Processing Methodology Research Articles

Innovative graph-based video processing methodology for collapse early warning of historic masonry buildings

In the present study an innovative video processing methodology is proposed for application to vibration data of historic masonry structures. The proposed methodology is based on graph theory and topology analysis applied to magnified videos in search of effective parameters for early-warning signals before the collapse of structures in case of earthquakes. The proposed method was validated through seismic tests of a brick-masonry mockup representing a vault of the mosque in the Palace of the Dey, Algiers. In particular, the seismic tests were carried out at increasing earthquake intensity up to the final collapse of the mockup. After processing the videos of the seismic tests by motion magnification method, the magnified video frames were transformed into a graph of the structure. Finally, several graph indices were calculated and monitored during the vibration. The monitored parameters were analyzed in search of potential threshold values suitable to generate an early warning signal. In particular, the inverse algebraic connectivity provided an early warning signal in the order of a few seconds before collapse. This was validated by comparison with an analogous signal provided at similar time by an accurate displacement lab measurements system based on optical markers positioned at several points of the tested mock-up.

Photoacoustic processing of decellularized extracellular matrix for biofabricating living constructs

The diverse biomolecular landscape of tissue-specific decellularized extracellular matrix (dECM) biomaterials provides a multiplicity of bioinstructive cues to target cells, rendering them highly valuable for various biomedical applications. However, the isolation of dECM biomaterials entails cumbersome xenogeneic enzymatic digestions and also additional inactivation procedures. Such, increases processing time, increments costs and introduces residues of non-naturally present proteins in dECM formulations that remain present even after inactivation. To overcome these limitations, herein we report an innovative conjugation of light and ultrasound-mediated dECM biomaterial processing for fabricating dECM biomaterials. Such approach gathers on ultrasound waves to facilitate dECM-in-liquid processing and visible light photocrosslinking of tyrosine residues naturally present in dECM biomaterials. This dual step methodology unlocked the in-air production of cell laden dECM hydrogels or programmable dECM hydrogel spherical-like beads by using superhydrophobic surfaces. These in-air produced units do not require any additional solvents and successfully supported both fibroblasts and breast cancer cells viability upon encapsulation or surface seeding. In addition, the optimized photoacoustic methodology also enabled a rapid formulation of dECM biomaterial inks with suitable features for biofabricating volumetrically defined living constructs through embedded 3D bioprinting. The biofabricated dECM hydrogel constructs supported cell adhesion, spreading and viability for 7 days. Overall, the implemented photoacoustic processing methodology of dECM biomaterials offers a rapid and universal strategy for upgrading their processing from virtually any tissue. Statement of significanceLeveraging decellularized extracellular matrix (dECM) as cell instructive biomaterials has potential to open new avenues for tissue engineering and in vitro disease modelling. The processing of dECM remains however, lengthy, costly and introduces non-naturally present proteins in the final biomaterials formulations. In this regard, here we report an innovative light and ultrasound two-step methodology that enables rapid dECM-in-liquid processing and downstream photocrosslinking of dECM hydrogel beads and 3D bioprinted constructs. Such photoacoustic based processing constitutes a universally applicable method for processing any type of tissue-derived dECM biomaterials.

First report of betalain production from endolichenic Bacillus sp. LDAB-1 from Dirinaria aegilita: Insights from novel quantification methodology of image processing.

