bluuebunny/crossref_metadata_2025 huggingface dataset:
Critical Thresholds for CO2 Foam Generation in Homogeneous Porous Media
Authors: Tang Jinyu , Wei Bing , Yang Mengke , Rossen William R.
Published: November 2024

Long-distance propagation of foam is one key to deep gas mobility control for enhanced oil recovery and CO2 sequestration. It depends on two processes—convection of bubbles and foam generation at the displacement front. Prior studies with N2 foam show the existence of a critical threshold for foam generation in terms of a minimum pressure gradient ∇pgenmin or minimum total interstitial velocity vt,genmin, beyond which strong-foam generation is triggered. The same mechanism controls foam propagation. There are few data for ∇pgenmin or vt,genmin for CO2 foam.

We extend previous studies to quantify ∇pgenmin and vt,genmin for CO2 foam generation and, for the first time, relate ∇pgenmin and vt,genmin to factors including injected quality (gas volume fraction in the fluids injected) fg, surfactant concentration Cs, and permeability K. In each experiment, steady pressure gradient ∇p is measured at fixed injection rate and quality, with total interstitial velocity vt increasing and then decreasing in a series of steps. The trigger for strong-foam generation features an abrupt jump in ∇p upon an increase in vt.

In most cases, the data for ∇p as a function of vt identify three regimes, which are coarse foam with low ∇p, an abrupt jump in ∇p, and strong foam with high ∇p. The abrupt jump in ∇p upon foam generation confirms the existence of ∇pgenmin and vt,genmin for CO2 foam. We further show how ∇pgenmin and vt,genmin scale with fg, Cs, and K. Conditions that stabilize lamellae reduce the values of the thresholds: Both ∇pgenmin and vt,genmin increase with fg and decrease with increasing Cs or K. Specifically, ∇pgenmin scales with fg as (fg)2 and vt,genmin scales as (fg)4, and both ∇pgenmin and vt,genmin scale with Cs as (Cs)−0.4. The effect of K on the thresholds for foam generation is greater than the effects of fg and Cs. Our data in artificial consolidated cores show that ∇pgenmin scales with K as K−2 for CO2 foam, in comparison with K−1 for N2 foam in unconsolidated sand/bead packs. More data are needed to verify these correlations.

It is encouraging that ∇pgenmin in the cores with K = 270 md or greater is less than 0.17 bar/m (~0.75 psi/ft), two to three orders of magnitude less than for N2 foam. Such low ∇pgenmin can be easily attainable throughout a formation. This suggests that limited ∇p deep in formations is much less of a restriction for long-distance propagation of CO2 foam than for N2 foam. Foam propagation could still be challenging in low-K reservoirs (∇pgenmin ~10 bar/m for K = 27 md). Nevertheless, formation heterogeneity and alternating slug injection of gas and liquid help foam generation and can reduce the values of ∇pgenmin. More research is needed to predict long-distance propagation of foam at low ∇p and vt.
Proven Advanced Data Science Techniques for Improved Automated Lithology Classification Using Well Log Data
Authors: Kurmankulov Alikhan , Kurmankulova Zhanat
Published: November 2024

The proposed paper introduces a novel machine learning-based approach that incorporates advanced data pre- and post-processing techniques to significantly enhance automated lithology classification using well log data.

Through rigorous testing of various machine learning algorithms and data science methods, the final approach emerged as the optimal solution. It integrates advanced well log data visualization, exploratory data analysis (EDA) for feature selection and engineering, the use of the Random Forest algorithm for data pre-processing and classification, and advanced post-processing techniques to refine the predictions of the machine learning model. These post-processing steps are particularly novel, as they address the unique challenges of lithology classification in the petroleum industry.

The approach was developed during a company-wide hackathon with over fifty participants from around the globe. The objective was to design an accurate, automated system for predicting lithology using real-world well log data. The dataset included measurements from twenty wells with twelve interpreted facies and ten additional wells for evaluation. The complexity of the data, such as missing logs and incomplete measurements, mirrored real-world challenges. The proposed approach excelled in this context, achieving the highest competition score and securing first place in the hackathon.

This paper presents a proven method for automated facies classification using machine learning, validated by real field data. It underscores the importance of integrating domain expertise with advanced data science techniques, particularly post-processing, to deliver value in the age of AI to the petroleum industry.
Fluid Design for Acid Fracturing and Stimulation in HT Reservoirs Accounting for Cooldown Effect Based on Measurements and Simulation
Authors: Yudin A. , Rivas J. , Farouk M. , Al-Jalal Z. , Nurlybayev N. , Kharrat W.
Published: November 2024

Fracturing operations in tight reservoirs tend to be driven by efficiency and costs of the operations due to low reservoir quality and field development economics dictate the continuously increasing demand to deliver more stages within a short period of time. The objective of this paper is to revise and enhance fluid design parameters in high-temperature (HT) reservoirs by dealing away with fluid over-designing practices through accurate fracturing modeling which will significantly reduce reservoir damage.

Fluid design practices often assume a safety factor for the fluid exposure duration to be greater than the full treatment duration. It is also commonly required to match the bottom hole static temperature (BHST) for the whole lab test with a minor focus on the overall tubulars and reservoir cooldown during later stages of the treatment. The data frac results were then compared with the experimental lab condition in order to measure leak-off severity and cool-down dynamics in both scenarios. The result was injected into a detailed sensitivity analysis in order to come up with the optimum fluid design assumptions.

