Search Results (24,873)

Multilingual learning is key in natural language processing, but is challenged by the transfer–interference trade-off, where positive transfer benefits certain languages, while negative interference affects others. Prior methods, including linguistic-based and embedding-based language clustering, have attempted to address this; yet, they remain constrained by their static design and lack of task-specific feedback. In this study, we propose a novel computational strategy inspired by molecular design that constructs molecules with targeted properties. Languages are modeled as nodes in an undirected graph, with edges representing the transfer strength. This language molecule is optimized via Reinforcement Learning to adjust edge connections and weights to enhance positive transfer and minimize interference, where graph clustering is applied, and clusters are then evaluated on the Named Entity Recognition and POS tagging tasks using 25 languages from the WikiANN dataset and 12 typologically diverse languages from the UDPOS dataset. Compared to linguistic and embedding-based language clustering baselines, our method yields substantial improvements, especially for low-resource languages, with some showing over 35% increase in F1 score, while high-resource languages benefit moderately, confirming reduced transfer–interference trade-off. Our atom–language model offers a novel path for multilingual learning, inspired by molecular principles from physical sciences. Full article

Pitch-based foam carbon, a novel lightweight material, boasts excellent mechanical and thermoelectric properties, and tantalum film deposition on its surface can further enhance its performance. However, this deposition process often suffers from non-uniform deposition and suboptimal coating quality. To address these issues, this study systematically optimized the furnace structure by tuning pipe diameter, tilt angle, and porous media height. Numerical simulations of 216 models were conducted to evaluate the effects of these parameters on axial velocity, turbulence intensity (quantified by the vortex criterion Q > 1), and reactant concentration uniformity. The results showed that pipe diameters below 70 mm increased the mean axial velocity by 8-fold compared to larger diameters, whereas tilt angles of 15° and porous media heights of 60–80 mm yielded limited velocity enhancements of only 2%. Pipe diameter was identified as the dominant factor governing flow stability, inducing up to a 300% variation in the volume fraction of Q > 1, with minimal turbulence observed at the maximum diameter. In contrast, adjustments to tilt angle and porous media height had weaker effects, altering the Q > 1 volume fraction by 26% and 5%, respectively. Smaller pipe diameter (70–80 mm) also optimized TaCl₅ concentration uniformity; tilt angles between 0° and 30° showed negligible influence, while porous media height exhibited no definitive trend. Guided by the practical priorities of process evaluation, a multi-objective optimization was performed. The globally optimal structural parameters were determined to be a pipe diameter of 70 mm, a tilt angle of 15°, and a porous media height of 60 mm, which comprehensively balance deposition uniformity, process stability, and deposition efficiency. These findings establish pipe diameter as the pivotal factor for deposition homogeneity and provide a reference scheme for the structural design of industrial tantalum deposition furnaces and lay a foundation for subsequent multi-physics coupling studies and experimental validation. Full article

Fibrin gel, a protein-based polymer naturally generated during coagulation, has garnered attention in the biomedical field for applications such as fibrin glue, due to its specific physical and biological properties. Despite it, low mechanical strength and rapid degradation limited its utilization for biomedical applications. This study presents a reproducible protocol for the synthesis of pure fibrin hydrogels, aimed at achieving predictable structural properties through the precise calibration of fibrinogen and thrombin concentrations. By examining the mechanical and morphological characteristics, as well as the relationship between reagent concentrations and structural integrity, this research assesses impacts on swelling behavior, water absorption, and overall stability. Through a comprehensive analytical approach, we identified an optimal formulation, specifically 2.25 mg/mL fibrinogen and 1.375 U/mL thrombin, that effectively balances structural integrity with high cytocompatibility. The results demonstrate that this calibrated approach ensures high procedural reproducibility and a well-defined hydrogel architecture without the need for exogenous chemical cross-linkers. This work provides a robust methodological framework to overcome the common lack of reproducibility in fibrin-based hydrogel studies, positioning these materials as highly reliable candidates for advanced 3D in vitro models and biomedical applications. Full article

