Search Results (429)

Mineral-scale deposition remains a persistent flow-assurance and asset-integrity constraint in oil and gas production. Calcium carbonate, calcium sulfate, barium sulfate, iron sulfide, and mixed inorganic scale deposits reduce tubing internal diameter. They also impair near-wellbore permeability, block safety-critical valves, reduce heat-transfer efficiency, and intensify under-deposit corrosion. Conventional management relies on prediction, chemical inhibition, squeeze treatments, acid dissolution, chelation, mechanical scraping, milling, jetting, and operational water management. These methods are indispensable, but each has a restricted operating envelope. Key limitations include mineral selectivity, corrosion risk, environmental discharge, intervention cost, debris generation, and poor effectiveness against chemically resistant sulfate scales, particularly BaSO₄. Ultrasound has therefore attracted interest as a non-chemical technology. Acoustic cavitation, microstreaming, pressure oscillation, mechanical vibration, and micro jetting may suppress nucleation, disturb boundary layers, weaken adhesion, and fragment brittle deposits. This review critically evaluates ultrasound-assisted scale prevention and removal, with emphasis on production tubing and oilfield relevance. Existing studies show credible mechanistic plausibility and promising laboratory performance for CaCO₃, CaSO₄/gypsum, KCl, NaCl, and membrane or heat-transfer fouling systems. It also compares performance metrics, field cases, and technology-readiness barriers. The evidence is less mature for long steel tubulars operating under high-pressure, high-temperature, multiphase production conditions. Current evidence positions ultrasound at technology-readiness level (TRL) 3–4 for CaCO₃ and CaSO₄ systems, where laboratory and bench-scale validation is established, and at TRL 2–3 for BaSO₄, where mechanistic plausibility exists but controlled experimental validation remains absent. The technology is not yet at the pilot–production transition for downhole tubing applications, but it is approaching that threshold for surface process equipment. Its most credible near-term role is as an intensifier paired with low-dose chemical inhibitors, where acoustic boundary-layer disruption can reduce the minimum inhibitory concentration threshold of inhibitors, and with mild chelating agents for early-stage BaSO₄ management, where ultrasound-enhanced mass transfer may accelerate chelant penetration into deposit microstructure. Advancing ultrasound from its current TRL toward field qualification requires targeted BaSO₄ scale validation in steel tubing systems, acoustic field mapping under HPHT multiphase conditions, mass-removal metrics, and a structured pilot programme. Full article

Background: This study evaluated the antibacterial, antibiofilm, and biofilm eradication activities of biogenically synthesized silver nanoparticles (AgNPs-L, AgNPs-R) mediated by extracts of Lavandula angustifolia and Salvia rosmarinus, individually and in combination with their respective essential oils (EOs) and ampicillin (AMP), against a multidrug-resistant biofilm-forming Staphylococcus equorum strain. Physicochemical characterization confirmed the successful biosynthesis of spherical AgNPs-L (10–25 nm) and AgNPs-R (5–15 nm). Individual treatments exhibited distinct antibacterial activity, with MIC values of 25 µg/mL for AgNPs-L, 12.5 µg/mL for AgNPs-R, and 0.1% (v/v) for both EOs; however, they showed no ability to eradicate preformed biofilms. Dual AgNPs/EO combinations at subinhibitory concentrations demonstrated borderline additive effects (FICI = 0.501) and significantly potentiated antibacterial and antibiofilm activity compared with individual treatments. Triple AgNPs/EO/AMP combinations exhibited the most pronounced biological effects, with predominantly additive interactions depending on AMP concentration. Lavender-based triple combinations achieved up to 63.9% inhibition of planktonic growth, 78.1% prevention of biofilm formation, and 37.3% eradication of mature biofilms, whereas rosemary-based combinations resulted in 57.1%, 69.4%, and 42.7% inhibition, respectively. These findings highlight the potential of multi-component systems integrating biogenic nanomaterials, phytochemicals, and conventional antibiotics as a promising strategy to enhance antimicrobial efficacy against persistent biofilm-forming non-aureus staphylococci. Full article