Advanced software modeling and rheological studies identified several ways to avoid fluids overdesign, leaving behind cleaner reservoirs without imposing operational risks. The sensitivity analysis revealed that depending on several parameters such as the fluids temperature at surface, volume and rate of the treatment; the safety factor applied in typical laboratory practice may reach up to 300% for some additives in the acid systems. This translates into additional challenges to formulate the fracturing fluid for excessive exposure periods, which as proven in lab experimental conditions, is not really required. Even more, the excess treatment chemicals create significant reservoir damage. The studies provided a workflow describing how and where fluid design can be tailored to reservoir conditions and a decision tree for fluid design in extreme HT formations.

This paper will reveal a modeling and experimental approach to optimizing assumed parameters of the fracturing and stimulation fluids and will help to shape the future of the treatments in HT reservoir conditions in a way that will not only reduce the fracturing cost but will also help to reduce reservoir damage hence improving well productivity.
Successful Implementation of Integrated Forecasting Tool for Complex Near Critical Jurassic Reservoir Fluids of North Kuwait
Authors: Al-Enizi Nawaf , Ameer Ali Abbas , Al-Saffar Ali , Khalaf Sara Ben , Abdullah B. , Rao Narhari , Karamahmut Umut , Rice John
Published: November 2024

Gas Fields Development asset in Kuwait Oil Company (KOC) has recently achieved the distinction of generating an Asset Action Plan (AAP) utilizing a new forecasting tool. In the past, the AAP was generated by a tool developed with visual basic functions in Excel, relied on the vendor for permanent code maintenance, and lacked full control of KOC staff. The management steer was to ensure activity continuity in KOC by regenerating AAP with an industry-standard commercial tool.

An immediate need for utilizing an industry-standard AAP forecasting tool was initiated. The functional specifications required by the asset were shared with several vendors. After rigorous review, AAP new tool was selected as it satisfied most modeling requirements such as the ability to handle a high number of wells over many fields and reservoirs, flexibility to run multiple development scenarios, and reduced run time that will enable sensitivity runs to be completed within limited preparation schedule. A comparison of the previous tool's outputs with the new tool of one of the previous year's results showed similar results with much less effort.

AAP tool takes subsurface dynamic simulation model results, drilling, and facilities schedules as input through the Data Management Tool (DMT) and the Decline Curves Extractors (DCEs). The DMT sheet is the main interface for all the dynamic model data while the DCEs are the well-specific decline curves initially grouped to form equivalent tank decline curves based on reservoir pressure performance.

After the completion of one of the legacy AAP P50 scenario using new tool, AAP new tool and the previous tool's forecast profiles were compared. The resulting profiles and cumulative production are similar. The asset strategic gas plateau rate from AAP new tool vs the previous tool was very similar.

The differences are within the calculations tolerance showing the new and previous tools resulted in a similar forecast. The cumulative volumes are up to the economic threshold of 50 MMscf/d. Major advantage of the new tool is fast run performance which requires ~ 1/3 of the time required for the previous tool.

After completing the testing phase with the new tool, further improvements were agreed with the vendor. New functionalities will make AAP a standalone, automated tool that is capable of linking to official AAP platform of KOC, Corporate Portfolio Management System (CPMS). Post processing of the AAP profiles including generation of opportunity profiles and calibration to short term production forecast (STPF) are additional improvements. This results in generation of an optimized drilling sequence.
Advanced Geothermal Well Testing Proves Sustainable New Energy Potential
Authors: Pham T. , Kaipov Y. , Maurya A. , Alawagi A.
Published: November 2024

In the past decade, the oil and gas industry has been progressively shifting towards renewable energy sources. Companies within the sector are exploring a variety of renewable technologies with the goal of achieving carbon neutrality by mid-century. Among these initiatives, geothermal energy has emerged as a significant focus in recent years.

Geothermal well testing, akin to well testing in the oil and gas industry, is crucial for evaluating the economic viability of geothermal projects. Despite the similarities, there are fundamental differences in the hardware used for geothermal and oil-and-gas well testing. This necessitates a re-evaluation of existing geothermal well testing techniques to ensure safety and data integrity.

This paper analyzes the technological gaps in conventional geothermal well testing and introduces advanced well testing technologies and equipment designed to enhance the performance evaluation of geothermal wells. A novel approach to geothermal surface well testing has been developed, significantly improving the efficiency of reservoir performance assessments. This was achieved through a combination of redesigning well testing equipment and workflows, and digitalizing data acquisition, integration, and calculation processes.

A hybrid design, incorporating the best features of both conventional geothermal and oil-and-gas well testing, was employed to ensure operational safety, particularly in controlling unknown H₂S. The use of digital electromagnetic flow meters, alongside traditional weir- box methods for flow rate measurement, was key to ensuring data accuracy and redundancy. The real-time integration of digitalized surface well test data with downhole electrical submersible pumps (ESP) was critical for the rapid evaluation of well test data and making necessary adjustments to the testing program in real-time.