Pigments are widely used in food supplements envisaging attractive colors along with health benefits. The desired advancements in the nutraceutical and antioxidant properties of pigments utilized in food products necessitate the search for novel additives. The present study is the first in the field to report the pigment-producing endolichenic bacteria, Bacillus sp. LDAB-1 from Dirinaria aegilita. Morphological, biochemical, and molecular characterization of the bacterium emphasizes that ideal pigment production occurs when utilizing sucrose and sodium nitrate. The pigment was salted out and dialyzed for further qualitative characterization using ultraviolet-visible, fluorescence, and Fourier transforminfraredspectra and the results corroborated the presence of betalains. The antioxidant activity of betalain is closer to the efficiency of α-tocopherol, which confers the pigment properties for antioxidant and nutraceutical significance. An optimal methodology for pigment affirmation is an issue when using an alternative methodology. Hence, the present assessment employs a comparative analysis of findings from both a spectrophotometric method and image processing technology encompassing RGB, CMYK, YCbCr, and L*a*b*color space models. Amongst these, the L*a*b* model potentially provides an effective modality for determining the pigment concentration. Bland-Altman plot analysis indicates similar consistency levels in betalain quantification by both methods at 95% confidence intervals, affirming the integrity and consistency of color image processing technology. Consequently, the present study represents novelty and innovativeness in reporting endolichenic Bacillus sp. LDAB-1 from D. aegilita and a rational image optimization protocol for pigment elucidation characteristics.

Advanced signal processing methodology of vibration response data toward Structural Health Monitoring purposes

Advanced signal processing methodology of vibration response data toward Structural Health Monitoring purposes

Proteomics of human platelet lysates and insight from animal studies on platelet protein diffusion to hippocampus upon intranasal administration.

Human platelet lysates (HPLs) from allogeneic platelet concentrates (PCs) are biomaterials, which are rich in various trophic factors, increasingly used in regenerative medicine and biotherapy. Understanding how preparation methods influence the HPL protein profile, biological function, and clinical outcomes is crucial. Our study sheds light on the proteomes and functionality of different HPLs, with the aim of advancing their scientifically grounded clinical applications. To achieve this, PCs suspended in plasma underwent three distinct processing methods, resulting in seven HPL types. We used three characterization techniques: label-free proteomics and tandem mass tag (TMT)-based quantitative proteomics, both before and after the immunodepletion of abundant plasma proteins. Bioinformatic tools assessed the proteome, and western blotting validated our quantitative proteomics data. Subsequent pre-clinical studies with fluorescent labeling and label-free proteomics were used as a proof of concept for brain diffusion. Our findings revealed 1441 proteins detected using the label-free method, 952 proteins from the TMT experiment before and after depletion, and 1114 proteins from the subsequent TMT experiment on depleted HPLs. Most detected proteins were cytoplasmic, playing key roles in catalysis, hemostasis, and immune responses. Notably, the processing methodologies significantly influenced HPL compositions, their canonical pathways, and, consequently, their functionality. Each HPL exhibited specific abundant proteins, providing valuable insight for tailored clinical applications. Immunoblotting results for selected proteins corroborated our quantitative proteomics data. The diffusion and differential effects to the hippocampus of a neuroprotective HPL administered intranasally to mice were demonstrated. This proteomics study advances our understanding of HPLs, suggesting ways to standardize and customize their production for better clinical efficacy in regenerative medicine and biotherapy. Proteomic analyses also offered objective evidence that HPPL, upon intranasal delivery, not only effectively diffuses to the hippocampus but also alters protein expression in mice, bolstering its potential as a treatment for memory impairments.

Development of solid recovery fuel for power generation with bacterial bio-drying of municipal solid waste in Lombok Island West Nusa Tenggara

Abstract The growth of waste in big cities in Indonesia increases sharply every year. The Government’s ability to manage solid waste only reaches 40.09% in urban areas and 1.02% in rural areas, so the right policies are needed so that Municipal Solid Waste (MSW) does not save potential problems that will impact the future. The waste problem is no longer just a matter of cleanliness and the environment but has become a social problem that has the potential to cause conflict. The most likely solution to solving the MSW problem is to use it as a source of alternative fuel, which can be done by a mechanical-biological treatment technology into Solid Recovery Fuel (SRF). The proposed SRF processing method is straightforward and requires a little technology; thus, it could be developed at the community level. The MSW is collected and processed by communities and then sold to power plants or factories or used in communities to replace solid fuels such as wood or charcoal. Compared with the standard RDF and SRF specifications, MSW SRF falls under medium-grade Refuse Derived Fuel (RDF) or SRF specifications. Further comparison with coal, water hyacinth SRF, and sorghum shows that MSW SRF has several advantages, such as low fixed carbon, sulfur, and moisture, that could be beneficial for combustion performance, which concludes that MSW has potential and meet the specification as an alternative fuel for thermal power plant or other industry that utilizes thermal energy. This paper proposes a processing methodology for municipal solid waste into alternative energy. The method could guide local people in using biomass resources to improve local economic prosperity.

