Effective Processing Method Research Articles

Evaluation of Thermal Liquid Biopsy Analysis of Saliva and Blood Plasma Specimens as a Novel Diagnostic Modality in Head and Neck Cancer

Background: Over the past decade, saliva-based liquid biopsies have emerged as promising tools for the early diagnosis, prognosis, and monitoring of cancer, particularly in high-risk populations. However, challenges persist because of low concentrations and variable modifications of biomarkers linked to tumor development when compared to normal salivary components. Methods: This study explores the application of differential scanning calorimetry (DSC)-based thermal liquid biopsy (TLB) for analyzing saliva and blood plasma samples from head and neck cancer (HNC) patients. Results: Our research identified an effective saliva processing method via high-speed centrifugation and ultrafiltration, resulting in reliable TLB data. Notably, we recorded unique TLB profiles for saliva from 48 HNC patients and 21 controls, revealing distinct differences in thermal transition features that corresponded to salivary protein denaturation. These results indicated the potential of saliva TLB profiles in differentiating healthy individuals from HNC patients and identifying tumor characteristics. In contrast, TLB profiles for blood plasma samples exhibited smaller differences between HNC patients and had less utility for differentiation within HNC. Conclusions: Our findings support the feasibility of saliva-based TLB for HNC diagnostics, with further refinement in sample collection and the incorporation of additional patient variables anticipated to enhance accuracy, ultimately advancing non-invasive diagnostic strategies for HNC detection and monitoring.

Research on Fine-Tuning Optimization Strategies for Large Language Models in Tabular Data Processing.

Recent advancements in natural language processing (NLP) have been significantly driven by the development of large language models (LLMs). Despite their impressive performance across various language tasks, these models still encounter challenges when processing tabular data. This study investigates the optimization of fine-tuning strategies for LLMs specifically in the context of tabular data processing. The focus is on the effects of decimal truncation, multi-dataset mixing, and the ordering of JSON key-value pairs on model performance. Experimental results indicate that decimal truncation reduces data noise, thereby enhancing the model's learning efficiency. Additionally, multi-dataset mixing improves the model's generalization and stability, while the random shuffling of key-value pair orders increases the model's adaptability to changes in data structure. These findings underscore the significant impact of these strategies on model performance and robustness. The research provides novel insights into improving the practical effectiveness of LLMs and offers effective data processing methods for researchers in related fields. By thoroughly analyzing these strategies, this study aims to establish theoretical foundations and practical guidance for the future optimization of LLMs across a broader range of application scenarios.

An automatic hybrid mesh generation method for combustion fluid dynamics simulations

PurposeAutomatic mesh generation is still a challenge problem for combustion fluid dynamics simulations because of the high-quality requirement and complexity of geometries. This paper aims to find an efficient automatic analysis model creation and mesh generation method to save the time for pre-processing in numerical simulations.Design/methodology/approachBased on the previous work, we explore effective model processing and mesh generation methods from practical engineering applications. Considering the automation and high quality requirement, we construct an automatic mesh generation procedure for combustion fluid dynamics problems.FindingsThe numerical results show that the procedure we proposed in this paper can automatically generate high quality mesh for combustion fluid dynamics simulations. The strategy of fluid model construction is time-saving and with high-precision. The mesh generation method in our procedure is automatic and efficient.Practical implicationsThe procedure proposed in this paper is applicable to the practical engineering application model, such as aircraft simulation, aeroengine simulation and so on. The procedure has been integrated into an numerical simulation software.Originality/valueThe method proposed in this paper has very practical application value. It can be used in the practical application and saves a lot of manual processing time for numerical stimulation specialists.

The Impact of Financial Disclosure on Stock Performance of Initial Public Offerings in the Indonesian Capital Market