These advancements not only enhance the safety of geothermal well testing operations but also significantly improve data acquisition and the evaluation of well potentials. This paper discusses the development of these advanced well testing technologies, lessons learned, and recommendations for further system improvements.
Simultaneous Determination Technology for Water Content and SARA Group Composition of Oil in Water-Oil Emulsions Using NMR Relaxometry
Authors: Fazlyyyakhmatov M. , Bakhtigaraev R. , Varfolomeev M.
Published: November 2024

The paper presents a technology for the quantitative analysis of water-oil emulsions using low-frequency pulsed NMR relaxometry, which allows for online monitoring of water content and the SARA group composition of oil. This technology can be applied in hydrocarbon production to determine the effectiveness of enhanced oil recovery methods at a specific well and/or field by controlling water content in real-time to determine the quantity and rate of demulsifier addition in separation units and facilities for desalting and dehydrating emulsified oil. Additionally, it can be used to enhance the efficiency of crude oil processing at petrochemical plants.

The study presents the results of examining samples of artificial water-oil emulsions of heavy oil with water content ranging from 10% to 50% by mass. It is shown that the method can determine water content up to 40% with an uncertainty not exceeding 15%, without affecting the quality of the oil's group composition determination.
Using Ultra-Low Emission Burner for Zero Gas Flaring in Oil Fields
Authors: Magomadov I. A. , Balhasan S. , Wahid F. , Izturganov T. B. , Iskakov R. , Hamad A. , Lyakhovskaya V. , Hassan Z. , Duot M. , Ame Y. , Sageer I.
Published: November 2024

This study introduces an innovative ultra-low emission burner designed to eliminate gas flaring in oil fields, contributing to global zero-flaring objectives. The burner integrates advanced combustion technologies and surface combustion to significantly reduce harmful emissions, including NOx and CO, by more than 90%. Through mechanical design refinement and extensive field testing, the burner has demonstrated high adaptability across diverse operational conditions, ensuring minimal disruption to existing workflows. The use of previously wasted flare gas to generate energy has resulted in increased overall efficiency, aligning oil field operations with stringent environmental standards and sustainability goals. A case study conducted in a residential area demonstrates the ULEB's effectiveness, reducing CO2e emissions from 1440 metric tons (venting) to 158 metric tons per day. The burner's design ensures adaptability to various operational settings and minimal interference with existing infrastructure. The findings from the case study highlight ULEB's potential to contribute to sustainable energy practices and compliance with stringent environmental standards, positioning it as a critical solution for the oil and gas industry.
Using ML-Supervised Learnings Based-Algorithms to Create a Relative Permeability Model
Authors: Aggarwal Akshay , Kumar Saket , Kumar Subbiah Surej , Bajpai Prakash
Published: November 2024

Relative permeability analysis in the field begins with compiling Special Core Analysis (SCAL) experimental data on core samples. Conventional methods categorize samples by parameters, including rock quality index (RQI), flow zone indicator (FZI), or Winland R35, based on porosity and permeability. Samples are binned by parameter ranges, and collectively analyzed to derive representative permeability curves. The curves receive endpoint analysis, normalization, and denormalization for different rock type bins as per the previously mentioned parameters. However, this relative permeability analysis is a tedious task and requires significant time. Hence, this paper presents a robust and effective machine learning (ML) based approach to derive the relative permeability data sets readily for reservoir engineering study tasks.

This paper presents a Machine Learning (ML) based approach by compiling a database of laboratory-derived SCAL experiments. Thirty-seven experimental oil-to-water relative permeability datasets were collected, which comprised of around 350 data points specific to sandstone reservoir settings. Subsequently, residual oil (Sorw) and irreducible water (Swir) values were tabulated for each core sample. The ML regression models were trained to predict Sorw and Swir using core porosity and permeability as feature variables. Subsequently, core porosity, core permeability, and water saturation (Sw) from relative permeability (kr) experiments were incorporated as features to model krw and kro in the regression models. The trained ML models were then used to further predict the krw and kro curves for any core porosity and permeability for varying water saturation points/steps.

It's often observed that multiple relative permeability curves arise when dealing with varying rock properties, such as permeability and porosity. However, when preparing a bin of rock types, we typically rely on an averaged relative permeability curve for each rock type based on porosity and permeability ratios. This averaging process often necessitates extensive manual calculations and can be quite time-consuming. In this paper, we present an approach that allows for the prediction of two-phase oil and water relative permeability across a range of datasets derived from specific reservoirs with different pore geometries. The derived curves can be effectively utilized in reservoir simulation exercises. We also compare these proposed curves to those generated using the conventional method of averaging relative permeability curves through a modified Brooks-Corey model.
SimOps Maps: A New Approach to Operational Efficiency in the Oil and Gas Industry
Authors: Russayev M.
Published: November 2024

During the active construction phase of the Future Growth Project (FGP), SimOps Maps have proven to be an indispensable tool. Integrated within the Geographic Information System (GIS) SimOps platform, these maps have significantly transformed project planning and execution. A standout feature of the GIS SimOps platform is its capability to enable planners from various groups to detect potential working activity clashes early in the planning stages. This proactive identification allows for timely adjustments during implementation, ensuring a smoother and more efficient workflow. By leveraging SimOps Maps, the FGP has achieved enhanced coordination and minimized disruptions, underscoring the critical role of advanced GIS tools in modern project management.
Advanced Oil Fingerprinting Technology for Enhanced Reservoir Characterization and Development
Authors: Shipaeva M. , Shakirov A. , Nurgaliev D. , Sudakov V. , Fazlyyyakhmatov M.
Published: November 2024

This study investigates the geochemistry of reservoirs in a mature oil and gas province, focusing on the use of oil and water fingerprinting for fluid source identification, production allocation, and reservoir characterization. The research aims to optimize field development strategies through advanced data processing methods. Over 100 oil samples from different fields were analyzed to determine the chemical composition of oil and its fractions. Gas chromatography and high-resolution mass spectrometry were used to identify various compounds. Advanced clustering methods allowed for effective interpretation of the results.