The impact of alcohol on affiliative verbal behavior: Asystematic review and meta-analysis.

Language is a fundamental aspect of human social behavior that is linked to many rewarding social experiences, such as social bonding. Potential effects of alcohol on affiliative language may therefore be an essential feature of alcohol reward and may elucidate pathways through which alcohol is linked to social facilitation. Examinations of alcohol's impact on language content, however, are sparse. Accordingly, this investigation represents the first systematic review and meta-analysis of alcohol's effects on affiliative language. We test the hypothesis that alcohol increases affiliative verbal approach behaviors and discuss future research directions. PsycInfo and Web of Science were systematically searched in March 2023 according to our preregistered plan. Eligible studies included social alcohol administration experiments in which affiliative verbal language was assessed. We present a random-effects meta-analysis that examines the effect of alcohol compared to control on measures of affiliative verbal behavior. Our search identified 16 distinct investigations (comprising 961 participants) that examined the effect of alcohol on affiliative verbal behavior. Studies varied greatly in methods and measures. Meta-analytic results demonstrated that alcohol is modestly associated with increases in affiliative verbal behavior (Hedges' g = 0.164, 95% CI [0.027, 0.301], p = 0.019). Study quality was rated using an adapted version of the Quality Assessment Tool for Quantitative Studies and did not significantly moderate alcohol's effects. This study provides preliminary evidence that alcohol can increase affiliative verbal behaviors. This effect may be an important feature of alcohol reward. Given heterogeneity in study features, low study quality ratings, and limited reporting of effect size data, results simultaneously highlight the promise of this research area and the need for more work. Advances in language processing methodologies that could allow future work to systematically expand upon this finding are discussed.

Geodata Processing Methodology on GIS Platforms When Creating Spatial Development Plans of Territorial Communities: Case of Ukraine

The article offers a detailed expert analysis on handling spatial information of territorial communities amid administrative and land reforms. The authors have developed multiple methodological recommendations and justified the selection of efficient tools for contemporary GIS systems. It also delves into the essence of GIS technologies, the nuances of working with geodata, and provides a concise overview of the world's most prevalent GIS platforms. The paper outlines an algorithm for developing a land resource management strategy for communities in preparation for land reform. Through the analysis and visualization of spatial data, the paper highlights enhancements in economic valuation and the ability to pinpoint risks and infringements of property rights. The study selects the Cadastr.UA system on the Soft.Farm platform, developed by Ukrainian company Quart Soft, as a notably user-friendly and promising GIS for land accounting and auditing. Research based on the experimental practical application of most functions of GIS Cadastr.UA indicates that this system aligns well with the needs for land plot accounting and auditing within the framework of Ukraine's Public cadastral map. The paper details the practical implementation of GIS technologies, using Cadastr.UA as a case study, and illustrates how to employ selected GIS tools in various scenarios and tasks.

A moment-based Kalman filtering approach for estimation in ensemble systems.