This research aims to explore whether the proceeds, age, revenue, and underwriter rating of IPO companies affect disclosure; whether disclosure mediates the relationship between the four exogenous variables and underpricing; and whether underpricing mediates the relationship between disclosure and returns. This research uses Partial Least Squares Structural Equation Modelling (PLS-SEM) as the data processing method. Since this research examines interactions between variables, PLS-SEM is an effective data processing method. This research identifies four key exogenous variables: funds raised from the IPO, the age of the IPO company, the income of the companies prior to the IPO, and the ranking of underwriting companies. The research finds that the R-squared of the disclosure variable is 0.0037, or 3.7%, while the Q-squared of the disclosure variable is 0.0039. Meanwhile, the R-squared of the underpricing variable is 0.435, and the Q-squared of the underpricing variable is 0.456. The research also finds that higher proceeds, age, revenue, and underwriter ranking of IPO companies will reduce disclosure. Conversely, higher proceeds and age of the IPO companies will reduce underpricing, while higher revenue and underwriter ranking of the IPO company will increase underpricing. Finally, the research finds that higher disclosure will increase underpricing. Статтю розміщено в рамках грантової програми ГО «НАУКОВО-ОСВІТНІЙ ІННОВА-ЦІЙНИЙ ЦЕНТР СУСПІЛЬНИХ ТРАНСФОРМАЦІЙ» за підсумками Конкурсу індивідуальнихгрантів «Ініціатива підтримки науковців»

Simulation and Experimental Study of Ultrasonic Vibratory Grinding of Internal Splines

As an important component of mechanical transmission systems, internal splines are widely used in aerospace, industrial equipment, and other fields. However, internal splines are prone to deformation and shrinkage after heat treatment. At present, most internal splines with a pitch circle diameter greater than φ60 mm can be processed and shaped by ordinary corundum grinding wheels, but there is no effective processing method for the shaping of small- and medium-sized internal splines. This paper establishes a single abrasive material removal model; uses Abaqus to simulate three-body free grinding; and analyzes the effects of abrasive rotation angle, rotation speed, and grinding depth on material removal under different conditions. By comparing the tooth lead deviation and tooth direction deviation before and after internal spline grinding, the experimental results show that after ultrasonic vibration grinding, the internal spline tooth profile deviation is reduced by 41.9%, and the tooth direction deviation is reduced by 44.1%, which provides a new processing method for the deformation recovery of internal splines after heat treatment.

Microscopic characteristics and properties of co-based coating by pulse current-assisted laser cladding on high carbon steel

Laser cladding has the advantages of a short processing cycle and good processing quality, and has been extensively researched in the field of rail property enhancement. The current study focuses mostly on enhancing the mechanical properties of rails by altering the laser process parameters and using complicated materials. However, the enhancement of mechanical properties such as corrosion and friction resistance by laser cladding is limited. As an effective metallurgical processing method, pulse current can be used to control the microstructure and improve the mechanical properties by altering the solidification process of the metal. In this paper, the effects of pulse current parameters (pulse current frequency, current peak, and duty cycle) on microscopic characteristics, such as microstructure number of pores, and microhardness, as well as mechanical properties, such as wear and corrosion resistance, of stellite-6 coatings created by pulse current-assisted laser cladding (PC-ALC), on the U71Mn substrate were investigated. The results showed that the electromagnetic action of the current refined the grains and reduced the number of pores in the PC-ALC coating. The degree of grain fineness increased, then decreased, as the current intensity increased. When compared with the microhardness without pulse current, the average hardness of the HAZ and coating rose by 49.7 % and 167 %, respectively. The results showed a positive link between corrosion resistance and current frequency, but a negative correlation between corrosion resistance and current peak. The corrosion resistance was improved by increasing the current duty cycle from 25 % to 50 %. The wear resistance of coatings was positively related to microhardness, and the coating reached the wear stabilization stage with a friction loss of about 0.5 %. The tests indicated that PC-ALC coatings had good corrosion and friction resistance for rail repair, extending their service life.

EFFECTS OF PROCESSING ON THE PROXIMATE AND MINERAL COMPOSITION OF CULTURED AND CAPTURED CLARIAS GARIEPPINUS

Fish and fish products serve as vital protein sources in Africa and the world in general. Since fish is typically processed before consumption, this study investigated how different processing methods affect the proximate and mineral composition of both cultured and captured Clarias gariepinus. The processing techniques employed in this research included smoking, boiling, and oven drying. Cultured catfish were sourced from Oris Farm in Osogbo, Osun State, Nigeria, while captured catfish were obtained from Asejire Dam in Oyo State. The proximate and mineral compositions of fresh, boiled, smoked, and oven-dried samples of both cultured and captured Clarias gariepinus were analyzed in triplicate. The results indicated significant differences (P<0.05) in crude protein, dry matter, moisture, ash, fiber, potassium, and phosphorus content among the different processing methods for both cultured and captured catfish samples. Potassium and phosphorus levels also varied significantly (P<0.05) across the processing methods. Boiled and oven-dried samples of both cultured and captured catfish exhibited higher crude protein and lipid content, suggesting that these processing methods provide consumers with a rich source of protein and essential nutrients. The study concludes that boiling and oven drying with samples possessing highest percentages of protein are the most effective processing methods for preparing Clarias gariepinus, ensuring optimal nutrient retention for consumers.