This paper presents a novel control strategy for hybrid rotor–bearing systems integrating hydrodynamic journal bearings with active magnetic bearings (AMBs) to address the persistent challenge of nonlinear vibrations in high-speed rotating machinery. The study introduces the application of a state-dependent parameter proportional-integral-plus (SDP-PIP) controller designed within a non-minimal state-space framework, offering a significant advancement over conventional control approaches. A four-degree-of-freedom model incorporating short-bearing approximation for hydrodynamic forces and nonlinear electromagnetic force characterization is developed to capture the complex system dynamics. The controller performance is evaluated through numerical simulations over a range of rotational speeds from 130 to 500 rad/s, together with sensitivity analyses under parameter variations and comparisons with a conventional PID controller. The results show that the proposed controller effectively suppresses nonlinear vibrations and stabilizes oil-whirl and oil-whip instabilities over the investigated operating conditions. In comparison with the PID controller, the SDP-PIP controller provides improved vibration attenuation and maintains stable journal motion with lower oscillation amplitudes, particularly near unstable operating regimes. These findings demonstrate the potential of the SDP-PIP control strategy for enhancing the dynamic performance and operational stability of hybrid journal bearing systems. Full article

Conventional milling separates the wheat germ and bran from the endosperm to obtain white semolina; although these fractions retain recognized nutritional value, a substantial share is still diverted to feed and other comparatively low-value uses rather than into food for direct human consumption. In this Perspective we ask whether recovering a larger share of these fractions—through a controlled industrial sequence of selection, de-oiling and physical fractionation—can be turned into a functional ingredient, commercialized as “altograno”, that improves the health profile of an ordinary pasta without compromising palatability or shelf stability. To address this objective we proceed in a fixed analytical sequence: what the product is and what a habitual portion actually delivers; a nutrient-by-nutrient appraisal graded by level of evidence; the biological mechanisms from the intestinal lumen to the hepatocyte; and only then the clinical endpoint. The evidence base integrates two complementary studies of one product line from the Casillo Next Gen Food chain: a full nutritional, microbiological and gastrointestinal characterization in healthy volunteers, and a double-blind randomized controlled trial with in vitro hepatocyte evidence in patients with metabolic syndrome (MetS), with and without major psychiatric disorders (MPDs). Because the clinically tested product (67% semolina, 27% de-oiled wheat germ, 6% microencapsulated wheat-germ oil) shares the same nominal formulation as the germ-plus-oil pasta (EP3) of the characterization study—although the measured nutritional values reported in the two papers differ, so the two products cannot be regarded as analytically identical—the two datasets can be read together, with due caution, across kernel processing, composition, hepatocellular mechanism and gut-microbiota switching to a clinical endpoint: a ~13.5% fall in non-HDL cholesterol, roughly 2.5-fold greater than with conventional pasta, with no statistically significant interaction with the polygenic risk score detected, and paralleled by normalized hepatocyte lipid loading in vitro. We argue that de-oiling is best understood as a deliberate technological optimization—trading a portion of labile lipids for stability and standardization while recovering the bioactive oil for protected re-addition—and that this evidence, while still preliminary on several fronts, supports dedicated, mechanistically instrumented clinical development of upcycled-kernel staples rather than establishing these products as proven therapeutic tools. Full article

Bioenergy development from biomass resources requires multi-criteria decision-making (MCDM) methods to rank and select suitable feedstock alternatives. Conventional TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is a widely used MCDM method that assists in selecting alternatives based on their relative closeness to the ideal solution. Unlike the existing TOPSIS, which provides only an overall ranking of alternatives, this study proposes a framework that integrates TOPSIS ranking with threshold-based performance classification and mapping to enable a more comprehensive assessment and support decision-making, thereby improving the interpretability of complex multi-criteria decision problems. The proposed framework was applied to evaluate, rank, and select eight potential biomass feedstocks in Ethiopia by integrating feedstock availability, technological readiness, and economic criteria. The ranking results indicate that molasses is the most suitable, followed by non-edible oil crops (castor seed, Ethiopian mustard, and Jatropha curcas) and lignocellulosic residues (cereal, cane, and coffee residues), while water hyacinth ranked lowest. The results were mapped into a strategic implementation timeline, with molasses prioritized for the short term, non-edible oil crops for the medium term, and lignocellulosic residues for the long term in Ethiopia’s bioenergy development. The study further supports decision-makers in strategic energy planning and facilitates bioenergy development. Full article

