) calculi and anhydrous uric acid (UA0) calculi. The HU value was distinguished between calcium-containing calculi and UA0 calculi, with a cut-off value of 644.00, a sensitivity of 88.00% and a specificity of 95.04%, and P < 0.001. The grayscale value of calculi on ultrasound was distinguished between calcium-containing calculi and UA0 calculi with a cut-off value of 200.29, a sensitivity of 38.84% and a specificity of 96.00%, P < 0.001. The grayscale value of the posterior acoustic shadow of calculi on ultrasound was distinguished between CA calculi and UA0 calculi with a cut-off value of 31.48, a sensitivity of 58.33% and a specificity of 84.00%, and P = 0.011. Conclusion Ultrasound can preliminarily distinguish the chemical composition of urinary calculi and provide certain information for clinicians to choose treatment plans.
Assessing the impact of complex health systems strengthening program on maternal health care utilization in fragile and conflict-affected states: evidence from the Democratic Republic of the Congo
Authors: Hotchkiss David R. , Blum Lauren S. , Craig Leslie S. , Yemweni Anicet , Wisniewski Janna , Lusamba-Dikassa Paul-Samson 
Published: August 2024

Abstract

Background
The maternal mortality ratio in the Democratic Republic of the Congo (DRC) remains among the highest in the world. However, to date, only limited evidence is available on the impact of maternal health care interventions on service quality and utilization in the DRC (11). The purpose of the study was to investigate the impact of a large, complex health systems strengthening program on maternal health care utilization in DRC.
Methods
The study used difference-in-differences models based on data collected from population-based household surveys at baseline (2014) and endline (2017) in treatment and matched comparison areas in three provinces. The dependent variables consist of the use of antenatal care (ANC) and facility-based delivery. Qualitative analyses were also incorporated in the study to explore perceptions of maternal health care offered and strategies to improve care.
Results
The health systems strengthening program was found to have had a positive impact on the probability of a woman delivering in a health care facility. There was not a significant impact on the receipt of at least four ANC consultations. The qualitative results indicated that uptake of ANC consultations in program areas was challenged by distance to facilities and the time involved in ANC, limited community outreach, and cultural norms.
Conclusions
The research points to the sample population’s high reliance on health centers and posts that provide basic maternal services and low accessibility to hospitals offering lifesaving comprehensive emergency obstetric care. The findings also highlight the need for formative research to inform interventions so that they better match social norms and address contextual needs.
A Novel 2D Nickel(II) Coordination Polymer Based on Trimesic Acid as Ligand For Coordination of Glyphosate
Authors: Gonçalves João M. R. , Wong Ademar , Santiago Pedro H. O. , Arroyos Guilherme , Piva Augusto H. , Ellena Javier A. , Demets Gregóire J. F. , Lima-Neto Benedito S. , Frem Regina C. G. 
Published: August 2024

Abstract

This work presents the synthesis and characterization of a new coordination polymer of the MOF type named Ni-BTC, where Trimesic Acid (H₃BTC) acts as the ligand and nickel as the metal center. The structure was initially determined by Single Crystal X-ray Diffraction (SCXRD), revealing that the MOF has a minimal formula {[Ni(HBTC)(DMF)₂]·xDMF}, exhibiting a 2D dimensionality. The Ni(II) center is hexacoordinated, expanding in the equatorial direction, while N,N'-dimethylformamide (DMF) molecules occupy the axial positions originating from the solvent used in solvothermal synthesis. Additionally, Co-BTC MOF was synthesized, showing an isostructural structure to Ni-BTC, with similar coordination modes and numbers for BTC and DMF ligands. Analysis using Field Emission Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy (FESEM-EDS) revealed the presence of two distinct phases in Ni-BTC, highlighting the influence of the counter-ion in modulating phases during synthesis. Ni-BTC was further characterized by thermal methods such as Thermogravimetric Analysis and Differential Thermal Analysis (TGA and DTA), demonstrating moderate thermal stability. Magnetic Susceptibility measurements indicated both metal centers are in the + 2 oxidation state. Finally, the bidentate molecule glyphosate (GLY) was used to coordinate with Ni-BTC, replacing the more labile axial DMF ligands. This coordination was confirmed by Powder X-ray Diffraction (PXRD) and Infrared Spectroscopy (FTIR), along with electrochemical techniques such as Cyclic Voltammetry (CV) and Square Wave Voltammetry (SWV). These results highlight the potential application of glyphosate-modified Ni-BTC in glyphosate detection and removal systems.
Enhancement in the Performance of Concrete using Crystalline
Authors: Firoj Mohd , Bahuguna Ashish 
Published: August 2024