A persistent challenge in tasks involving large-scale dynamical systems, such as state estimation and error reduction, revolves around processing the collected measurements. Frequently, these data suffer from the curse of dimensionality, leading to increased computational demands in data processing methodologies. Recent scholarly investigations have underscored the utility of delineating collective states and dynamics via moment-based representations. These representations serve as a form of sufficient statistics for encapsulating collective characteristics, while simultaneously permitting the retrieval of individual data points. In this paper, we reshape the Kalman filter methodology, aiming its application in the moment domain of an ensemble system and developing the basis for moment ensemble noise filtering. The moment system is defined with respect to the normalized Legendre polynomials, and it is shown that its orthogonal basis structure introduces unique benefits for the application of Kalman filter for both i.i.d. and universal Gaussian disturbances. The proposed method thrives from the reduction in problem dimension, which is unbounded within the state-space representation, and can achieve significantly smaller values when converted to the truncated moment-space. Furthermore, the robustness of moment data toward outliers and localized inaccuracies is an additional positive aspect of this approach. The methodology is applied for an ensemble of harmonic oscillators and units following aircraft dynamics, with results showcasing a reduction in both cumulative absolute error and covariance with reduced calculation cost due to the realization of operations within the moment framework conceived.

Fake Indian Currency Detection

The proliferation of counterfeit currency poses a significant threat to both individuals and the national economy. While existing fake currency detection tools are primarily accessible to banks and large enterprises, everyday people and small businesses remain susceptible. Thus, this project aims to delve into the security features of Indian currency and develop a software solution leveraging advanced image processing and computer vision techniques to detect and neutralize counterfeit notes. Counterfeiting currency poses a genuine menace to both the populace's well-being and the nation's economic stability. Although counterfeit currency detection tools exist, their accessibility is typically confined to banking institutions and corporate entities, leaving ordinary citizens and small enterprises susceptible to fraud. Thus, this project endeavours to examine the diverse security attributes of Indian currency and subsequently craft a software-driven apparatus capable of discerning and nullifying counterfeit Indian currency through sophisticated image processing and computer vision methodologies. Notably, this currency authentication system will be meticulously crafted using the Python programming language within the Jupyter Notebook framework.

Numerical Optimization of Variable Blank Holder Force Trajectories in Stamping Process for Multi-Defect Reduction.

An intelligent optimization technology was proposed to mitigate prevalent multi-defects, particularly failure, wrinkling, and springback in sheet metal forming. This method combined deep neural networks (DNNs), genetic algorithms (GAs), and Monte Carlo simulation (MCS), collectively as DNN-GA-MCS. Our primary aim was to determine intricate process parameters while elucidating the intricate relationship between processing methodologies and material properties. To achieve this goal, variable blank holder force (VBHF) trajectories were implemented into five sub-stroke steps, facilitating adjustments to the blank holder force via numerical simulations with an oil pan model. The Forming Limit Diagram (FLD) predicted by machine learning algorithms based on the Generalized Incremental Stress State Dependent Damage (GISSMO) model provided a robust framework for evaluating sheet failure dynamics during the stamping process. Numerical results confirmed significant improvements in formed quality: compared with the average value of training sets, the improvements of 18.89%, 13.59%, and 14.26% are achieved in failure, wrinkling, and springback; in the purposed two-segmented mode VBHF case application, the average value of three defects is improved by 12.62%, and the total summation of VBHF is reduced by 14.07%. Statistical methodologies grounded in material flow analysis were applied, accompanied by the proposal of distinctive optimization strategies for the die structure aimed at enhancing material flow efficiency. In conclusion, our advanced methodology exhibits considerable potential to improve sheet metal forming processes, highlighting its significant effect on defect reduction.

Osmotic Dehydration Model for Sweet Potato Varieties in Sugar Beet Molasses Using the Peleg Model and Fitting Absorption Data Using the Guggenheim-Anderson-de Boer Model.