Accurate prediction of input–output relationships in natural gas exploration is essential for improving exploration efficiency and optimizing investment allocation. However, this task is severely hindered by data sparsity and strong nonlinear characteristics inherent in oil and gas exploration systems, rendering conventional statistical methods and single machine learning models ineffective. This study develops a novel integrated framework combining data augmentation, nonlinear feature engineering, and ensemble learning to achieve accurate and interpretable prediction of exploration input–output matching under limited data constraints. Taking four core exploration indicators—including the number of exploration wells, total drilling depth, reserve abundance, and proven reserves—as input variables, adaptive prediction models were constructed for seven typical hydrocarbon basin exploration systems. To ensure comprehensive algorithmic exploration, nine advanced algorithms, including mainstream ensemble methods (RandomForest), few-shot neural networks (FewShot_NN), and kernel-based regressions, were systematically benchmarked. Furthermore, SHapley Additive exPlanations (SHAPs) was adopted to enhance model interpretability, and non-parametric Wilcoxon signed-rank tests were introduced to rigorously validate statistical significance. The results demonstrate that the optimal predictive pathway varies across different geological systems. Specifically, RandomForest and GBDT exhibit superior performance in systems with moderate heterogeneity (e.g., Jialingjiang and Changxing–Feixianguan Formations), whereas FewShot_NN and Kernel Ridge achieve the highest accuracy under extreme data sparsity and volatility (e.g., Xujiahe Formation and Lower Permian). The established framework yields a coefficient of determination (${\mathrm{R}}^2$) greater than 0.96 for the majority of study cases, with overall absolute percentage errors heavily minimized. SHAP analysis further verifies that drilling depth and reserve abundance are the dominant controlling factors. This data-driven framework provides a robust and interpretable technical tool for the intelligent management of energy resources. Full article

Vineyards and wineries generate seasonal, wet and compositionally variable side-streams whose safe use is constrained by rapid spoilage, contaminants, fragmented regulation and scale. This EU-focused structured narrative review synthesised 90 scientific and official sources and proposes an integrated decision framework rather than another catalogue of valorisation routes. The framework applies a non-compensatory sequence: characterise and stabilise the batch, test route-specific hazards, assign evidence-level and technology readiness, verify legal eligibility, define a safe fallback, and only then compare material flows, environmental burdens and risk-adjusted economics. Current implementation evidence is strongest for controlled composting, conventional wastewater treatment, anaerobic digestion and selected grape-seed oil, polyphenol and heat-integrated biochar operations; clinical, plant-protection and several novel-extract claims remain product- and context-specific. Illustrative ENPV cases show that high-value extraction can offer greater upside but lower robustness than compost/biochar when moisture, transport, rejection and price uncertainty are included. The framework provides an auditable basis for pilot design, regional cooperation and data collection, while explicitly requiring industrial and multi-season validation before investment or product approval. Full article

The six Gulf Cooperation Council (GCC) economies share a dollar peg and heavy hydrocarbon dependence, yet their inflation paths diverge sharply over time. Because the peg imports US monetary policy and forecloses nominal adjustment, GCC inflation plausibly reflects imported price pressure, oil revenue cycles working through fiscal capacity, and domestic real activity. We quantify the associations between these channels and headline inflation in country fixed-effects regressions estimated on an annual panel of the six economies over 1991–2023. Trading partner inflation is associated with domestic inflation with a contemporaneous, within-year coefficient of about 0.46—an association, not an identified causal effect, which strengthens to about 0.65 under country-specific, backward-looking trade weights—while an oil-orthogonal proxy for discretionary fiscal stance is negatively associated with inflation. Both results are stable in leave-one-country-out checks and are robust to lagged inflation; the external association holds under wild cluster bootstrap inference appropriate to six clusters, under Driscoll–Kraay inference robust to cross-sectional dependence, and in first-difference and distributed lag specifications, while two-stage bootstraps propagating the fiscal proxy’s construction uncertainty place the fiscal association at the margin of conventional significance. After the 2014 oil price collapse, the external association weakens and domestic activity and financial volatility carry the largest standardized weights. The cyclically adjusted fiscal residual—our methodological contribution—tracks published IMF non-oil balances more closely than the raw balance does and requires only an overall balance and an oil price. Full article