Reliable production forecasting is a critical task for evaluating asset valuation and commercial performance in oil and gas reservoirs. Conventional short-term forecasting methods, such as Arps’ decline curve analysis, rely on simple mathematical curve fitting and often oversimplify reservoir performance. On the other hand, long-term forecasting requires complex multidisciplinary models that integrate geophysics, reservoir engineering, and production engineering, but these approaches are time-consuming and have high turnaround times. To bridge the gap between long and short-term production forecasts, reduced-physics models such as Blasingame type curves have been developed, incorporating transient well behaviour derived from diffusivity equations and Darcy’s law. These models assume homogeneity and uniform reservoir properties, enabling faster results while honouring pressure performance. However, despite their efficiency, they still face limitations in reliability, particularly when extended to long-term forecasts. This paper proposes a hybrid modelling approach that integrates flowing material balance (FMB) concepts into physics-informed neural networks (PiNNs) and machine learning models to improve the accuracy and reliability of production forecasting. The proposed methodology introduces two hybrid strategies: physics-informed models enriched with FMB feature, and PiNNs. The first proposed hybrid model uses a created FMB-derived feature as input to neural networks. The second PiNN model embeds data-driven loss functions with a physics-based envelope to reflect reservoir response into the machine learning model. The primary loss function is mean squared error, ensuring minimization of data misfit between predicted and observed production rates. The study validates both proposed physically informed neural network models through performance metrics such as RMSE, MAE, MAPE, and R². Results application on field data shows that the integration of FMB into neural network models using the PiNN concept guides the neural network models to predict the production rates with higher reliability over the full span of the tested data period, which was the last year of unseen production data. Additionally, the proposed PiNN model is able to predict the well productivity index via hyper-tuning of the PiNN model. Furthermore, the PiNN is not improving the metric performance of conventional neural networks, as it has to satisfy an additional material balance equation. This is due to a lower degree of freedom in the PiNN models. Full article

Fully compressible two-phase flow configurations present many challenges for numerical modelling, requiring the development of Real Fluid Models (RFMs) able to simulate flows in subcritical, transcritical and supercritical regimes. Such an RFM has been recently developed at IFPEN based on physical properties lookup tables, mainly for binary and ternary chemical systems. This paper proposes an Artificial Neural Network (ANN) approach to overcome the limitations of lookup tables of thermodynamic properties and to apply RFM to multi-species combustion. A methodology for generating an optimized data set by combining a vapor–liquid equilibrium (VLE) thermodynamic solver and the in situ adaptive tabulation (ISAT) method is developed. It aims to improve the neural network training process for two-phase combustion simulations where many species are present. This ANN methodology has been implemented in the CONVERGE CFD solver and validated using a mixing layer (LOX/GH₂) benchmark from the literature relevant to rocket conditions, and an academic gaseous (H₂/O₂) case relevant to hydrogen combustion. The results show that this ANN approach makes H₂ combustion simulation possible when coupled to the RFM framework and using a 10-species kinetic mechanism. Full article

Phase change material (PCM)-based latent heat storage (LHS) systems help address the mismatch between renewable energy supply and thermal demand. However, their practical implementation is constrained by the strongly nonlinear and multiphysics nature of phase change, which makes high-fidelity simulations and real-time applications computationally expensive. This review examines mathematical reduced-order modeling (ROM) as an effective strategy to overcome this limitation by combining physics-based simplifications, projection methods, interpolation techniques, and data-driven models for PCM-based LHS systems. While physical simplifications (such as dimensional reduction and effective property approximations) represent an important first layer of model reduction, the primary focus of this work is on the mathematical ROM methodologies that operate on the governing equations after such physical simplifications have been applied. The review covers approaches including two-temperature non-equilibrium and analytical thermal-resistance models, Proper Orthogonal Decomposition (POD), CFD-derived look-up tables, kriging and ε-NTU grey/black-box metamodels, and machine-learning methods such as artificial neural networks and gradient-boosted regressors trained from CFD data. These ROM techniques have been applied to packed beds, PCM-integrated heat exchangers, finned enclosures, triplex-tube systems, and solar thermal components, achieving speed-ups from tens to over 80,000 times faster than full CFD simulations while maintaining prediction errors typically below 5% or within sub-Kelvin temperature deviations. A critical comparative analysis exposes the fundamental trade-off between interpretability, data dependence, and computational efficiency, leading to a practical decision-making framework that guides method selection for specific applications such as design optimization, real-time control, and system-level simulation. Remaining challenges—including accurate representation of phase change nonlinearity, moving phase boundaries, multi-timescale dynamics, generalization across geometries, experimental validation, and integration into industrial workflows—motivate a structured roadmap for future hybrid physics–machine learning developments, standardized validation protocols, and pathways toward industrial deployment. Full article