Evaluation of ultrasound-assisted tomato sour soup marination on beef: Insights into physicochemical, sensory, microstructural, and flavour characteristics

This study evaluated the quality attributes of tomato sour soup marinade and investigated the effects of ultrasound-assisted marination on the physicochemical properties, microstructure, texture, sensory quality, and flavour profile of beef. The results showed that tomato sour soup significantly increased the marinade absorption rate and improved beef tenderloin’s physicochemical properties, texture, and flavour attributes compared to static brine (P < 0.05), with organic acids playing an essential role in the marinade tenderisation process. Compared to static sour soup marination, ultrasound treatment significantly accelerated the marination process, reducing beef’s shear force, hardness, and chewiness while increasing its tenderness. Microstructural observations revealed that sour soup marination induced a fragmented and irregular muscle fibre structure. Furthermore, sour soup marination significantly increased the relative concentrations of volatile flavour compounds, including alkanes, organic sulphides, alcohols, aldehydes, and aromatic compounds. Appropriate ultrasound treatment positively affects the texture and flavour characteristics of beef marinated with tomato sour soup, and the optimal approach was 320 W ultrasound treatment for 60 min. Overall, tomato sour soup improved beef’s textural and flavour attributes, while ultrasound-assisted marination is an effective processing method to improve the quality of meat products.

Human Vital Signs Signal Monitoring and Repairment with an Optical Fiber Sensor Based on Deep Learning

Optical fiber sensors have been widely applied for their advantages such as small size, lightweight, and strong electronic interference robustness. Compared with current electronic sensors, optical fiber sensors perform better in measuring parameters in harsh environments, which makes them suitable for more and more applications, such as target tracing and detection and monitoring of health signs in medical services. However, due to fiber optic sensor failure, improper transmission and storage, or other reasons, missing data occur from time to time. Therefore, effective missing value processing methods are desirable as they can be used to facilitate data processing or analysis. In the present study, gated recurrent unit (GRU) interpolation is performed by using the generative adversarial network (GAN) model to process the irregular delay relationship between the data before and after the collection of incomplete vital signs data. Furthermore, a data interpolation model based on VS-E2E-GAN is proposed to reconstruct vital signs signals. The ROC curve (AUC), metrics including mean squared error (MSE), and accuracy (ACC) of experiments reach 0.901, 0.777, and 0.908, respectively, which indicates that the proposed VS-E2E-GAN model performs well in terms of vital signs data imputation and repairment, has strong robustness when compared with other works, and has potential clinical application in health monitoring, smart home, and so on.

In-package cold atmospheric plasma processing for shelf-life extension of gilthead seabream (Sparus aurata) fillets.

In-package cold atmospheric plasma (CAP) processing, which refers to the generation of CAP inside a sealed package, enables a local disinfecting reaction, allowing no post-process contamination and extending the shelf-life (SL) of perishable food products, such as fresh fish. In the present study, four in-package CAP treatments, differing in frequency and processing time, were applied on fresh gilthead seabream (Sparus aurata) fillets, prepacked in low-permeability pouches. Fish SL was evaluated during isothermal storage at 2°C, whereas untreated packaged fillets were used as control samples. The SL assessment of the fish fillets was based on microbial enumeration of total aerobic mesophilic count (TMC), total aerobic psychrotrophic count (TPC), Pseudomonas spp., Enterobacteriaceae, and lactic acid bacteria (LAB), pH measurement, determination of color and texture parameters, lipid oxidation, total volatile basic nitrogen (TVB-N) measurement, and sensory evaluation. All CAP treatments were effective against microbial inhibition in fish fillets, especially regarding TMC, TPC, and Pseudomonas spp., resulting in maximum reduction of 1.49, 1.24, and 1.43logCFU/g, respectively, compared to the control samples after 16 days of storage. However, minor effect was observed against Enterobacteriaceae and no effect against LAB. CAP processing did not affect the color and texture parameters of fish fillets, and TVB-N production was slightly reduced in CAP-treated samples; however, lipid oxidation was accelerated, especially at the more intense processing conditions, by a maximum of 75.5%. The results of the study indicated that in-package CAP processing could be effectively applied for inhibiting spoilage during refrigerated storage and extending SL of fresh fish fillets. PRACTICAL APPLICATION: In-package cold atmospheric plasma (CAP) processing was tested on gilthead seabream fillets, a highly perishable product with high commercial potential if its shelf-life can be extended through minimal processing. The food industry could benefit from in-package CAP technology as it is a cost effective nonthermal processing method while preventing post-processing contamination of the products. Although in-package CAP processing has not been extensively tested on fish, this study examined the quality and shelf-life of a highly perishable fish species, and the results could be further used as a reference for processing optimization of the CAP treatments.