We found significant variations in hydrocarbon composition across formations within the same oil and gas province, providing insights into oil age, migration patterns, and reservoir heterogeneity. The integration of geochemical data into field operations has improved reservoir characterization accuracy and production efficiency, particularly in mature fields where traditional methods such as production logging (PLT) tools have proven less effective.

Additionally, the findings emphasize that no universal biomarkers exist; each oil field, even neighboring ones, has a unique set of fingerprints. This prompted the development of a chromatogram processing algorithm that clusters data for any oil field, further advancing geochemistry methods in the industry. This work introduces novel oil and water fingerprinting techniques, offering significant advancements in geochemical reservoir assessment and providing valuable tools for improving field development and management practices in hydrocarbon basins.
AI Seismic Interpretation: Improving Our Understanding of the Subsurface
Authors: Whittaker H.
Published: November 2024

Manual seismic interpretation has long been the backbone of E&P workflows however it is a process that demands significant time and a highly skilled interpreter. Artificial intelligence (AI) technology, specifically those that utilise deep learning convolutional neural networks have emerged as a credible alternative, producing results of comparable accuracy and detail.

A key area where AI solutions have already accelerated seismic interpretation workflows are in the delineation and mapping of faults and horizons, as well as identifying and defining features, such as karst platforms or injectites. These workflows have been tested on datasets of varying qualities and ages, as well as on both onshore and offshore datasets.

Once the faults can be detected by the AI networks, the next stage becomes horizon detection and this is done by identifying every single peak and trough in a seismic cube however, accurately correlating individual horizon patches proved to be a much bigger challenge than detecting the discontinuities. A new solution was developed that is entirely aware of faults and understands the interplay between the faults and the horizons it is analysing. The fact that the horizons are aware of the faults means these objects are the perfect input to generate a watertight model. The latest stage of the AI seismic interpretation workflow involved creating a solution to identify and extract geological features such as channels and injectites. AI networks enable the fast, accurate and unbiased analysis of dozens of volumes, applying the same criteria and rigour to every voxel in every volume. For example, every partial stack – angle stacks, azimuth stacks – can now be processed with the same AI network, allowing the human interpreter to focus on comparing and interrogating the resulting AI fault volumes to understand their significance. Furthermore, due to the AI Network's ability to assign more nuanced fault confidence values from 0 to 100%, we can investigate the uncertainty around the structural model based on fault confidence results.

Finally, all of these AI Networks can be fine-tuned by the addition of manually interpreted data labels to add extra information to the neural networks. This can result in increased fault recognition, reduced false positives and an increase in fault confidence amongst many other desirable results. These AI-based techniques significantly enhance the speed and quality of the interpretation of the subsurface. With the help of AI we can rapidly identify and extract faults, horizons and geobodies with unprecedented detail and accuracy. The time saved on manual interpretation can be used to better understand the geology and analyse feasible scenarios, enabling regional and field assessment to benefit from the most up to date information and ensuring high-quality exploration and development decisions.
Multi component biosorption of U(VI), Cu(II), and Ni(ΙΙ) in the fixed bed column: dynamic mathematical modeling, GWO optimization, and biosorption experiments
Authors: Keshtkar Ali Reza , Amiri Mehdi , Moosavian Mohammad Ali
Published: November 2024

In this study, the multi component biosorption by Cystoseria indica brown alga in a fixed bed column from a solution containing U(VI), Cu(II), and Ni(ΙΙ) was investigated at various flow rates (1.15, 2.3, and 4.6 mL/min) and bed heights (3.5, 5.25, and 7 cm). The results demonstrated that the biosorbent's affinity to metal ions follows the order of U(VI) > Cu(II) > Ni(ΙΙ). Furthermore, concentration overshoot was detected in all biosorption assays for Ni(II) ions. The best flow rate of 2.3 mL/min was selected, resulting in the maximum biosorption capacities of 133.2, 52.3, and 23.9 mg/g for U(VI), Cu(IΙ), and Ni(ΙΙ), respectively. The results also demonstrated that the biosorption capacity increases for U(VI) ion and decreases for Cu(IΙ) and Ni(Ι) ions as the bed height increases. The multi component biosorption process was modeled using the internal mass transfer model and the Gray Wolf Optimizer (GWO) algorithm. The modling results revealed that the mass transfer mechanism occurs through diffusion within the biosorbent's pores.
Antimicrobial Effect of Bio-Ceramic Sealer in Necrotic Primary molars
Authors: Aly Esraa Hamed
Published: November 2024