The non-Gaussian height distribution and power spectral density (PSD) characteristics of rough surfaces have significant effects on the real contact area, local pressure distribution, oil-film formation, and friction and wear behavior of lubricated contact interfaces in mechanical components. Conventional methods for generating non-Gaussian rough surfaces commonly rely on iterative correction under explicit statistical constraints, which limits their computational efficiency in large-scale sample generation. To address this issue, this study proposes a PSD-amplitude-constrained phase conditional variational autoencoder (phase C-VAE) for generating non-Gaussian rough surfaces. Unlike conventional constructive methods that repeatedly correct surface samples under explicit statistical constraints, the proposed method learns the conditional distribution of the Fourier phase, while the spectral amplitude used for reconstruction is directly determined from the prescribed PSD. By taking the target skewness, kurtosis, and PSD as conditional inputs, the proposed method achieves joint control of higher-order statistical characteristics and spectral characteristics within a unified generative framework. Under target conditions derived from measured surfaces, the generated non-Gaussian rough surface samples achieved mean absolute relative errors of $0.056\%$ and $0.044\%$ for skewness and kurtosis, respectively, with a generation time of $24.62\mathrm{s}$. These results indicate that the proposed method can effectively match the target skewness and kurtosis while maintaining good consistency between the generated surfaces and the target PSD. The proposed method alleviates the efficiency limitation of conventional constructive methods in the large-scale generation of non-Gaussian rough surface samples and provides an effective machine-learning-based generative approach for rapid batch modeling of rough surfaces in lubrication, friction, and contact analyses. Full article

Power transformers are critical components of electric power infrastructure, and their liquid insulation systems are decisive for operational safety, reliability, and longevity. Conventional insulating fluids, particularly mineral oil, face increasing scrutiny due to low biodegradability, poor thermal performance, and non-renewable origin. Insulating oil-based nanofluids, engineered by dispersing nanoparticles (1–100 nm) into base oils, have emerged as transformative candidates for next-generation transformer liquid insulation. This review provides a comprehensive and critically integrated analysis of insulating oil-based nanofluids, systematically covering historical development, synthesis methodologies, colloidal stabilization strategies, and multi-technique characterization approaches. Dielectric performance metrics, including AC, DC, lightning-impulse breakdown voltages, partial-discharge inception voltage, and dielectric loss, are critically reviewed alongside thermal-conductivity enhancements and the thermo-viscous trade-off. Experimental evidence demonstrates that optimally formulated nanofluids enhance AC breakdown voltage by 20–60%, improve thermal conductivity by 10–40%, and significantly elevate partial discharge inception voltage depending upon various factors such as doping concentration, dispersion quality, moisture content, particle size/morphology, nanoparticle-oil system compatibility, etc. However, long-term colloidal instability, nanoparticle migration, compatibility with ageing products, and absence of standardized testing protocols continue to impede industrial deployment. This review identifies key research gaps and outlines a roadmap toward reliable, sustainable, and commercially viable insulating nanofluids for power transformer applications. Full article