Smart insect-computer hybrid robots empowered with enhanced obstacle avoidance capabilities using onboard monocular camera

Insect-computer hybrid robots are receiving increasing attention as a potential alternative to small artificial robots due to their superior locomotion capabilities and low manufacturing costs. Controlling insect-computer hybrid robots to travel through terrain littered with complex obstacles of various shapes and sizes is still challenging. While insects can inherently deal with certain obstacles by using their antennae to detect and avoid obstacles, this ability is limited and can be interfered with by control signals when performing navigation tasks, ultimately leading to the robot being trapped in a specific place and having difficulty escaping. Hybrid robots need to add additional sensors to provide accurate perception and early warning of the external environment to avoid obstacles before getting trapped, ensuring smooth navigation tasks in rough terrain. However, due to insects’ tiny size and limited load capacity, hybrid robots are very limited in the sensors they can carry. A monocular camera is suitable for insect-computer hybrid robots because of its small size, low power consumption, and robust information acquisition capabilities. This paper proposes a navigation algorithm with an integrated obstacle avoidance module using a monocular camera for the insect-computer hybrid robot. The monocular cameras equipped with a monocular depth estimation algorithm based on deep learning can produce depth maps of environmental obstacles. The navigation algorithm generates control commands that can drive the hybrid robot away from obstacles according to the distribution of obstacle distances in the depth map. To ensure the performance of the monocular depth estimation model when applied to insect-computer hybrid robotics scenarios, we collected the first dataset from the viewpoint of a small robot for model training. In addition, we propose a simple but effective depth map processing method to obtain obstacle avoidance commands based on the weighted sum method. The success rate of the navigation experiment is significantly improved from 6.7% to 73.3%. Experimental results show that our navigation algorithm can detect obstacles in advance and guide the hybrid robots to avoid them before they get trapped.

Regression Analysis Data for E-Sport Athletes Prediction using OSEMN Framework

In the fast-growing E-Sports industry, athlete performance is the key to achieving success and winning. Therefore, analyzing the factors that contribute to the performance of E-Sports athletes is essential in order to optimize their performance in competition. This study aims to analyze the relationship between age, number of training hours, and experience playing in competition with rank, kill death ratio (KDA), and the number of wins of E-Sports athletes using the OSEMN approach (Obtain, Scrub, Explore, Model, Interpret, and Communicate). The data was obtained from 300 professional or non-professional E- Sports athletes, over the past three years who were involved in various competitions. Independent variables included age, number of training hours, and experience playing in competitions, while the dependent variables included rank, KDA, and number of wins. Data was collected, processed and explored and then analyzed using multiple linear regression methods. This study succeeded in applying the regression analysis method using the OSEMN framework, identifying relevant variables, and developing effective data collection and processing methods. This model has the potential to provide accurate predictions of E- Sport athlete performance data. However, it is still important to consider other factors such as business context, comparison with other models, and cross- validation to confirm the reliability of the prediction results.

Feature selection algorithm based on optimized genetic algorithm and the application in high-dimensional data processing.