Abstract

Concrete structures suffer deterioration due to penetration of water, aggressive chemicals or other liquids in concrete, which leads to degradation and other issues which abbreviate the life of the concrete structure. If such penetration continues, the durability of the structure is seriously affected. Ingress of water or dampness or atmospheric pollution in the RCC structures results in corrosion of steel and spalling of concrete, thus endangering the structure’s integrity and durability. In the present study, the crystalline admixture is used in concrete with the experimental investigation to avoid these problems. Concrete specimens with 0.8% by weight cement admixed concrete are investigated and compared with control concrete. Compressive strength tests, water permeability tests, rapid chloride penetration test, and durability studies of the specimen are performed. The performance of crystalline admixture in terms of durability indicates that admixed concrete is better than control concrete. Crystalline admixture is one of the best solutions to reduce the water permeability or resistance of water permeability in concrete.
Prediction of late-onset depression in the elderly Korean population using machine learning algorithms
Authors: Park Jong Wan , Ko Chang Woo , Lee Diane Youngmi , Kim Jae Chul 
Published: August 2024

Abstract

Late-Onset Depression (LOD) refers to depression that newly appears in elderly individuals without prior depression episodes. Predicting future depression is crucial for mitigating the risk of major depression in prospective patients. This study aims to develop machine learning models to predict future depression. Using public data from the nationwide panel survey ‘Korean Longitudinal Study of Aging,’ we employed latent growth modeling and growth mixture modeling to identify four latent classes of depression trajectories in the elderly Korean population. Based on the results of binary logistic regression, we selected 12 variables capable of distinguishing the LOD population from the reference population and tested 12 machine learning (ML) algorithms. While most ML algorithms showed acceptable predictive capability, LIGHTGBM, GBC, and RF demonstrated superior performance. Consequently, we successfully established new ML-based LOD prediction programs. These programs could be further developed into self-checking online tools, expected to serve as decision support systems for primary medical care and health screening services.
Deep Intergranular Fluoride Attack by High Temperature Corrosion on Alloy 625 by LiF in Air at 600°C
Authors: Nikbakht Aida , Bahramian Behnam , Geers Christine 
Published: August 2024

Abstract

In most chemical and high temperature processes metals are exposed to temperature gradients which in turn affect the extent of corrosion phenomena. In this study, a long, continuous strip of alloy 625 was exposed to lithium fluoride in a temperature range of 50–600°C, air environment. The hottest section of this strip was analyzed as a coupon and compared with two other coupons which were exposed isothermally. One of the isothermal exposures was carried out in a tube furnace and the other one was in a vertical furnace. Oxygen had three different kinds of access to these three coupons, which in turn affected the corrosion process. In order to limit the access of oxygen, a long column of lithium fluoride was used in a vertical furnace. The results of the isothermal exposure showed that more access of oxygen in a horizontal tube furnace facilitated the fluoride ingress to a great extent. However, a long sample exposed to a temperature gradient suffered more corrosion attack than the isothermal coupon, under the same LiF load in the vertical furnace. This was associated with the reduction of oxygen at a larger cathode area reaching into colder regions in Inconel 625 strip. Increased oxygen reduction also increases the efficiency of an inner anode at the hottest section, causing the observed rapid intergranular fluoride uptake. The study proposes a mechanism explaining these observations.
Simulation Study of the Risk Transmission of Energy Supply Chain in Sichuan-Chongqing Region Based on SEIRS Model
Authors: Liu Bo , Sun Xiaowen , Qin Tong 
Published: August 2024

Abstract

The safe and efficient operation of all network nodes in the energy supply chain is of great significance for promoting China's economic development. In order to explore the internal law of risk transmission in the energy supply chain, this paper establishes a SEIRS (Susceptible-Exposed-Infected-Recovered-Susceptible) model for the risk transmission of the energy supply chain in the Sichuan-Chongqing region and analyzes the impact of its threshold on the risk transmission in the energy supply chain in the Sichuan-Chongqing region using MATLAB software. The study analyzes factors affecting risk transmission and puts forward suggestions for improving the stability and competitiveness of the energy supply chain. The results show that the model can provide some references for policymaking for the security and stability of energy supply in the Sichuan-Chongqing region.
Targeting IKK epsilon to Prevent Meniscal Degeneration in Osteoarthritis: Insights from Human Tissue and Mouse Models
Authors: Hirose Ryota , Akasaki Yukio , Kuwahara Masanari , Uchida Taisuke , Hyodo Yuki , Sakai Mamiko , Kita Takumi , Kurakazu Ichiro , Lotz Martin , Nakashima Yasuharu 
Published: August 2024