Natural fractures strongly influence fluid flow, hydraulic-fracturing performance, and production heterogeneity in tight oil reservoirs. Their identification from conventional logs remains challenging because image-log and core coverage is limited, fracture-related logging responses are non-unique, and discrete fracture interpretations are difficult to align with regularly sampled logging sequences. This study used conventional logging data and electrical image-log interpretations from 17 wells in the Xifeng Oilfield, Ordos Basin, together with core observations from selected intervals, to develop a fracture response mapping and triangular topology-optimized bidirectional long short-term memory model (FRM-BiLSTM-TTAO). After sliding-window construction and density-based undersampling, 1713 samples were retained and partitioned at the well level into 14 training wells and three independent test wells, yielding an approximate training-to-test sample ratio of 75:25. FRM extracts lithologic-background, local-abrupt-change, multiscale-fluctuation, and integrated fracture response features; BiLSTM captures bidirectional depth dependencies; and TTAO selects fracture response features and optimizes the network architecture and training parameters. On the test set, the model achieved a ROC-AUC of 0.9079, a recall of 0.8671, and an F1-score of 0.8464, outperforming CNN, MLP, ResNet1D, XGBoost, and the corresponding ablation models. The predicted high-probability intervals were generally consistent with image-log interpretations and core observations, indicating the feasibility of the proposed method for identifying fracture-prone intervals within the study area. Full article

Transdermal immunization represents a promising needle-free alternative to conventional vaccination. However, efficient antigen delivery and robust immune activation remain major challenges. In this study, a solid-in-oil (S/O) nanodispersion system comprising a dendritic cell-targeting peptide (HR8), ovalbumin (OVA), and an adjuvant was developed for transdermal immunization. The HR8 peptide along with OVA was successfully incorporated into an S/O nanodispersion with an optimal hydrodynamic diameter of particles (<200 nm) and exhibited stable physical properties for up to 90 days. In vitro and in vivo studies demonstrated enhanced antigen delivery with insignificant skin irritation in C57BL/6N mice. Moreover, in vivo transdermal immunization studies demonstrated that the addition of the HR8 peptide enhanced OVA-specific IgG responses (~1.5-fold). Notably, the HR8 peptide also promoted an approximately 2.5-fold increase in IgG2c levels, suggesting a shift toward a more T helper type 1-biased immune response, as reflected by an increased IgG2c/IgG1 ratio. Overall, these findings demonstrate that the incorporation of HR8 peptide into an S/O nanodispersion enables efficient transdermal antigen delivery and improved immunogenicity, highlighting its potential as a simple, non-invasive, and patient-friendly immunization strategy. Full article

With the gradual decline in conventional oil and gas production growth, unconventional natural gas has become a strategic alternative for hydrocarbon supply. Coal-rock gas (CRG) represents a deep unconventional gas resource with huge potential. Major exploration breakthroughs of CRG have been achieved in China, while systematic research targeting CRG as an independent gas reservoir is still lacking internationally. After effective commercial development, CRG serves as an important supplementary energy source for the domestic natural gas supply. Existing logging evaluation methods exhibit notable deficiencies, as porosity is typically estimated by fitting well logging data or proximate analysis data, resulting in limited accuracy. To address the lack of a dedicated logging volumetric model, ambiguous coal-matrix framework parameters, and substantial porosity calculation errors in deep CRG reservoirs, this study investigates the medium–high rank No. 8 coal seam of the Benxi Formation in the central-eastern Ordos Basin. From an oil and gas reservoir logging evaluation perspective, multi-scale experiments were conducted to systematically characterize the material composition and microscopic characteristics of the coal rock. From the perspective of oil and gas reservoir logging evaluation, a three-component logging volumetric model, consisting of a coal matrix, inorganic minerals, and pore fluids, was constructed, and the corresponding coal-matrix framework parameters were calibrated. The results demonstrate that coal rock is an organic–inorganic composite system, with organic macerals dominated by vitrinite (averaging 59.1%) and inertinite (27.1%). The sum of fixed carbon and volatiles exhibits strong correlations with total organic carbon (TOC) and micro-CT-derived coal-matrix content, yielding determination coefficients of 0.99 and 0.95, respectively, which validates the reliability of the multi-scale quantitative composition characterization. The coal-matrix framework parameters are non-constant: density ranges from 1.08 to 1.56 g·cm⁻³, acoustic slowness from 281 to 425 μs·m⁻¹, and compensated neutron from 39% to 79%. Borehole enlargement severely affects compensated density and neutron logs but has negligible interference with acoustic slowness. Notably, inertinite content shows a significant negative correlation with the acoustic-slowness framework response (R² = 0.80), indicating that structurally dense inertinite is a key intrinsic factor controlling the elastic response of the coal matrix. For porosity evaluation, a dual-variable framework model is proposed. The core novelty of this method is that it simultaneously incorporates variations in inorganic mineral content and differences in inertinite proportion within organic components as dynamic framework constraints, breaking through the limitation of the conventional constant-matrix assumption. The acoustic-slowness-based model achieves an average relative error of merely 7.1%, effectively resolving the large errors inherent in conventional fitting methods. The dedicated coal-rock logging evaluation system established in this study overcomes the limitations of fixed framework models, offers a scientific basis for fine-scale interpretation and resource assessment of deep CRG reservoirs, and provides a valuable reference for evaluating analogous reservoirs. Full article