High-dimensional data is widely used in many fields, but selecting key features from it is challenging. Feature selection can reduce data dimensionality and weaken noise interference, thereby improving model efficiency and enhancing model interpretability. In order to improve the efficiency and accuracy of high-dimensional data processing, a feature selection method based on optimized genetic algorithm is proposed in this study. The algorithm simulates the process of natural selection, searches for possible subsets of feature, and finds the subsets of feature that optimizes the performance of the model. The results show that when the value of K is less than 4 or more than 8, the recognition rate is very low. After adaptive bias filtering, 724 features are filtered to 372, and the accuracy is improved from 0.9352 to 0.9815. From 714 features to 406 Gaussian codes, the accuracy is improved from 0.9625 to 0.9754. Among all tests, the colon has the highest average accuracy, followed by small round blue cell tumor(SRBCT), lymphoma, central nervous system(CNS) and ovaries. The green curve is the best, with stable performance and a time range of 0-300. While maintaining the efficiency, it can reach 4.48 as soon as possible. The feature selection method has practical significance for high-dimensional data processing, improves the efficiency and accuracy of data processing, and provides an effective new method for high-dimensional data processing.

APPLICATION OF ARTIFICIAL INTELLIGENCE FOR SOLUTION OF ENGINEERING PROBLEMS. ADVANTAGES AND CHALLENGES

The intensive development of information technology leads to an increase in data volumes, which requires the application of effective methods for data processing and analysis for managing organizations and strategic planning. Data mining methods (DM) are widely used in various fields, including education, where they can help improve the quality of learning and the efficiency of teachers. This work demonstrates the use of the Orange platform, a framework for data visualization and analytics, which allows integrating visual programming with Python to solve complex analytical tasks. The application of Data Mining methods and the use of Orange allow for a deep analysis of educational data, contributing to the development of strategies to improve the efficiency of the learning process.

Automatic measurement of slug flow processes from in‐line videos

AbstractSlug flow processes draw much attention in chemical and pharmaceutical manufacturing thanks to their ability to eliminate downtime costs and batch‐to‐batch variation. Using in‐line video can monitor the current process status and improve the stability of the slug flow. However, there are no effective image processing methods aiming at such videos from chemical experiments due to limited training sample size and the variety of experimental settings. The paper proposes a training‐free method to automatically detect and measure the slugs from in‐line videos. Multiple image features are fused to identify the slug shapes under various lighting conditions, a six‐point model is fitted to achieve better volume estimation, and a consistency score is estimated to quantify the detection uncertainty. The proposed algorithm achieves similar results to manual labeling for various types of slug flow processes, improving the accuracy of slug column estimation by a large margin compared to the existing automatic methods. Finally, using it to monitor the slug flow process's stability and the change of the slug volume in an injection point are demonstrated. The method not only shows that handcrafted image features have the capability for detection and segmentation from chemical experiment images but also paves the road for a training‐based algorithm for the task.

Steaming Maintains Fatty Acids, Antioxidants, and Proximate Content in Snack Bar Products from Cocoa Beans.

Chocolate products on the market are generally in the form of chocolate bars as snacks made from cocoa powder. Fat and powder are separated first through a pressing process to obtain the cocoa powder. Cocoa powder loses most of its fat content during processing. Therefore, the study aimed to determine the effect of steaming time on the cocoa bean content of fatty acids, free fatty acids, proximate levels, and antioxidant activity of snack bar products made from steamed cocoa beans. Seven steaming time intervals for cocoa beans were studied. The results showed that a longer steaming time affects the fatty acids, saturated fatty acids, antioxidants, and proximate in cocoa beans. Steaming time treatment at 45 minutes increased oleic acid, palmitic acid, and antioxidant activity. In addition, reducing free fatty acids represents a quality improvement that meets international Codex Alimentarius standards, offering a competitive advantage in the market. The food industry can adopt this steaming technique to develop snack bars and new products that are healthier and more sustainable by using steaming as an effective processing method in maintaining and increasing the nutritional value of products.

Web-Based Outsourcing Workforce Information System

&#x0D; Trimegah Cipta Mandiri is a company that operates in the service sector, namely outsourcing or what is known as outsourcing and this company, so far, for marketing still uses telephone, print media and comes directly to the prospective employment company so it cannot find out information about PT. Trimegah Cipta Mandiri is complete and up to date and takes quite a long time and costs quite a lot. Apart from that, when work relations require new potential workers, they experience difficulties, because they do not know directly the data about potential new workers. The purpose of writing this thesis is to design a web-based ordering system at PT. Trimegah Cipta Mandiri, so that it can act as a liaison between PT. Trimegah Cipta Mandiri and work relations without being limited by the location of a region and access times, as well as ease of obtaining information about prospective workers. new work required by work relations. The research methods used included field studies and literature studies. Field studies include interviews and direct observations. Meanwhile, literature research is carried out by looking for materials that are relevant to the problem. Design methods include system analysis in the form of Flow of Documents, system design in the form of Context Diagrams, DFD, ERD, Normalization. The results of the design are applied in the form of web design using the PHP programming language and database management using MySQL. After the system analysis and design has been carried out, it can be seen that PT. Trimegah Cipta Mandiri requires a more effective and efficient method of processing and data collection, so that it is hoped that it will be able to produce information quickly, precisely and accurately which is useful for improving services to work relations.&#x0D; &#x0D;