Zinc oxide (ZnO) is currently one of the most significant wide-bandgap semiconductor materials, attracting extensive research across diverse fields including materials science, chemistry, physics, medicine, electronics, and power engineering. Its exceptional properties, such as high optical transparency, high electron mobility, chemical stability, and compatibility with low-cost fabrication techniques, have established ZnO as a versatile material with immense application potential. A critical application for ZnO is its role as a transparent conducting oxide (TCO) in modern optoelectronic and photovoltaic devices, as well as in sensors, transparent electronics, and spintronics. To meet the requirements of these advanced applications, precise control over the structural, optical, and electrical properties of ZnO thin films is essential. This is effectively achieved through the selection of specific synthesis methods and intentional modification techniques, such as doping. This review provides a comprehensive overview of the synthesis and modification of ZnO thin films, with a particular focus on how various dopants influence their fundamental characteristics. The work discusses a range of deposition techniques, including physical vapor deposition (PVD), chemical vapor deposition (CVD), atomic layer deposition (ALD), sol–gel methods, spray pyrolysis, and other solution-based approaches. The novelty of this review lies in its comparative analysis of different doping strategies combined with various thin-film deposition techniques, highlighting how specific synthesis routes influence dopant incorporation and ultimately determine functional properties. Furthermore, recent advances in tailoring ZnO thin films are summarized, alongside the identification of key challenges and future research directions. Ultimately, this work aims to provide researchers with a systematic perspective on the synthesis–structure–property relationships in doped ZnO thin films to support the development of optimized materials for next-generation electronic and optoelectronic devices. This review, thus, serves as a comprehensive reference for researchers and engineers seeking to optimize the functionality of ZnO-based thin films for emerging technological applications. Full article

Soil electrical resistivity (SER) is widely used as an indirect indicator of soil physical, chemical, and hydrological properties and plays an important role in applications such as grounding system design, geotechnical site characterization, agricultural soil monitoring, and environmental contamination assessment. However, SER is strongly influenced by environmental variables including soil moisture content, temperature, salinity, and soil texture, which makes accurate prediction challenging under heterogeneous field conditions. A systematic review was conducted following the PRISMA 2020 protocol using the Scopus database to identify peer-reviewed studies published between 2018 and 2026 related to predictive models of soil electrical resistivity based on environmental parameters. After applying defined inclusion and exclusion criteria, a set of relevant studies was selected for qualitative and comparative analysis. The reviewed studies consistently identify soil moisture content as the most frequently reported influential factor affecting SER, followed by temperature, salinity, and soil texture. This observation reflects the predominant focus of the analyzed literature within the selected time frame rather than a definitive representation of all controlling physical processes. Similarly, the reviewed literature suggests that empirical and statistical models remain valuable due to their simplicity and interpretability, whereas machine learning approaches such as artificial neural networks, support vector regression, and ensemble methods are often reported to achieve higher predictive accuracy in complex soil environments. The predictive SER modeling represents a rapidly evolving research field, and future work should focus on hybrid physics-informed machine learning models, the development of standardized datasets, and the integration of predictive algorithms with emerging sensing technologies and IoT-based monitoring systems. Full article

This study investigates the development of high-strength biofiber cement boards with enhanced thermal insulation properties by utilizing recycled biofibers derived from cement packaging bags, combined with a water-based adhesive to enhance the curing efficiency of Portland cement through a cementation–curing process. This approach reduces waste from cement packaging and other biofiber residues through recycling, thereby promoting environmental sustainability. Moreover, it does not require the use of additional chemicals for the disposal or treatment of fiber waste, nor does it require the incineration of biofiber waste. Recycled biofiber from cement bags, composed primarily of cellulose (60 wt%), lignin (15 wt%), and hemicellulose (10 wt%), serves as a reinforcing phase, while the cement and adhesive mixture functions as a strong binding matrix. The fabrication of composite materials using undamaged cement bag fibers preserves fiber integrity and enables a well-ordered one-dimensional (1D) fiber alignment, which promotes more effective reinforcement than two-dimensional (2D) or three-dimensional (3D) orientations, in accordance with the rule of mixtures. In addition, the incorporation of a water-based PVAc adhesive accelerates the curing rate of the cement phase, promoting the formation of a strong interconnected network structure, and facilitates a more complete curing process. The physical, mechanical, chemical, and thermal properties of the biofiber cement boards were evaluated in accordance with relevant industrial standards, including TISI 878:2023, BS 874, ASTM C1185, ASTM D570, ASTM C518, ISO 8301, and JIS A1412. The results indicate that an optimal cement mortar to water-based adhesive ratio of 1:2, combined with an increased number of biofiber sheet layers, significantly enhances material performance, particularly in Formulas (7)–(9). Among these, Formula (9) exhibits the lowest water absorption (0.0835 ± 0.0102%), the highest tensile strength (19.489 ± 0.670 MPa), the highest flexural strength (20.867 ± 2.505 MPa), the highest Young’s modulus (5735.068 ± 387.032 MPa), and low thermal conductivity (0.152 W/m.K). The resulting boards demonstrate strong bonding ability, enhanced resistance to fire, moisture, and weathering, and a longer service life compared to lower cement-to-adhesive ratios (1:1 and 1:0). These findings demonstrate the potential of recycled biofiber composites, combined with water-based adhesives, as sustainable alternative materials for thermal insulation and structural applications, including ceilings and walls in building construction. Full article