Abstract

Objective. Meniscal degeneration may precede or indicate early-stage osteoarthritis (OA); however, the pathogenetic involvement of the NF-κB pathway and its upstream IκB kinase (IKK) is unclear. This study investigated the functional role of IKK in the pathogenesis of meniscal degeneration, and the efficacy of IKKε inhibition as a therapeutic approach.
Methods. IKK expression in normal and OA human menisci was analyzed immunohistochemically. Gain- or loss-of-function experiments were performed in human meniscal cells. Additionally, meniscal degeneration was induced in wild-type mice and treated with intraarticular injection of the IKKε inhibitors amlexanox and BAY-985 every 5 days for 4 weeks. Meniscal degeneration was also induced in IKKε knockout mice. Mice were subsequently examined histologically.
Results. IKK protein expression was increased in human OA menisci. In vitro, the expression of meniscal degeneration–related factors was decreased after knockdown of each IKK, particularly IKKε, using small interfering RNA in human OA meniscal cells. Conversely, IKKε overexpression significantly increased the expression of these factors, and amlexanox and BAY-985 canceled this effect. Western blot analysis showed that IKKε overexpression increased IκBα and p65 phosphorylation. In vivo, both IKKε deletion and intraarticular injection of IKKε inhibitors protected mouse menisci against degeneration.
Conclusion. These results indicate that IKKs are involved in meniscal degeneration when it constitutes the preliminary or early stage of OA, with IKKε possibly playing a significant role. Furthermore, IKKε regulates meniscal degeneration through NF-κB signaling–mediated catabolism. Two IKKε inhibitors, amlexanox and BAY-985, are potential targets for the treatment of meniscal degeneration prior to OA.
Assessing Landslide Susceptibility of Türkiye at Drainage Basin Scale: A Semi-quantitative Approach
Authors: Okalp Kıvanç , Akgün Haluk 
Published: August 2024

Abstract

This study uses the Analytical Hierarchy Process (AHP) integrated with Geographic Information Systems (GIS) to assess landslide susceptibility in Türkiye at a drainage basin scale. Türkiye's mountainous terrain makes it highly prone to landslides, especially due to heavy rainfall from climatic changes. Addressing the need for comprehensive assessments, the study integrates multiple parameters such as slope, lithology, internal relief, land cover, and rainfall intensity using publicly accessible datasets such as digital elevation models (DEM), geological maps, and rainfall records. The first comprehensive landslide susceptibility map for Türkiye was produced, validated through histogram and ROC curve analyses, resulting in 1:500,000 scale maps for each basin. This assessment offers a detailed understanding of landslide occurrences, identifying that moderate slopes (5°-15°) and internal relief (200–250 m/km²) significantly influence landslides. A potential threshold for the Topographic Wetness Index (TWI) at 12–13 was also identified. The study highlights the importance of DEM resolution and strategic area subdivision for detailed analysis. The maps and classification methods provide a valuable framework for future research, enhancing understanding of geological hazards and aiding decision-makers in managing landslide risks. Recommendations include prioritizing open data, evaluating TWI, and refining classification methods for improved accuracy.
The Postoperative Lidocaine and Ketamine Effects on Morphine Requirement in Bariatric Surgery
Authors: Pérez Gregory Contreras , Avendaño Carolina Frederico , Cotínez Luis Ignacio , Crouseilles José Giménez , Carví Alex 
Published: August 2024

Abstract

Background
Effective postoperative pain management in obese patients undergoing bariatric surgery is challenging due to the adverse effects associated with opioid use. Multimodal analgesic approaches during the intraoperative period have shown to effectively reduce postoperative opioid consumption This study evaluates the impact of prolonged postoperative lidocaine and ketamine infusion for 90 minutes on postoperative morphine consumption as a complementary multimodal analgesic approach.
Methods
This retrospective cohort study included 64 patients who underwent elective sleeve gastrectomy and gastric bypass. Thirty-two patients who received lidocaine and ketamine infusions postoperatively (Group A) were compared with 32 patients who received standard postoperative analgesia (Group B). The primary outcome measured was total morphine consumption within the first 48 hours post-surgery. Pain was assessed using the Visual Analog Scale (VAS) at 1, 2, 4, 24, and 48 hours post-surgery.
Results
Mean cumulative morphine consumption at 48 hours was 0.82 ± 1.55 mg in Group A versus 2.03 ± 2.61 mg in Group B (p = 0.0696). : In total, 62.5% of patients did not require morphine during the first 48 postoperative hours. VAS scores were significantly lower in Group A at the four postoperative hour (1.03 ± 1.36) vs Group B (2.16 ± 1.65), p-value 0.0024.
Conclusion
Postoperative morphine consumption and pain scores were low with the current multimodal analgesic approach. Prolonging lidocaine and ketamine infusion for 90 minutes postoperatively is not justified based on current results.
COVID-19 symptoms and obesity in Korean airline crew
Authors: Jeong Yeo-Won , Kim Jung-Ha 
Published: August 2024

Abstract

Background We aimed to investigate the differences in coronavirus disease 2019 (COVID-19) symptoms according to weight among flight and cabin attendants in Korea.Methods This study was designed as a cross-sectional survey, comprising 180 airline crew members.Results Out of 180 participants, 22 participants had no symptoms at the onset. Among the 158 participants who experienced symptoms, the most common ones were fatigue (79.7%), cough (63.9%), and myalgia (63.3%). Participants who did not currently smoke and drank less than once a month reported more symptoms of COVID-19. The number of COVID-19 symptoms was significantly higher in participants with normal weight compared to those who were overweight/obese (p = .004). The time taken to return to flight duties after completing COVID-19 treatment was significantly longer for participants with normal weight compared to those who were overweight/obese (p = .041).Conclusion Airlines should make efforts to manage fatigue in airline crew members infected with COVID-19. Additionally, smoking and weight have an impact on the severity of COVID-19 in young people; therefore, continuous management is necessary, even if the number of reported symptoms is small.