This study investigates the applicability of the Peleg model to the osmotic dehydration of various sweet potato variety samples in sugar beet molasses, addressing a notable gap in the existing literature. The osmotic dehydration was performed using an 80% sugar beet molasses solution at temperatures of 20 °C, 35 °C, and 50 °C for periods of 1, 3, and 5 h. The sample-to-solution ratio was 1:5. The objectives encompassed evaluating the Peleg equation's suitability for modeling mass transfer during osmotic dehydration and determining equilibrium water and solid contents at various temperatures. With its modified equation, the Peleg model accurately described water loss and solid gain dynamics during osmotic treatment, as evidenced by a high coefficient of determination value (r2) ranging from 0.990 to 1.000. Analysis of Peleg constants revealed temperature and concentration dependencies, aligning with previous observations. The Guggenheim, Anderson, and de Boer (GAB) model was employed to characterize sorption isotherms, yielding coefficients comparable to prior studies. Effective moisture diffusivity and activation energy calculations further elucidated the drying kinetics, with effective moisture diffusivity values ranging from 1.85 × 10-8 to 4.83 × 10-8 m2/s and activation energy between 7.096 and 16.652 kJ/mol. These findings contribute to understanding the complex kinetics of osmotic dehydration and provide insights into the modeling and optimization of dehydration processes for sweet potato samples, with implications for food processing and preservation methodologies.

Effects of Sex on Growth Performance, Carcass Traits, Blood Biochemical Parameters, and Meat Quality of XueShan Chickens.

The demand for high-quality chilled chicken has continued to increase in China. Chickens are sexually dimorphic, and to better understand the specific differences in chicken production based on sex, we examined how sex affects growth performance, carcass traits, and meat quality of yellow-feathered chickens. Male and female Xueshan chickens were used as the experimental model. Although males exhibited better growth performance, including body weight (BW), body slope, keel, shank length, and shank girth (p < 0.05), as well as carcass traits, such as dressed weight, leg muscle, and lean meat, females had higher carcass and breast muscle yields (p < 0.05). Males had higher follicle density and yellowness (b*) of the skin and better skin than females (p < 0.05). Among blood biochemical parameters, the serum content of corticosterone (CORT) was higher in males, while those of superoxide dismutase (SOD), glutathione peroxidase (GSH-PX), total antioxidant capacity (T-AOC), and catalase (CAT) were lower in males than in females (p < 0.05). The pH levels, shear force, and moisture content quality were better in male breast meat, while the intramuscular fat content (IMF) was lower in males than in females (p < 0.05). The redness (a*) and moisture content were higher in male leg meat, while the pH, water-loss rate (WLR), lightness (L*), and IMF were lower (p < 0.05). The muscle fiber diameter and cross-sectional area were also higher in males (p < 0.05). Consumers felt that soup of male chicken was better than female (p < 0.05), while mouthfeel and tenderness acceptance of breast meat were different between the sexes. These results indicate that female chickens can be marketed as a whole carcass, while males are more suitable for processed carcass products. This study provides significant insights into the production and processing methodologies of yellow-feathered chickens.

Exploring sample treatment strategies for untargeted metabolomics: A comparative study of solid phase microextraction (SPME) and homogenization with solid-liquid extraction (SLE) in renal tissue

BackgroundThe selection of the sample treatment strategy is a crucial step in the metabolomics workflow. Solid phase microextraction (SPME) is a sample processing methodology with great potential for use in untargeted metabolomics of tissue samples. However, its utilization is not as widespread as other standard protocols involving steps of tissue collection, metabolism quenching, homogenization, and extraction of metabolites by solvents. Since SPME allows us to perform all these steps in one action in tissue samples, in addition to other advantages, it is necessary to know whether this methodology produces similar or comparable metabolome and lipidome coverage and performance to classical methods. ResultsSPME and homogenization with solid-liquid extraction (Homo-SLE) sample treatment methods were applied to healthy murine kidney tissue, followed by comprehensive metabolomics and lipidomics analyses. In addition, it has been tested whether freezing and storage of the tissue causes alterations in the renal metabolome and lipidome, so the analyses were performed on fresh and frozen tissue samples Lipidomics analysis revealed the exclusive presence of different structural membrane and intracellular lipids in the Homo-SLE group. Conversely, all annotated metabolites were detected in both groups. Notably, the freezing of the sample mainly causes a decrease in the levels of most lipid species and an increase in metabolites such as amino acids, purines, and pyrimidines. These alterations are principally detected in a statistically significant way by SPME methodology. Finally, the samples of both methodologies show a positive correlation in all the analyses. SignificanceThese results demonstrate that in SPME processing, as long as the fundamentals of non-exhaustive extraction in a pre-equilibrium kinetic regime, extraction in a tissue localized area, the chemistry of the fiber coating and non-homogenization of the tissue are taken into account, is an excellent method to use in kidney tissue metabolomics; since this methodology presents an easy-to-use, efficient, and less invasive approach that simplifies the different sample processing steps.