The Xiangshan ore field is characterized by extensive alkali metasomatism, which represents the early-stage hydrothermal event before the acidic metasomatism during major U mineralization. However, the mineralogical and geochemical characteristics of alkali metasomatism, as well as its association with uranium mineralization, remain poorly understood. This study evaluates these scientific problems by conducting petrographic and geochemical analyses on feldspar, together with thermodynamic modeling. Hydrothermal feldspars are present as veinlets, differing from the magmatic ones with granular and subhedral structures. Hydrothermal albites have lower Na but higher K content than magmatic ones, while hydrothermal K-feldspars have lower K but higher Na content than magmatic ones. In addition, hydrothermal feldspars are significantly depleted in Ca and Sr, likely associated with the consumption of Ca in fluids by fluorite and calcite precipitation. Furthermore, alkali metasomatism is accompanied by intense hematitization, indicating the oxidized properties of ore fluids that are favorable for uranium transport. Thermodynamic modeling further demonstrates that continuous K⁺ consumption during fluid–rock interaction leads to a pH increase in the fluid, which is buffered by quartz–muscovite–K-feldspar (QMF). Given that quartz solubility is positively correlated with pH, this process induces extensive quartz dissolution in the host rocks. Such dissolution significantly enhances the porosity and permeability of the host rocks, creating ideal physical traps for the subsequent accumulation of uranium-bearing fluids. Consequently, alkali-metasomatized rocks associated with quartz dissolution and hematitization serve as critical indicators for regional uranium exploration. Full article

Unmanned Aerial Vehicles (UAVs) are complex nonlinear systems characterized by high dimensionality. They are prone to aerodynamic effects, structural dynamics, actuation constraints, and networked interactions, requiring advanced mathematical models and precise control. Their governing equations involve nonlinear rigid-body dynamics coupled with fluid and elasticity models, while modern architectures introduce redundancy that creates constrained mappings between generalized forces and actuator inputs. Coordinated UAV teams add another layer of mathematical structure through graph-based interaction models that determine consensus, formation keeping, and distributed stability. These characteristics give rise to several interconnected challenges. High-fidelity aerodynamic and aeroelastic solvers provide accurate results; however, these are computationally intensive, motivating the development of reduced-order models and data-driven approximations that preserve dominant physical behavior. Methods for quantifying uncertainty support robustness assessments by characterizing the effects of parametric variation and model form error. At the actuation level, control allocation problems rely on constrained linear algebra, convex optimization, and dynamic formulations to ensure feasible and stable realization of command forces and moments. In multi-agent systems, the spectral properties of adjacency and Laplacian matrices govern convergence and cooperative behavior. This article reviews the state of the art in these areas, highlights the mathematical foundations that relate them, and provides a coherent perspective on the methods that enable reliable modeling and control of modern UAV systems. Full article

We review the jet kinematics of HBL blazars based on results of the MOJAVE survey, which obtained images of active galactic nuclei with the Very Long Baseline Array (VLBA) at 15 GHz, and the VLBA-BU-BLAZAR program as well as its successor BEAM-ME program, which have observed $\gamma$-ray blazars at 43 GHz. We present and discuss recent kinematic behavior and polarization properties of the parsec-scale jets of three typical HBL sources over the 2020–2025 period. We outline current physical implications for reconciling the high-energy characteristics of HBL sources with their parsec-scale jet properties. Full article