Background
Pulpally infected primary teeth should be retained until exfoliation whenever possible, as deciduous teeth are ideal space maintainers. Removing bacteria from the root canal is essential for a successful root canal treatment. Therefore, the present study compared the antimicrobial effects of resorbable Bio-ceramic sealer with pastes of calcium hydroxide and iodoform mixed with zinc oxide and zinc oxide eugenol when used as intracanal medication on microorganisms of necrotic primary molars. The materials used in the study were: Paste A: Non-setting calcium hydroxide paste mixed with iodoform, Paste B: Zinc oxide eugenol, Paste C: Metapex with zinc oxide, Paste D: Metapex with zinc oxide eugenol, Paste E: Bio-C Pulpecto. Polymicrobial growth inhibition zone diameters around each of the five pastes were measured in mm to determine antimicrobial activity. The collected data was presented, tabulated, and statistically analyzed.
Results
Comparison among all groups demonstrated a significant difference between them (P < 0.0001) using the Kruskal-Wallis test. Furthermore, Dunn's multiple comparisons test revealed that: Paste C (0.93 ± 3.55) and Paste A (2.13 ± 2.67) exhibited significantly the lowest antibacterial activity with no significant difference between them. Subsequently, Paste D (5.2 ± 5.89) and Paste E (8.73 ± 1.36) displayed no significant difference between them. In contrast, Paste B (11.8 ± 1.58) demonstrated significantly the highest antibacterial activity.
Conclusions
Bioceramic sealers could be utilized as filling material for root canal in primary molars because of their antimicrobial and biological properties.
Community characteristics of benthic macroinvertebrates and ecosystem health assessment in ten Reservoirs of Henan Province, China
Authors: Zhao Jiannan , Gao Yunni , Zhang Jingxiao , Li Yongli , Gao Xiaofei , Yuan Huatao , Dong Jing , Li Xuejun
Published: November 2024

The eco-health assessment of regional reservoirs is important for ensuring the sustainable utilization of water resources and maintenance of water security, particularly in regions facing water scarcity. The present study aimed to construct a B-IBI based on the community characteristics of macrobenthos in ten large and medium-sized reservoirs across four major river basins in Henan Province, China. The results revealed the identification of 92 species belonging to 3 phyla, 6 classes, 18 orders, 47 families. The B-IBI was established based on five key metrics, namely the number of crustacean and mollusca taxa (M6), Intolerant % (M15), the BI index (M17), the BMWP index (M18), and the Shannon-Wiener index (M27). The total B-IBI score of the 44 sites in ten reservoirs ranged from 0.35 to 3.99. The assessment results indicated two reservoirs (QTH and HKC in the Yellow River basin) were classified as poor, whereas only one reservoir (QP in Huai River basin) was classified as excellent. The B-IBI index demonstrates a strong capability to distinguish the impaired sites from the reference sites, thereby indicating its suitability for assessing regional reservoirs in Henan Province.
A novel mitochondrial gene rearrangement delineate Magallana gryphoides species from other Magallana crassostreine oysters
Authors: Santhi Vineetha Vijayan , Chaube Shail K. , Antony Mano Mohan
Published: November 2024

Background
The Magallana genus represents commercially most important oysters globally. Due to phenotypic plasticity, members of Magallana genus are often confused morphologically and limit their taxonomical as well as conservation efforts. On this background, the complete mitochondrial genome of Magallana gryphoides was sequenced.
Methods and results
The complete mitochondrial genome (mitogenome) of Magallana gryphoides was sequenced for the first time employing the next-generation sequencing. Results suggest that the mitogenome of M. gryphoides is of 18313 bp in length (accession number OR177662). The mitogenome contain 13 protein-coding genes including ATP8 and 26 tRNAs with 3 new suppressor tRNAs as well as 2 rRNAs. Further, presence of two copies of rrnS and rrnL lack 3’ half are unique, unusual and absent in other Crassostreinae oysters. The positions of COX1, trnG, trnV, and ND2 genes in the mitogenome of M. gryphoides are significantly altered. The phylogenetic analysis indicates a close relationship of M. gryphoides with the native Indian oyster Magallana bilineata and other Asian oyster Magallana iredalei.
Conclusion
Taken together, these data provide a new insight into phylogenetic relationship of oysters and may help to place the correct taxonomical status of M. gryphoides, M. bilineata and M. iredalei that that are often confused in the Ostreidae taxonomy due to phenotypic plasticity.
Poor efficacy of the combination of clarithromycin, amikacin, and cefoxitin against Mycobacterium abscessus in the hollow fiber infection model
Authors: Vignaud Etienne , Goutelle Sylvain , Genestet Charlotte , Guitton Jérôme , Cohen Sabine , Bourg Chloé , Durand Aurore , Lebouteiller Laura , Bernard Albin , Richet Caroline , Dumitrescu Oana , Hodille Elisabeth
Published: November 2024