Background: Multidrug-resistant and biofilm-forming staphylococci pose a threat to the sustainability of public health, livestock health and the ecosystem. Pathogenic potential with a worsening prognosis of therapy is mainly due to methicillin-resistant Staphylococcus aureus (MRSA) or multidrug-resistant Non-aureus staphylococci and mammaliicocci (NASM). Alternative approaches based on the use of biosynthesized nanoparticles or substances of natural origin appear to be promising solutions to the problem of ineffective suppression of infections caused by pathogenic microorganisms. Methods: The aim of this study was to monitor and compare the biological effects of silver nanoparticles prepared by green synthesis using Lavandula angustifolia and lavender essential oil. In particular, the antibacterial, antibiofilm, and biofilm-eradicating effects against biofilm-forming and multidrug-resistant reference strains and field isolates of staphylococci were monitored. Results: AgNPs inhibited the growth and formation of biofilms of S. aureus strains at a concentration of 0.05 μg/μL, but clinical NASM at 0.025 μg/μL. Sensitivity to Lavender essential oil (LEO) was the same against all tested staphylococcal strains, with an antibacterial MIC of 0.901 μg/μL. The essential oil also had an effect on biofilm formation against all tested strains, but its effect was recorded at a tenfold lower concentration (antibiofilm MIC = 0.0901 μg/μL). No eradication activity was recorded for either tested substance. Their activity against the formed biofilms was not recorded. Conclusions: The results demonstrate promising antibacterial and antibiofilm activities of biosynthesized AgNPs and lavender essential oil under in vitro conditions. These findings support further investigation of these materials as potential alternative antimicrobial approaches, particularly in combination with conventional antimicrobial agents. Full article

During the exploration and development of oil and gas fields, near-borehole formations exhibit considerable axial heterogeneity in elastic-wave velocity. However, existing borehole azimuthal acoustic imaging methods often ignore the effect of this heterogeneity on imaging performance and thus cannot accurately locate anomalous near-borehole bodies in formations with axially non-uniform wave velocities. Hence, a borehole azimuthal acoustic imaging method based on ray tracing and spatial scanning was developed to resolve this problem. Subsequently, the borehole azimuthal acoustic imaging responses of near-borehole caves in formations with axially uniform and axially non-uniform wave velocities were numerically simulated. Single-shot spatial-scanning imaging and multi-shot stack imaging were then implemented separately for PP (incident P-waves scattered as P-waves) scattered-echo data obtained from forward modelling. The results revealed that in formations with axially uniform velocities, the waveforms of scattered echoes from the near-borehole caves exhibited a typical parabolic variation as a function of depth. Conversely, in formations with axially non-uniform velocities, the waveforms exhibited an asymmetric, curved variation pattern with respect to depth. The conventional downhole three-dimensional spatial-scanning method could not accurately locate the near-borehole anomalies in formations with axially non-uniform wave velocities, producing large imaging errors for the simulated caves. By contrast, the proposed imaging method more precisely determined the radial distances, azimuths and depths of the simulated caves. The proposed method may broaden the application scope of acoustic remote detection logging in the exploration and development of complex heterogeneous reservoirs. Full article