Replicating outdoor environments using VR and ambisonics: a methodology for accurate audio-visual recording, processing and reproduction

This paper introduces a methodology tailored to capture, post-process, and replicate audio-visual data of outdoor environments (urban or natural) for VR experiments carried out within a controlled laboratory environment. The methodology consists of 360∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circ$$\\end{document} video and higher order ambisonic (HOA) field recordings and subsequent calibrated spatial sound reproduction with a spherical loudspeaker array and video played back via a head-mounted display using a game engine and a graphical user interface for a perceptual experimental questionnaire. Attention was given to the equalisation and calibration of the ambisonic microphone and to the design of different ambisonic decoders. A listening experiment was conducted to evaluate four different decoders (one 2D first-order ambisonic decoder and three 3D third-order decoders) by asking participants to rate the relative (perceived) realism of recorded outdoor soundscapes reproduced with these decoders. The results showed that the third-order decoders were ranked as more realistic.

Enhancing ferroelectric characterization at nanoscale: A comprehensive approach for data processing in spectroscopic piezoresponse force microscopy

Switching Spectroscopy Piezoresponse Force Microscopy (SSPFM) stands out as a powerful method for probing ferroelectric properties within materials subjected to incremental polarization induced by an external electric field. However, the dense data processing linked to this technique is a critical factor influencing the quality of obtained results. Furthermore, meticulous exploration of various artifacts, such as electrostatics, which may considerably influence the signal, is a key factor in obtaining quantitative results. In this paper, we present a global methodology for SSPFM data processing, accessible in open-source with a user-friendly Python application called PySSPFM. A ferroelectric thin film sample of potassium sodium niobate has been probed to illustrate the different aspects of our methodology. Our approach enables the reconstruction of hysteresis nano-loops by determining the PR as a function of applied electric field. These hysteresis loops are then fitted to extract characteristic parameters that serve as measures of the ferroelectric properties of the sample. Various artifact decorrelation methods are employed to enhance measurement accuracy, and additional material properties can be assessed. Performing this procedure on a grid of points across the surface of the sample enables the creation of spatial maps. Furthermore, different techniques have been proposed to facilitate post-treatment analysis, incorporating algorithms for machine learning (K-means), phase separation, and mapping cross correlation, among others. Additionally, PySSPFM enables a more in-depth investigation of the material by studying the nanomechanical properties during poling, through the measurement of the resonance properties of the cantilever–tip–sample surface system.

Tackling unbalanced datasets for yellow and brown rust detection in wheat.

This study evaluates the efficacy of hyperspectral data for detecting yellow and brown rust in wheat, employing machine learning models and the SMOTE (Synthetic Minority Oversampling Technique) augmentation technique to tackle unbalanced datasets. Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and Gaussian Naïve Bayes (GNB) models were assessed. Overall, SVM and RF models showed higher accuracies, particularly when utilizing SMOTE-enhanced datasets. The RF model achieved 70% accuracy in detecting yellow rust without data alteration. Conversely, for brown rust, the SVM model outperformed others, reaching 63% accuracy with SMOTE applied to the training set. This study highlights the potential of spectral data and machine learning (ML) techniques in plant disease detection. It emphasizes the need for further research in data processing methodologies, particularly in exploring the impact of techniques like SMOTE on model performance.