Parametric Modeling for Curved Slots of Vivaldi Antenna Based on Artificial Neural Network

This paper proposes an artificial neural network (ANN) model based on parametric modeling for curved slots of the Vivaldi antenna. A more effective processing method is achieved by feeding ANN with the point positions that produce curved edges via cubic spline interpolation rather than the picture of metallic patches. The predicted results of ANN, including S-parameter and gain, agree well with those from the full-wave simulation. With the trained model, a Vivaldi antenna with the lower cut-off frequency is optimized by the multi-objective genetic algorithm.

Impact of argon dielectric barrier discharge cold plasma on the physicochemical and cooking properties of lightly-milled rice

This study investigated the influence of argon dielectric barrier discharge-cold plasma (DBD-CP) applied at different voltage settings (1 kV and 2 kV) and exposure durations (5 min and 10 min) on the physicochemical properties, cooking behavior, textural attributes, and flavor characteristics of lightly-milled-rice (LMR). The DBD-CP treatment increased the brightness of untreated LMR from 62.93 to 76.69, and reduced the water droplet contact angle from 66.66° to 58.99° following a treatment of 2 kV-10 min. Moreover, the treatment enhanced the crystallinity and gelatinization enthalpy of LMR starch by 8.93% and 60.55%, respectively. Conversely, it led to a decrease in the amylose content by 7.66% when compared to the control group. Metabolomic analysis revealed a decrease in the content of key compounds involved in metabolic pathways, including amino acids, α-hydroxy acids, and GABA. The cooking experiment demonstrated a substantial reduction by 5.4 min in optimal cooking time, along with water absorption capacity and volume expansion rate increased by 54.19% and 68.42% in 2 kV-10 min DBD-treated LMR, respectively. Notably, DBD-CP treatment positively influenced the production of important aroma compounds such as alcohols, aldehydes, and ketones. Overall, DBD-CP proved to be an effective physical processing method for significantly enhancing the edible quality and aroma of LMR.

УСУНЕННЯ ЗАВАД У ДИНАМІЧНИХ СПЕКТРАХ ЗА НАЯВНОСТІ ПОТУЖНОГО СИГНАЛУ. Частина 2. ВУЗЬКОСМУГОВІ ЗАВАДИ УМОВНО-СТАЦІОНАРНОГО ХАРАКТЕРУ

Subject and Purpose. A new method for detecting narrow band interferences is discussed, with the use of an example record of Jovi- an Io-C decametric radio storm as obtained with the UTR-2 array on April 10, 2020. We aimed at developing an efficient and simple algorithm based on a detailed analysis of the effect the radio interference environment and the frequency response of the telescope may have on the efficiency of the interference mitigation procedure. Methods and Methodology. The ‘orthogonal detection’ method proposed for identifying linear interference patterns in dynamic spectra of powerful radio sources has been adapted for application to narrow band interferences of various spectral widths and variable brightness, which often happen to be located close to (or intersect with) the signal of interest. In order to minimize the impact of the telescope’s frequency response, a discretized frequency-scanning technique is used, which permits a steady approximation of the averaged spectrum by low order polynomials, as well as removal of the frequency trend which hinders distinguishing between the low-level interference signals and those coming from the source. Results. An efficient approach to the problem of detecting long lasting, narrowband interferences in dynamical spectra (con- ditionally stationary interferences) is proposed. The algorithm has been tested on the example of a powerful storm of Jovian radio emission. The proposed technique can be especially useful in situations where the signal of interest overlaps with the interference in the time-frequency domain. Conclusions. The new approach to the problem of interference mitigation, based on the development of combined algorithms of signal separation in the time-frequency domain, has been shown to offer an effective method of signal processing. In contrast to the previously used methods based on calculation of statistical moments for amplitudes, the new approach allows avoiding use of the same statistical indicators for both interference detection and subsequent steps of data analysis intended for building physical models and interpreting the observational data.