Background
Mycobacterium abscessus (MABS) causes difficult-to-treat pulmonary and extra-pulmonary infections. A combination therapy comprising amikacin, cefoxitin, and a macrolide agent is recommended, but its antimicrobial activity and clinical efficacy is uncertain. Inducible resistance to macrolides (macrolides-iR) has been associated with poor clinical response in pulmonary infections, whilst for extra-pulmonary infections data are scarce.
Objectives
Herein, the aim was to evaluate the effect of the amikacin, cefoxitin, and clarithromycin combination against macrolides-iR MABS in a hollow-fiber infection model.
Methods
The hollow-fiber system was inoculated with M. abscessus subsp. abscessus type strain ATCC 19977 and treated during 10 days with the antibiotics combination. Two level of macrolide concentrations were evaluated mimicking the pharmacokinetics profiles of free (i.e. unbound) drug in blood and lung.
Results
Using blood concentrations, the combination failed to prevent bacterial growth. Using lung concentrations, the combination had a limited but significant effect on bacterial growth from day 2 to day 10. Moreover, increasing clarithromycin concentrations stabilized the amikacin-tolerance level: amikacin minimal inhibitory concentration of amikacin-tolerant strains increased over time using blood concentrations while it remained stable using lung concentrations.
Conclusions
Our finding confirms the low activity of the amikacin, cefoxitin, and clarithromycin combination against macrolide-iR MABS infection, and suggest the influence of clarithromycin concentrations on response. The low concentration of clarithromycin in blood may hamper efficacy for the treatment of extra-pulmonary MABS infection. Consequently, it should not be considered as an active molecule in the chosen antibiotic combination, as recently recommended for pulmonary infections.
Experimental Study on Optimization of Pipe Jacking Mud Mixture Ratio Based on MICP Technology
Authors: Cui Guangqin , Zhang Hang , Ma Chenguang , Zhang Xiaoli , Shao Hong
Published: November 2024

During the pipe jacking construction process, the back-soil effect often occurs due to various factors such as excavation unloading, continuous disturbance from subsequent pipe sections, and accumulated soil adhesion. Pipe jacking slurry, as the primary measure for friction reduction and strata support, is crucial to ensuring the safe execution of pipe jacking construction. This paper introduces the microbial-induced calcium carbonate precipitation (MICP) technology into the field of pipe jacking slurry to improve its friction-reducing effect and enhance soil stability. To achieve this, the optimal mix ratio of MICP-slurry was selected through the uniform design method. Model tests were conducted to investigate the friction-reducing effect of MICP-slurry, and the mechanism of MICP-slurry in strengthening strata stability was analyzed through mechanical tests and scanning electron microscope (SEM) tests. The results show that the optimal mix ratio of MICP-slurry is bentonite: CMC: soda ash: PAM: xanthan gum = 12%: 0.31%: 0.36%: 0.25%: 0.54%. MICP-slurry reduces the friction coefficient between the test block and sand by 42.2%. Compared to the untreated sample S-1, the cohesion and internal friction angle of the MICP-treated sample S-2 increase by 38.12% and 14.01%, respectively. SEM analysis reveals that the calcium carbonate crystals induced by MICP-slurry in the soil fill the pores, enhance the bite force between soil particles, and improve the mechanical properties of the soil.
Morphological Instability of Kirkendall Voids: A Phase-Field Study
Authors: khorasgani Ahmadreza Riyahi , Steinbach Ingo , Camin Bettina , Kundin Julia
Published: November 2024

The present research explores theoretical and computational aspects of the morphological instability ofKirkendall voids induced by a directed flux of vacancies. A quantitative phase-field model is coupled witha multi-component diffusion model and CALPHAD-type thermodynamic and kinetic databases to obtaina meso-scale description of Kirkendall void morphologies under isothermal annealing. The material underinvestigation is a diffusion couple consisting of a multi-phase multi-component single-crystal Ni-basedsuperalloy on one side and pure Ni on the other side. The flux of the fast diffuser Al towards the pureNi causes a strong flux of vacancies in the opposite direction. This directed flux of vacancies leads tomorphologically instable growth of voids, known as Mullins–Sekerka instability. Phase-field simulationsare performed in two (2D) and three dimensions (3D) to understand these instabilities, and the resultsare compared with experimental observations obtained by synchrotron X-ray tomography. Finally, thesimulation results are analyzed with respect to the Mullins–Sekerka linear stability criterion.
Improving the thermal and hydrophobic properties of bamboo biocomposite as sustainable acoustic absorbers
Authors: irvani hassan , mahabadi hassan asilian , khavanin ali , Variani ali Safari
Published: November 2024

The purpose of this research is to investigate the potential of chemical modification to improve the hydrophobic properties and thermal stability of bamboo fibers and to evaluate the sound absorption performance of raw and modified fibers. To achieve this goal, bamboo fibers were modified using stearic acid coatings and aluminum hydroxide nanoparticles. The results showed that the modification of fibers with stearic acid (STA) can improve the contact angle and hydrophobicity of bamboo fibers, so that for modified fibers with a concentration of 0.3 M, the contact angle was 143.8 degrees, placing the fibers in the superhydrophobic range.Additionally, the thermogravimetric results showed that aluminum hydroxide (ATH) loading increased the coal residue at 600 °C by 12%, indicating an improvement in hydrophobic properties and thermal stability. The average sound absorption for composites based on raw fiber and modified with STA and ATH in the range of 630-6300 Hz was 0.51 and 0.472, respectively. This shows that chemical modification has reduced the sound absorption of the composite to a small extent. On the other hand, the average sound absorption coefficient for frequencies above 630 Hz was 0.84 and 0.79, respectively, indicating that the sound absorption composite has good absorption. Overall, these observations provide strong evidence that chemical modification of natural fibers can lead to the development of more stable and safer porous acoustic absorbers.
Overview of Menstrual Hygiene Management and Other Risk Factors concerning Bacterial Vaginosis
Authors: Zahra Fatima Tuz , Sultana Ummay Farihin , Quinn-Walker Natalie
Published: November 2024