In porous ignimbrites, porosity defines the first-order control on elastic trend, but rocks with similar porosity can still behave differently because their pore networks are arranged differently. We analyzed 50 specimens from seven ignimbrite units in Mexico using density and porosity measurements, permeability tests, mercury intrusion porosimetry, image-based pore descriptors, and ultrasonic P- and S-wave velocities. At the unit scale averages, total porosity ranges from 31.4% in Tl to 42.9%, but elastic properties and permeability vary widely, showing that porosity alone does not define a unique physical state. Two end-member pore-network tendencies can be recognized: crack-linked, throat-restricted systems and more equant or intergranular systems. At similar porosity, crack-dominated networks are generally less stiff and less permeable, whereas more equant networks show higher permeability and stiffer behavior under dry conditions. Effective-medium models indicate that most samples are consistent with KT aspect ratios of 0.15–0.20 and a critical-porosity range of 40–60%. Overall, porosity defines the first-order elastic trend, whereas pore-network architecture explains much of the remaining hydraulic variability and part of the residual elastic spread. Full article

Palm kernel oil is commonly incorporated into plant-based cheeses to mimic the textural and structural properties of animal fats owing to its high saturated fat content. Nevertheless, growing concerns regarding saturated fat consumption have stimulated research into alternative lipid sources for plant-based products. Therefore, this study aimed to evaluate the effects of substituting palm kernel oil with rice bran oil shortening (SRBO) on some selected physical, textural, functional, chemical, fatty acid and microstructural properties of plant-based mozzarella cheese analogs. Five formulations with SRBO levels of 0, 25, 50, 75, and 100% were prepared and their physicochemical properties were analyzed. Increasing SRBO significantly affected color due to natural pigments in rice bran oil. The pH value declined with higher SRBO, likely due to oxidation of unsaturated fatty acids. Texture profile analysis showed increases in hardness, springiness, cohesiveness, gumminess, and chewiness when SRBO was increased from 0% to 100%. Meltability slightly decreased at 25–75% but remained unchanged at 100% SRBO, while stretchability decreased significantly, attributed to β-type fat crystals disrupting protein networks. The work of shear decreased significantly (p ≤ 0.05), indicating improved spreadability attributed to the softer, less-crystalline nature of unsaturated fats compared to saturated fats. Proximate analysis revealed reduced fat content and a shift from saturated to unsaturated fats, notably oleic and linoleic acids, offering potential cardiovascular benefits. Confocal laser scanning microscopy showed denser fat crystal networks and smaller fat droplets at higher SRBO levels, enhancing oil retention and stability. Protein, fiber, moisture, and ash content remained stable across samples. These findings suggested that SRBO could be a functional and health-conscious alternative to palm kernel oil in plant-based mozzarella cheese, improving nutritional quality without compromising texture or functionality. Full article

Plastic manufacturing depends heavily on petroleum-derived monomers like terephthalic acid, the main component of polyethylene terephthalate (PET). However, the depletion of fossil resources and increasing environmental concerns have heightened the need for sustainable alternatives. Lignocellulosic biomass has emerged as a promising resource due to its renewable, abundant, and eco-friendly nature. Understanding its chemical composition enables conversion of this biomass into platform chemicals, such as 2,5-furandicarboxylic acid (FDCA) and lactic acid, derived from cellulose and hemicellulose. These can be polymerized into bio-based plastics such as polyethylene furanoate (PEF), polylactic acid (PLA), and polyhydroxyalkanoates (PHAs), offering greener alternatives to fossil-based plastics. PEF features rigid furan rings that enhance thermal stability, mechanical strength, and barrier properties, and reduce gas permeability compared to PET. PLA is a renewable, biodegradable plastic widely used in packaging and medical applications. This review covers the chemical composition of lignocellulosic biomass cellulose, hemicellulose, and lignin, and various pretreatment strategies, chemical, physicochemical, and physical, to overcome biomass recalcitrance and improve conversion efficiency. It also highlights recent catalytic advances in transforming cellulosic carbohydrates into bio-based plastic precursors such as FDCA and lactic acid. Lastly, this review discusses polymerization pathways for producing PEF and PLA, emphasizing their role in reducing the environmental impact of polymer manufacturing and promoting green chemistry principles. Full article