Convolutional neural networks and Internet of Things for fault detection by aerial monitoring of photovoltaic solar plants

Aerial thermographic inspection is performed with thermal cameras embedded in unmanned aerial vehicles, being one of the most relevant monitoring techniques for photovoltaic panels. This technique allows the detection of thermal patterns associated with faults in the photovoltaic panels, although image analysis and fault detection require novel processing methodologies. The volume and variety of aerial thermal data together with the need for faster and more efficient analysis for early maintenance operations require efficient Internet of Things architectures to provide fast and effective diagnostics with easy access.This paper presents a new approach based on image analysis with two consecutive convolutional neural networks to detect hot spots in an Internet of Things platform and reduce the number of false positives. The architecture of the platform is designed to automatically process the received data through the implementation of different Convolutional Neural Networks. A real case study is proposed with thermograms from three photovoltaic solar plants with different sizes, shapes of panels and a wide temperature range. The results show an accuracy of 99 % for panel detection and 96 % for hot spot detection with a reduction of false positives compared to other studies, such as Support Vector Machine or different Artificial Neural Networks, demonstrating the robustness of the method.

Fibrous Structures: An Overview of Their Responsiveness to External Stimuli towards Intended Application.

In recent decades, the interest in responsive fibrous structures has surged, propelling them into diverse applications: from wearable textiles that adapt to their surroundings, to filtration membranes dynamically altering selectivity, these structures showcase remarkable versatility. Various stimuli, including temperature, light, pH, electricity, and chemical compounds, can serve as triggers to unleash physical or chemical changes in response. Processing methodologies such as weaving or knitting using responsive yarns, electrospinning, as well as coating procedures, enable the integration of responsive materials into fibrous structures. They can respond to these stimuli, and comprise shape memory materials, temperature-responsive polymers, chromic materials, phase change materials, photothermal materials, among others. The resulting effects can manifest in a variety of ways, from pore adjustments and altered permeability to shape changing, color changing, and thermal regulation. This review aims to explore the realm of fibrous structures, delving into their responsiveness to external stimuli, with a focus on temperature, light, and pH.

INFLUENCE OF ELECTROMAGNETIC TREATMENT ON HYDRAULIC CHARACTERISTICS OF HOT PIPELINES DURING NEWTONIAN AND NON-NEWTONIAN PETROLEUM SYSTEMS TRANSPORTATION

This work examines the influence of radio-frequency (RF) electromagnetic (EM) influence on the hydraulic characteristics of hot pipelines for the transport of petroleum Newtonian and non-Newtonian systems.Newtonian and non-Newtonian petroleum and their rheological properties before and after RF EM exposure, as well as the hydraulic characteristics of hot petroleum pipelines for pumping selected petroleum, were selected as objects of study.As a result: a methodology for petroleum processing has been developed that corresponds to industrial practice in the operation of petroleum pipelines; empirical dependences of petroleum viscosity on temperature were obtained in the form of piecewise exponential functions. The influence of RF EM exposure on the thixotropic properties of petroleum and the values of ultimate shear stresses was assessed. A decrease or complete disappearance of hysteresis loops after RF EM exposure to petroleum and a decrease in the maximum shear stress for non-Newtonian petroleum were revealed.By using the software developed by the authors, «Hydraulic calculation of isothermal and non-isothermal petroleum pipelines (GRINT)», the processes of pipeline transport of petroleum treated with EM fields were modeled, taking into account the obtained empirical dependencies. An assessment of the influence of RF EM exposure on the hydraulic characteristics of «hot» pipelines was carried out. It has been established that after RF EM exposure to petroleum, the zone of stable operation of a «hot» petroleum pipeline increases and the required pressure for pumping the same volume of petroleum decreases.