Background
Bangladesh is a developing nation where, for a variety of reasons, adequate female reproductive health is not maintained. Bangladeshi women are plagued by a variety of nations and a variety assortment of reproductive tract disorders plague a variety of reproductive tract disorders plagues Bangladeshi women.
Method
The study employed a one-year cross-sectional design, combining a quantitative participatory approach. It was conducted at two healthcare facilities, AK Memorial Hospital in Maona, Gazipur, and Lubana General Hospital & Uttara Cardiac Center, involving 244 voluntary participants. Among them, 153 women meeting specific inclusion criteria (reproductive and menopausal age, BV diagnosis) were selected, while those with a negative BV diagnosis were excluded. Data collection primarily occurred in the outpatient department, with participants covering a wide age range and diverse socioeconomic and educational backgrounds.
Result
65.4% of the BV-positive women were married. It was observed that married women were more susceptible to BV than unmarried women. The study also revealed that poor menstrual hygiene was the leading cause of BV. Around 81% of women had poor menstrual hygiene, whereas 68% of them changed their absorbents than once or twice a day. However, of women (43%) utilized cloudified things, and operated absorbents.
Microbial diversity of high-elevated fumarole fields - low-biomass communities on the boundary between ice and fire
Authors: Shevchenko Alla , Ursalov Gleb , Eromasova Natalya , Shelyakin Pavel , Gelfand Mikhail , Tutukina Maria , Abramov Andrey , Vishnivetskaya Tatiana , Rivkina Elizaveta
Published: November 2024

Fumarole fields on active volcanoes are habitats that host unique microbial ecosystems, however, DNA extraction from them for further analysis is rather challenging. In this study, we compared two different ways of sample homogenization for DNA extraction to further profile the microbial communities of active fumarolic fields from Elbrus and Ushkovsky volcanoes and the frozen fumarole deposits of Fujiyama. Vertical homogeniser gave significantly higher DNA concentrations for the Elbrus samples, and more archaeal amplicon sequence variants (ASVs) for Elbrus and Ushkovsky samples compared to the horizontal one. This suggests that vertical homogeniser might be preferable for DNA extraction from sandy and rocky soils. Independent of the homogenizer type, the dominant phyla for Elbrus were Acidobacteriota and Pseudomonadota, and Crenarchaeota for Ushkovsky. The bacterial community of Fuji was less diverse, with Actinomycetota, Pseudomonadota and Bacillota being the dominant phyla. Thus, the studied fumaroles showed distinct microbial profiles, revealing unique adaptations to their respective extreme environments. Within the fungal community, Ascomycota, Basidiomycota and Chytridiomycota were the most dominant phyla for all three volcanoes, but their abundance varied. This study offers the first comprehensive analysis of microbial and fungal communities of active and frozen fumarolic fields, and demonstrates that the choice of methodology can significantly influence the understanding of microbial diversity in extreme environments.
Characteristics, outcomes and treatment patterns in acute myeloid leukemia patients 60 Years or older in Colombia: a RENEHOC-PETHEMA study
Authors: Sossa-Melo Claudia , Polo Virginia Abello , Salazar Luis A. , Peña Angela M. , González María Lucrecia Luna , Cuervo-Lozada Diana Marcela , Quintero-Vega Guillermo E. , Daza Jorge , Omaña-Orduz Olga Paola , Mantilla William , Perdomo Iván , Galvez Kenny , Correa Laura María Díaz , Guerrero-Burbano Paola Andrea , Herrera Juan Manuel , Idrobo Henry , Gaviria Lina M. , Correa Mario Ernesto Correa , Lobatón José , Bermúdez Carlos Daniel , Morales Julian Eduardo Pedraza , Casas Juan Carlos Serrano , Jaramillo Francisco , Gomez Rigoberto , Rosales Carmen , Solano María Helena , Varón Carlos , Veiga Rebeca Rodríguez , Martínez-Cuadrón David , Montesinos Pau
Published: November 2024

There is a limited information available on the clinical characteristics, treatment patterns and outcomes on older patients diagnosed with Acute Myeloid Leukemia (AML) in Latin-America. This multicenter retrospective study analyzed 269 patients over 60 years of age diagnosed with AML in Colombia, using data from RENEHOC-PETHEMA registry, from 2009 to 2023. The median age at diagnosis was 70 years (Range: 60–98), 55% were men, 61% had an ECOG < 2, and 75.5% had de novo AML. FLT3-ITD or NPM1 mutations were performed in 23.4% and 15.6% of patients, and detected in 14.3% and 16.7% of cases, respectively. Treatment included intensive chemotherapy (IC) (36.8%), non-intensive chemotherapy (non-IC) low dose cytarabine based chemotherapy (12.6%), hypomethylating agents (HMA, with/without venetoclax) (35.3%), and supportive care (15.2%). Median overall survival (OS) was 8.2 months (10.6 months after IC, 8.8 after non-IC, 8.9 months after HMA, 8.2 months after HMA plus venetoclax, and 1.9 months with supportive care). OS was 35.2% at 1 year and 5.6% at 5 years (13.7% treated with IC). Only 1.5% of patients were transplanted in frontline. Leukemia-free survival (LFS) was 17.6 month, with 67.6% survival at 1 year and 27.3% at 5 years. Among patients treated with IC, the 5-year LFS was 41.3%, and 0% for non-IC. A shift from IC to HMA plus venetoclax regimens was observed after venetoclax marketing. This study provides new insights into the management of patients in Colombia, highlighting the need for a highly individualized approach in treating AML in elderly patients.
Calm me down, or I'll leave: anxiety and institutional support among Ukrainian academic staff during wartime
Authors: Suchikova Yana , Kolomiiets Uliana , Popova Anastasia , Lopatina Hanna , Tsybuliak Natalia
Published: November 2024

Background
Mental health conditions among academic staff are a growing global concern, driven by factors such as heavy workloads, job insecurity, and a lack of institutional support. Anxiety, one of the most common mental health disorders, is particularly widespread in academia, affecting cognitive function and productivity. In Ukraine, the ongoing war has intensified these challenges, creating unprecedented working conditions for academic staff. This study investigates the prevalence of anxiety among Ukrainian academics during wartime. It explores the impact on their desire to change careers and the perceived effectiveness of institutional mental health support.
Methods
This cross-sectional study surveyed 429 academic staff from Ukrainian higher education institutions between December 2023 and February 2024. Anxiety levels were measured using the Generalized Anxiety Disorder (GAD-7) scale, while additional questions assessed the desire to change professions and the perceived effectiveness of institutional mental health support. The data were analyzed using chi-squared tests, correlation analysis, and Ordinary Least Squares (OLS) regression.
Results
The findings revealed that 37.1% of respondents experienced moderate anxiety, while 20.3% reported severe anxiety. There was a significant relationship between higher anxiety levels and an increased likelihood of considering a career change. While 90.2% of respondents viewed institutional mental health support as important, many felt that the existing programs were ineffective. Greater accessibility to psychological support services was associated with lower anxiety levels.
Conclusions
The high prevalence of anxiety among Ukrainian academic staff during the war highlights the urgent need for targeted mental health interventions. Institutional support – such as accessible psychological services- is important in the ongoing war conditions. These findings underscore the broader implications of anxiety for the sustainability of Ukraine's educational and scientific sector during wartime and emphasize the need for comprehensive mental health programs tailored to the unique challenges faced by academics in wartime.
Seasonal and anthropogenic factors-driven distribution of nutrient salts and microbial communities in the Pearl River Delta
Authors: Huang Zhiwei , Wang Jie , Li Weijie , Yang Aixiu , Mao Yupeng , Gu Yangliang , Zeng Luping , Du Hongwei , Shi Lei , Fang Huaiyang
Published: November 2024

The transformations of Iron (Fe), Phosphorus (P) and sulfide (S) has been previously investigated, but quantifying the effects of seasonal and anthropogenic influences on nutrient transformations and microbial community distributions is a major issue that requires urgent attentionin areas with serious anthropogenic disturbance. We used the diffusive gradients in thin-films (DGT) technique and 16S rRNA gene sequencing to determine spatial heterogeneity in nutrient distribution and microbial community structure in the overlying water and sediment in the Pearl River Delta (PRD), the sampling campaigns were conducted in summer and winter, respectively. The results show that the nutrient salts exhibited greater differences in time than in space, and the higher water pollution levels in winter than in summer. Sediment was identified as a potential source, especially in summer, due to the loose structure of sediment particles and diffusion fluxes of P, Fe, and NH4+-N caused by rainfall and high temperature. Microbial diversity and abundance were also affected by seasonal and anthropogenic factors, with higher richness and diversity of microbial communities in summer than in winter, and high salinity and nutrient salts concentration had a significant inhibitory effect on microorganisms. Mantel test revealed that the spatiotemporal distribution patterns of dominant bacteria were closely related to the TOC and DO levels and play an important role in the P, Fe, S, and N cycle. These observations are important for understanding nutrient salts transformation and diffusion in the Pearl River Delta.
Comparing Methods for Forecasting Time Series with Multiple Observations per Period using Singular Spectrum Analysis
Authors: Mahmoudvand Rahim
Published: November 2024

In practice, a univariate time series typically represents the value of a quantitative variable in successive order over a period of time. However, in certain fields like hydrology, multiple observations may be available for each time point. Commonly, functions such as the average, maximum, and minimum are used to summarize these observations into a univariate time series, potentially losing valuable information.
This paper proposes an alternative approach by constructing a time series of distributions from the observations. We explore two methods: (1) a non-parametric approach using boxplots to create a time series of boxplots, and (2) a parametric approach using a parametric distribution, where the parameters of the distribution form the time series. These methods allow for the application of multivariate time series analysis techniques to better capture the underlying information.
To demonstrate the practical application of these approaches, we employ singular spectrum analysis to model real climate change data from Europe.